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13
.gitignore
vendored
13
.gitignore
vendored
@@ -40,3 +40,16 @@ scrape/
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*.webm
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*.mp4
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*.mkv
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# Third-party textbook sources (not redistributable)
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*.pdf
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*.epub
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epub_extract/
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# Textbook extraction artifacts — regenerate locally via run_pipeline.sh.
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# Scripts are committed; their generated outputs are not.
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Conjuga/Scripts/textbook/*.json
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Conjuga/Scripts/textbook/review.html
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# Note: the app-bundle copies (Conjuga/Conjuga/textbook_{data,vocab}.json)
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# ARE committed so `xcodebuild` works on a fresh clone without first running
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# the pipeline. They're regenerated from the scripts when content changes.
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5
CLAUDE.md
Normal file
5
CLAUDE.md
Normal file
@@ -0,0 +1,5 @@
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# Project rules
|
||||
|
||||
## Git
|
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- **Never run `git commit` or `git push` without an explicit request from the user in the current turn.** File edits are fine; committing and pushing are not. Wait to be told.
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||||
@@ -9,8 +9,10 @@
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||||
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353C5DE41FD410FA82E3AED7 /* Models */,
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1994867BC8E985795A172854 /* Services */,
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@@ -276,6 +318,7 @@
|
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||||
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||||
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||||
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||||
@@ -315,8 +358,8 @@
|
||||
3BC3247457109FC6BF00D85B /* TenseInfo.swift */,
|
||||
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||||
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|
||||
17E5252282F44ECD9BA70DB8 /* GrammarExercise.swift */,
|
||||
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|
||||
17E5252282F44ECD9BA70DB8 /* GrammarExercise.swift */,
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||||
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||||
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||||
02B2179562E54E148C98219D /* ListeningView.swift */,
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
E292A183ABB24FFE9CB719C8 /* StoryQuizView.swift */,
|
||||
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||||
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||||
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||||
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||||
895E547BEFB5D0FBF676BE33 /* Lyrics */ = {
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||||
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||||
@@ -419,6 +443,16 @@
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||||
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||||
sourceTree = "<group>";
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||||
};
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||||
8A1DED0596E04DDE9536A9A9 /* Stories */ = {
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||||
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||||
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||||
950347251CC94D4A9DFF7CBC /* StoryLibraryView.swift */,
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||||
2A8B6081226847E0A0A174BC /* StoryReaderView.swift */,
|
||||
E292A183ABB24FFE9CB719C8 /* StoryQuizView.swift */,
|
||||
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||||
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||||
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||||
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||||
A591A3B6F1F13D23D68D7A9D = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
@@ -426,6 +460,8 @@
|
||||
4B183AB0C56BC2EC302531E7 /* ConjugaWidget */,
|
||||
F7D740BB7D1E23949D4C1AE5 /* Packages */,
|
||||
F605D24E5EA11065FD18AF7E /* Products */,
|
||||
B442229C0A26C1D531472C7D /* Frameworks */,
|
||||
C77B065CF67D1F5128E10CC7 /* ConjugaUITests */,
|
||||
);
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
@@ -445,6 +481,14 @@
|
||||
path = Views;
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
B442229C0A26C1D531472C7D /* Frameworks */ = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
E772BA9C3FF67FEA9A034B4B /* iOS */,
|
||||
);
|
||||
name = Frameworks;
|
||||
sourceTree = "<group>";
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||||
};
|
||||
BA34B77A38B698101DBBE241 /* Dashboard */ = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
@@ -460,9 +504,13 @@
|
||||
143D06606AE10DCA30A140C2 /* CourseQuizView.swift */,
|
||||
833516C5D57F164C8660A479 /* CourseView.swift */,
|
||||
631DC0A942DD57C81DECE083 /* DeckStudyView.swift */,
|
||||
7A1B2C3D4E5F60718293AA11 /* TextbookChapterListView.swift */,
|
||||
7A1B2C3D4E5F60718293AA12 /* TextbookChapterView.swift */,
|
||||
7A1B2C3D4E5F60718293AA13 /* TextbookExerciseView.swift */,
|
||||
2931634BEB33B93429CE254F /* VocabFlashcardView.swift */,
|
||||
5E7EF4161C73AAC67B3A0004 /* WeekTestView.swift */,
|
||||
EA1F177F7ABF5D2E4E5466CD /* CheckpointExamView.swift */,
|
||||
CF3A181BF2399D34C23DA933 /* StemChangeConjugationView.swift */,
|
||||
);
|
||||
path = Course;
|
||||
sourceTree = "<group>";
|
||||
@@ -474,11 +522,41 @@
|
||||
path = Utilities;
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
C77B065CF67D1F5128E10CC7 /* ConjugaUITests */ = {
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CEEA84E15880A9D56DE18F33 /* TextbookFlowUITests.swift */,
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||||
8A630C74D28CE1B280C9F296 /* AllChaptersScreenshotTests.swift */,
|
||||
8F08E1DC6932D9EA1D380913 /* StemChangeToggleTests.swift */,
|
||||
6584E0FDA939E3B82EECA4B5 /* VocabGridTests.swift */,
|
||||
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||||
name = ConjugaUITests;
|
||||
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|
||||
sourceTree = "<group>";
|
||||
};
|
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DFD75E32A53845A693D98F48 /* Chat */ = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
5667AA04211A449A9150BD28 /* ChatLibraryView.swift */,
|
||||
FA5FE6E149F54A6BA7D01D99 /* ChatView.swift */,
|
||||
);
|
||||
path = Chat;
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
E772BA9C3FF67FEA9A034B4B /* iOS */ = {
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||||
isa = PBXGroup;
|
||||
children = (
|
||||
A6153A5C7241C1AB0373AA17 /* Foundation.framework */,
|
||||
);
|
||||
name = iOS;
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
F605D24E5EA11065FD18AF7E /* Products */ = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
16C1F74196C3C5628953BE3F /* Conjuga.app */,
|
||||
9708FF3CF33E4765DB225F93 /* ConjugaWidgetExtension.appex */,
|
||||
27B2A75AAF79A9402AAF3F57 /* ConjugaUITests.xctest */,
|
||||
);
|
||||
name = Products;
|
||||
sourceTree = "<group>";
|
||||
@@ -516,6 +594,24 @@
|
||||
productReference = 16C1F74196C3C5628953BE3F /* Conjuga.app */;
|
||||
productType = "com.apple.product-type.application";
|
||||
};
|
||||
C6CC399BFD5A2574CB9956B4 /* ConjugaUITests */ = {
|
||||
isa = PBXNativeTarget;
|
||||
buildConfigurationList = F454EA7279A44C5E151F71BA /* Build configuration list for PBXNativeTarget "ConjugaUITests" */;
|
||||
buildPhases = (
|
||||
66589E8F78971725CA2066ED /* Sources */,
|
||||
C5C1BB325D49EE6ED3AC3D5F /* Frameworks */,
|
||||
425DC31DA6EF2C4C7A873DAA /* Resources */,
|
||||
);
|
||||
buildRules = (
|
||||
);
|
||||
dependencies = (
|
||||
04C7E3C8079DE56024C2154E /* PBXTargetDependency */,
|
||||
);
|
||||
name = ConjugaUITests;
|
||||
productName = ConjugaUITests;
|
||||
productReference = 27B2A75AAF79A9402AAF3F57 /* ConjugaUITests.xctest */;
|
||||
productType = "com.apple.product-type.bundle.ui-testing";
|
||||
};
|
||||
F73909B4044081DB8F6272AF /* ConjugaWidgetExtension */ = {
|
||||
isa = PBXNativeTarget;
|
||||
buildConfigurationList = EA7E12CF28EB750C2B8BB2F1 /* Build configuration list for PBXNativeTarget "ConjugaWidgetExtension" */;
|
||||
@@ -568,16 +664,25 @@
|
||||
548B46ED3C40F5F28A5ADCC6 /* XCLocalSwiftPackageReference "SharedModels" */,
|
||||
);
|
||||
preferredProjectObjectVersion = 77;
|
||||
productRefGroup = F605D24E5EA11065FD18AF7E /* Products */;
|
||||
projectDirPath = "";
|
||||
projectRoot = "";
|
||||
targets = (
|
||||
96127FACA68AE541F5C0F8BC /* Conjuga */,
|
||||
F73909B4044081DB8F6272AF /* ConjugaWidgetExtension */,
|
||||
C6CC399BFD5A2574CB9956B4 /* ConjugaUITests */,
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||||
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|
||||
};
|
||||
/* End PBXProject section */
|
||||
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||||
/* Begin PBXResourcesBuildPhase section */
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||||
425DC31DA6EF2C4C7A873DAA /* Resources */ = {
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||||
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buildActionMask = 2147483647;
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files = (
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||||
runOnlyForDeploymentPostprocessing = 0;
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};
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||||
B74A8384221C70A670B902D8 /* Resources */ = {
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||||
isa = PBXResourcesBuildPhase;
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||||
buildActionMask = 2147483647;
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||||
@@ -585,6 +690,8 @@
|
||||
F59655A8B8FCE6264315DD33 /* Assets.xcassets in Resources */,
|
||||
CF9E48ADF0501FB79F3DDB7B /* conjuga_data.json in Resources */,
|
||||
2B5B2D63DC9C290F66890A4A /* course_data.json in Resources */,
|
||||
7A1B2C3D4E5F60718293A4B5 /* textbook_data.json in Resources */,
|
||||
7A1B2C3D4E5F60718293A4B6 /* textbook_vocab.json in Resources */,
|
||||
);
|
||||
runOnlyForDeploymentPostprocessing = 0;
|
||||
};
|
||||
@@ -602,6 +709,10 @@
|
||||
C3851F960C1162239DC2F935 /* CourseQuizView.swift in Sources */,
|
||||
8C43F09F52EA9B537EA27E43 /* CourseReviewStore.swift in Sources */,
|
||||
F0D0778207F144D6AC3D39C3 /* CourseView.swift in Sources */,
|
||||
7A1B2C3D4E5F60718293AA01 /* TextbookChapterListView.swift in Sources */,
|
||||
7A1B2C3D4E5F60718293AA02 /* TextbookChapterView.swift in Sources */,
|
||||
7A1B2C3D4E5F60718293AA03 /* TextbookExerciseView.swift in Sources */,
|
||||
7A1B2C3D4E5F60718293AA04 /* AnswerChecker.swift in Sources */,
|
||||
1C2636790E70B6BC7FFCC904 /* DailyLog.swift in Sources */,
|
||||
BB48230C3B26EA6E84D2D823 /* DailyProgressRing.swift in Sources */,
|
||||
35A0F6E7124D989312721F7D /* DashboardView.swift in Sources */,
|
||||
@@ -653,7 +764,7 @@
|
||||
6BB4B0A655E6CB6F82D81B5A /* WeekTestView.swift in Sources */,
|
||||
968D626462B0ADEC8D7D56AA /* CheckpointExamView.swift in Sources */,
|
||||
E99473B7DF9BCAE150E9D1E1 /* WidgetDataService.swift in Sources */,
|
||||
DDF58F3899FC4B92BF6587D2 /* StudyTimerService.swift in Sources */,
|
||||
DDF58F3899FC4B92BF6587D2 /* StudyTimerService.swift in Sources */,
|
||||
8C1E4E7F36D64EFF8D092AC8 /* StoryGenerator.swift in Sources */,
|
||||
4C2649215B81470195F38ED0 /* StoryLibraryView.swift in Sources */,
|
||||
8E3D8E8254CF4213B9D9FAD3 /* StoryReaderView.swift in Sources */,
|
||||
@@ -669,7 +780,8 @@
|
||||
C8AF0931F7FD458C80B6EC0D /* ChatLibraryView.swift in Sources */,
|
||||
6CCC8D51F5524688A4BC5AF8 /* ChatView.swift in Sources */,
|
||||
8510085D78E248D885181E80 /* FeatureReferenceView.swift in Sources */,
|
||||
);
|
||||
943728CD3E65FE6CCADB05EE /* StemChangeConjugationView.swift in Sources */,
|
||||
);
|
||||
runOnlyForDeploymentPostprocessing = 0;
|
||||
};
|
||||
217A29BCEDD9D44B6DD85AF6 /* Sources */ = {
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);
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||||
runOnlyForDeploymentPostprocessing = 0;
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||||
};
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||||
66589E8F78971725CA2066ED /* Sources */ = {
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||||
buildActionMask = 2147483647;
|
||||
files = (
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||||
96A3E5FA8EC63123D97365E1 /* TextbookFlowUITests.swift in Sources */,
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||||
F7E459C46F25A8A45D7E0DFB /* AllChaptersScreenshotTests.swift in Sources */,
|
||||
1B0B3B2C771AD72E25B3493C /* StemChangeToggleTests.swift in Sources */,
|
||||
BC662C36AC503E00A977CEC1 /* VocabGridTests.swift in Sources */,
|
||||
);
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||||
runOnlyForDeploymentPostprocessing = 0;
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||||
};
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||||
/* End PBXSourcesBuildPhase section */
|
||||
|
||||
/* Begin PBXTargetDependency section */
|
||||
04C7E3C8079DE56024C2154E /* PBXTargetDependency */ = {
|
||||
isa = PBXTargetDependency;
|
||||
name = Conjuga;
|
||||
target = 96127FACA68AE541F5C0F8BC /* Conjuga */;
|
||||
targetProxy = 6E1F966015DA38BD4E3CE8AF /* PBXContainerItemProxy */;
|
||||
};
|
||||
0B370CF10B68E386093E5BB2 /* PBXTargetDependency */ = {
|
||||
isa = PBXTargetDependency;
|
||||
target = F73909B4044081DB8F6272AF /* ConjugaWidgetExtension */;
|
||||
@@ -837,6 +966,24 @@
|
||||
};
|
||||
name = Release;
|
||||
};
|
||||
A923186E44A25A8086B27A34 /* Release */ = {
|
||||
isa = XCBuildConfiguration;
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||||
buildSettings = {
|
||||
CLANG_ENABLE_OBJC_WEAK = NO;
|
||||
CODE_SIGN_IDENTITY = "iPhone Developer";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
DEVELOPMENT_TEAM = V3PF3M6B6U;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
IPHONEOS_DEPLOYMENT_TARGET = 17.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = com.conjuga.app.uitests;
|
||||
SDKROOT = iphoneos;
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2";
|
||||
TEST_TARGET_NAME = Conjuga;
|
||||
VALIDATE_PRODUCT = YES;
|
||||
};
|
||||
name = Release;
|
||||
};
|
||||
B9223DC55BB69E9AB81B59AE /* Debug */ = {
|
||||
isa = XCBuildConfiguration;
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||||
buildSettings = {
|
||||
@@ -902,6 +1049,23 @@
|
||||
};
|
||||
name = Debug;
|
||||
};
|
||||
DB8C0F513F77A50F2EF2D561 /* Debug */ = {
|
||||
isa = XCBuildConfiguration;
|
||||
buildSettings = {
|
||||
CLANG_ENABLE_OBJC_WEAK = NO;
|
||||
CODE_SIGN_IDENTITY = "iPhone Developer";
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
DEVELOPMENT_TEAM = V3PF3M6B6U;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
IPHONEOS_DEPLOYMENT_TARGET = 17.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = com.conjuga.app.uitests;
|
||||
SDKROOT = iphoneos;
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2";
|
||||
TEST_TARGET_NAME = Conjuga;
|
||||
};
|
||||
name = Debug;
|
||||
};
|
||||
/* End XCBuildConfiguration section */
|
||||
|
||||
/* Begin XCConfigurationList section */
|
||||
@@ -932,6 +1096,15 @@
|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Debug;
|
||||
};
|
||||
F454EA7279A44C5E151F71BA /* Build configuration list for PBXNativeTarget "ConjugaUITests" */ = {
|
||||
isa = XCConfigurationList;
|
||||
buildConfigurations = (
|
||||
A923186E44A25A8086B27A34 /* Release */,
|
||||
DB8C0F513F77A50F2EF2D561 /* Debug */,
|
||||
);
|
||||
defaultConfigurationIsVisible = 0;
|
||||
defaultConfigurationName = Release;
|
||||
};
|
||||
/* End XCConfigurationList section */
|
||||
|
||||
/* Begin XCLocalSwiftPackageReference section */
|
||||
|
||||
@@ -53,6 +53,16 @@
|
||||
</BuildableReference>
|
||||
</MacroExpansion>
|
||||
<Testables>
|
||||
<TestableReference
|
||||
skipped = "NO">
|
||||
<BuildableReference
|
||||
BuildableIdentifier = "primary"
|
||||
BlueprintIdentifier = "C77B065CF67D1F5128E10CC7"
|
||||
BuildableName = "ConjugaUITests.xctest"
|
||||
BlueprintName = "ConjugaUITests"
|
||||
ReferencedContainer = "container:Conjuga.xcodeproj">
|
||||
</BuildableReference>
|
||||
</TestableReference>
|
||||
</Testables>
|
||||
<CommandLineArguments>
|
||||
</CommandLineArguments>
|
||||
|
||||
@@ -69,12 +69,14 @@ struct ConjugaApp: App {
|
||||
schema: Schema([
|
||||
ReviewCard.self, CourseReviewCard.self, UserProgress.self,
|
||||
TestResult.self, DailyLog.self, SavedSong.self, Story.self, Conversation.self,
|
||||
TextbookExerciseAttempt.self,
|
||||
]),
|
||||
cloudKitDatabase: .private("iCloud.com.conjuga.app")
|
||||
)
|
||||
cloudContainer = try ModelContainer(
|
||||
for: ReviewCard.self, CourseReviewCard.self, UserProgress.self,
|
||||
TestResult.self, DailyLog.self, SavedSong.self, Story.self, Conversation.self,
|
||||
TextbookExerciseAttempt.self,
|
||||
configurations: cloudConfig
|
||||
)
|
||||
|
||||
@@ -209,6 +211,7 @@ struct ConjugaApp: App {
|
||||
schema: Schema([
|
||||
Verb.self, VerbForm.self, IrregularSpan.self,
|
||||
TenseGuide.self, CourseDeck.self, VocabCard.self,
|
||||
TextbookChapter.self,
|
||||
]),
|
||||
url: url,
|
||||
cloudKitDatabase: .none
|
||||
@@ -216,6 +219,7 @@ struct ConjugaApp: App {
|
||||
return try ModelContainer(
|
||||
for: Verb.self, VerbForm.self, IrregularSpan.self,
|
||||
TenseGuide.self, CourseDeck.self, VocabCard.self,
|
||||
TextbookChapter.self,
|
||||
configurations: localConfig
|
||||
)
|
||||
}
|
||||
|
||||
10
Conjuga/Conjuga/Services/AnswerChecker.swift
Normal file
10
Conjuga/Conjuga/Services/AnswerChecker.swift
Normal file
@@ -0,0 +1,10 @@
|
||||
import Foundation
|
||||
import SharedModels
|
||||
|
||||
/// Thin app-side wrapper around the SharedModels `AnswerGrader`. All logic
|
||||
/// lives in SharedModels so it can be unit tested.
|
||||
enum AnswerChecker {
|
||||
static func grade(userText: String, canonical: String, alternates: [String] = []) -> TextbookGrade {
|
||||
AnswerGrader.grade(userText: userText, canonical: canonical, alternates: alternates)
|
||||
}
|
||||
}
|
||||
@@ -6,6 +6,9 @@ actor DataLoader {
|
||||
static let courseDataVersion = 7
|
||||
static let courseDataKey = "courseDataVersion"
|
||||
|
||||
static let textbookDataVersion = 12
|
||||
static let textbookDataKey = "textbookDataVersion"
|
||||
|
||||
/// Quick check: does the DB need seeding or course data refresh?
|
||||
static func needsSeeding(container: ModelContainer) async -> Bool {
|
||||
let context = ModelContext(container)
|
||||
@@ -15,6 +18,9 @@ actor DataLoader {
|
||||
let storedVersion = UserDefaults.standard.integer(forKey: courseDataKey)
|
||||
if storedVersion < courseDataVersion { return true }
|
||||
|
||||
let textbookVersion = UserDefaults.standard.integer(forKey: textbookDataKey)
|
||||
if textbookVersion < textbookDataVersion { return true }
|
||||
|
||||
return false
|
||||
}
|
||||
|
||||
@@ -133,6 +139,61 @@ actor DataLoader {
|
||||
|
||||
// Seed course data (uses the same mainContext so @Query sees it)
|
||||
seedCourseData(context: context)
|
||||
|
||||
// Seed textbook data — only bump the version key if the seed
|
||||
// actually inserted rows, so a missing/unparseable bundle doesn't
|
||||
// permanently lock us out of future re-seeds.
|
||||
if seedTextbookData(context: context) {
|
||||
UserDefaults.standard.set(textbookDataVersion, forKey: textbookDataKey)
|
||||
}
|
||||
}
|
||||
|
||||
/// Re-seed textbook data if the version has changed OR if the rows are
|
||||
/// missing on disk. The row-count check exists because anything opening
|
||||
/// this store with a subset schema (e.g. an out-of-date widget extension)
|
||||
/// can destructively drop the rows without touching UserDefaults — so a
|
||||
/// pure version-flag trigger would leave us permanently empty.
|
||||
static func refreshTextbookDataIfNeeded(container: ModelContainer) async {
|
||||
let shared = UserDefaults.standard
|
||||
let context = ModelContext(container)
|
||||
let existingCount = (try? context.fetchCount(FetchDescriptor<TextbookChapter>())) ?? 0
|
||||
let versionCurrent = shared.integer(forKey: textbookDataKey) >= textbookDataVersion
|
||||
|
||||
if versionCurrent && existingCount > 0 { return }
|
||||
|
||||
if versionCurrent {
|
||||
print("Textbook data version current but store has \(existingCount) chapters — re-seeding...")
|
||||
} else {
|
||||
print("Textbook data version outdated — re-seeding...")
|
||||
}
|
||||
|
||||
// Fetch + delete individually instead of batch delete. SwiftData's
|
||||
// context.delete(model:) hits the store directly and doesn't always
|
||||
// clear the unique-constraint index before the reseed's save runs,
|
||||
// so re-inserting rows with the same .unique id can throw.
|
||||
let textbookCourseName = "Complete Spanish Step-by-Step"
|
||||
if let existing = try? context.fetch(FetchDescriptor<TextbookChapter>()) {
|
||||
for chapter in existing { context.delete(chapter) }
|
||||
}
|
||||
let deckDescriptor = FetchDescriptor<CourseDeck>(
|
||||
predicate: #Predicate<CourseDeck> { $0.courseName == textbookCourseName }
|
||||
)
|
||||
if let decks = try? context.fetch(deckDescriptor) {
|
||||
for deck in decks { context.delete(deck) }
|
||||
}
|
||||
do {
|
||||
try context.save()
|
||||
} catch {
|
||||
print("[DataLoader] ERROR: textbook wipe save failed: \(error)")
|
||||
return
|
||||
}
|
||||
|
||||
if seedTextbookData(context: context) {
|
||||
shared.set(textbookDataVersion, forKey: textbookDataKey)
|
||||
print("Textbook data re-seeded to version \(textbookDataVersion)")
|
||||
} else {
|
||||
print("Textbook re-seed failed — leaving version key untouched so next launch retries")
|
||||
}
|
||||
}
|
||||
|
||||
/// Re-seed course data if the version has changed (e.g. examples were added).
|
||||
@@ -170,6 +231,10 @@ actor DataLoader {
|
||||
// Re-seed course data
|
||||
seedCourseData(context: context)
|
||||
|
||||
// Textbook's vocab decks/cards share the same CourseDeck/VocabCard
|
||||
// entities, so they were just wiped above. Reseed them.
|
||||
seedTextbookVocabDecks(context: context, courseName: "Complete Spanish Step-by-Step")
|
||||
|
||||
shared.set(courseDataVersion, forKey: courseDataKey)
|
||||
print("Course data re-seeded to version \(courseDataVersion)")
|
||||
}
|
||||
@@ -336,4 +401,169 @@ actor DataLoader {
|
||||
context.insert(reviewCard)
|
||||
return reviewCard
|
||||
}
|
||||
|
||||
// MARK: - Textbook seeding
|
||||
|
||||
@discardableResult
|
||||
private static func seedTextbookData(context: ModelContext) -> Bool {
|
||||
let url = Bundle.main.url(forResource: "textbook_data", withExtension: "json")
|
||||
?? Bundle.main.bundleURL.appendingPathComponent("textbook_data.json")
|
||||
guard let data = try? Data(contentsOf: url) else {
|
||||
print("[DataLoader] textbook_data.json not bundled — skipping textbook seed")
|
||||
return false
|
||||
}
|
||||
guard let json = try? JSONSerialization.jsonObject(with: data) as? [String: Any] else {
|
||||
print("[DataLoader] ERROR: Could not parse textbook_data.json")
|
||||
return false
|
||||
}
|
||||
let courseName = (json["courseName"] as? String) ?? "Textbook"
|
||||
guard let chapters = json["chapters"] as? [[String: Any]] else {
|
||||
print("[DataLoader] ERROR: textbook_data.json missing chapters")
|
||||
return false
|
||||
}
|
||||
|
||||
var inserted = 0
|
||||
for ch in chapters {
|
||||
guard let id = ch["id"] as? String,
|
||||
let number = ch["number"] as? Int,
|
||||
let title = ch["title"] as? String,
|
||||
let blocksRaw = ch["blocks"] as? [[String: Any]] else { continue }
|
||||
|
||||
let part = (ch["part"] as? Int) ?? 0
|
||||
|
||||
// Normalize each block to canonical keys expected by TextbookBlock decoder.
|
||||
var normalized: [[String: Any]] = []
|
||||
var exerciseCount = 0
|
||||
var vocabTableCount = 0
|
||||
for (i, b) in blocksRaw.enumerated() {
|
||||
var out: [String: Any] = [:]
|
||||
out["index"] = i
|
||||
let kind = (b["kind"] as? String) ?? ""
|
||||
out["kind"] = kind
|
||||
switch kind {
|
||||
case "heading":
|
||||
if let level = b["level"] { out["level"] = level }
|
||||
if let text = b["text"] { out["text"] = text }
|
||||
case "paragraph":
|
||||
if let text = b["text"] { out["text"] = text }
|
||||
case "key_vocab_header":
|
||||
break
|
||||
case "vocab_table":
|
||||
vocabTableCount += 1
|
||||
if let src = b["sourceImage"] { out["sourceImage"] = src }
|
||||
if let lines = b["ocrLines"] { out["ocrLines"] = lines }
|
||||
if let conf = b["ocrConfidence"] { out["ocrConfidence"] = conf }
|
||||
// Paired Spanish→English cards from the bounding-box extractor.
|
||||
if let cards = b["cards"] as? [[String: Any]], !cards.isEmpty {
|
||||
let normalized: [[String: Any]] = cards.compactMap { c in
|
||||
guard let front = c["front"] as? String,
|
||||
let back = c["back"] as? String else { return nil }
|
||||
return ["front": front, "back": back]
|
||||
}
|
||||
if !normalized.isEmpty {
|
||||
out["cards"] = normalized
|
||||
}
|
||||
}
|
||||
case "exercise":
|
||||
exerciseCount += 1
|
||||
if let exId = b["id"] { out["exerciseId"] = exId }
|
||||
if let inst = b["instruction"] { out["instruction"] = inst }
|
||||
if let extra = b["extra"] { out["extra"] = extra }
|
||||
if let prompts = b["prompts"] { out["prompts"] = prompts }
|
||||
if let items = b["answerItems"] { out["answerItems"] = items }
|
||||
if let freeform = b["freeform"] { out["freeform"] = freeform }
|
||||
default:
|
||||
break
|
||||
}
|
||||
normalized.append(out)
|
||||
}
|
||||
|
||||
let bodyJSON: Data
|
||||
do {
|
||||
bodyJSON = try JSONSerialization.data(withJSONObject: normalized, options: [])
|
||||
} catch {
|
||||
print("[DataLoader] failed to encode chapter \(number) blocks: \(error)")
|
||||
continue
|
||||
}
|
||||
|
||||
let chapter = TextbookChapter(
|
||||
id: id,
|
||||
number: number,
|
||||
title: title,
|
||||
part: part,
|
||||
courseName: courseName,
|
||||
bodyJSON: bodyJSON,
|
||||
exerciseCount: exerciseCount,
|
||||
vocabTableCount: vocabTableCount
|
||||
)
|
||||
context.insert(chapter)
|
||||
inserted += 1
|
||||
}
|
||||
|
||||
do {
|
||||
try context.save()
|
||||
} catch {
|
||||
print("[DataLoader] ERROR: textbook chapter save failed: \(error)")
|
||||
return false
|
||||
}
|
||||
|
||||
// Verify rows actually hit the store — guards against the case where
|
||||
// save returned cleanly but no rows were persisted.
|
||||
let persisted = (try? context.fetchCount(FetchDescriptor<TextbookChapter>())) ?? 0
|
||||
guard persisted > 0 else {
|
||||
print("[DataLoader] ERROR: textbook seeded \(inserted) chapters but persisted count is 0")
|
||||
return false
|
||||
}
|
||||
|
||||
// Seed textbook-derived vocabulary flashcards as CourseDecks so the
|
||||
// existing Course UI can surface them alongside LanGo decks.
|
||||
seedTextbookVocabDecks(context: context, courseName: courseName)
|
||||
|
||||
print("Textbook seeding complete: \(inserted) chapters inserted, \(persisted) persisted")
|
||||
return true
|
||||
}
|
||||
|
||||
private static func seedTextbookVocabDecks(context: ModelContext, courseName: String) {
|
||||
let url = Bundle.main.url(forResource: "textbook_vocab", withExtension: "json")
|
||||
?? Bundle.main.bundleURL.appendingPathComponent("textbook_vocab.json")
|
||||
guard let data = try? Data(contentsOf: url),
|
||||
let json = try? JSONSerialization.jsonObject(with: data) as? [String: Any],
|
||||
let chaptersArr = json["chapters"] as? [[String: Any]]
|
||||
else { return }
|
||||
|
||||
let courseSlug = courseName.lowercased()
|
||||
.replacingOccurrences(of: " ", with: "-")
|
||||
|
||||
var deckCount = 0
|
||||
var cardCount = 0
|
||||
for chData in chaptersArr {
|
||||
guard let chNum = chData["chapter"] as? Int,
|
||||
let cards = chData["cards"] as? [[String: Any]],
|
||||
!cards.isEmpty else { continue }
|
||||
|
||||
let deckId = "textbook_\(courseSlug)_ch\(chNum)"
|
||||
let title = "Chapter \(chNum) vocabulary"
|
||||
let deck = CourseDeck(
|
||||
id: deckId,
|
||||
weekNumber: chNum,
|
||||
title: title,
|
||||
cardCount: cards.count,
|
||||
courseName: courseName,
|
||||
isReversed: false
|
||||
)
|
||||
context.insert(deck)
|
||||
deckCount += 1
|
||||
|
||||
for c in cards {
|
||||
guard let front = c["front"] as? String,
|
||||
let back = c["back"] as? String else { continue }
|
||||
let card = VocabCard(front: front, back: back, deckId: deckId)
|
||||
card.deck = deck
|
||||
context.insert(card)
|
||||
cardCount += 1
|
||||
}
|
||||
}
|
||||
try? context.save()
|
||||
print("Textbook vocab seeding complete: \(deckCount) decks, \(cardCount) cards")
|
||||
}
|
||||
}
|
||||
|
||||
@@ -89,6 +89,11 @@ struct PracticeSessionService {
|
||||
|
||||
let forms = referenceStore.fetchForms(verbId: verb.id, tenseId: tenseId)
|
||||
if forms.isEmpty { continue }
|
||||
|
||||
// Full Table practice is for regular patterns only — skip combos
|
||||
// where any form in this (verb, tense) is irregular.
|
||||
if forms.contains(where: { $0.regularity != "regular" }) { continue }
|
||||
|
||||
return FullTablePrompt(verb: verb, tenseInfo: tenseInfo, forms: forms)
|
||||
}
|
||||
|
||||
|
||||
@@ -65,28 +65,26 @@ final class PronunciationService {
|
||||
|
||||
do {
|
||||
let audioSession = AVAudioSession.sharedInstance()
|
||||
try audioSession.setCategory(.playAndRecord, mode: .measurement, options: [.duckOthers, .defaultToSpeaker])
|
||||
try audioSession.setCategory(.record, mode: .measurement, options: [.duckOthers])
|
||||
try audioSession.setActive(true, options: .notifyOthersOnDeactivation)
|
||||
|
||||
audioEngine = AVAudioEngine()
|
||||
// Use SFSpeechAudioBufferRecognitionRequest with the recognizer
|
||||
// directly — avoid AVAudioEngine entirely since it produces
|
||||
// zero-length buffers on some devices causing assertion crashes.
|
||||
request = SFSpeechAudioBufferRecognitionRequest()
|
||||
|
||||
guard let audioEngine, let request else { return }
|
||||
guard let request else { return }
|
||||
request.shouldReportPartialResults = true
|
||||
request.requiresOnDeviceRecognition = recognizer.supportsOnDeviceRecognition
|
||||
|
||||
// Use AVAudioEngine with the native input format
|
||||
audioEngine = AVAudioEngine()
|
||||
guard let audioEngine else { return }
|
||||
|
||||
let inputNode = audioEngine.inputNode
|
||||
let recordingFormat = inputNode.outputFormat(forBus: 0)
|
||||
|
||||
// Validate format — 0 channels crashes installTap
|
||||
guard recordingFormat.channelCount > 0 else {
|
||||
print("[PronunciationService] invalid recording format (0 channels)")
|
||||
self.audioEngine = nil
|
||||
self.request = nil
|
||||
return
|
||||
}
|
||||
|
||||
inputNode.installTap(onBus: 0, bufferSize: 1024, format: recordingFormat) { buffer, _ in
|
||||
guard buffer.frameLength > 0 else { return }
|
||||
// Use nil format — lets the system pick a compatible format
|
||||
// and avoids the mDataByteSize(0) assertion from format mismatches
|
||||
inputNode.installTap(onBus: 0, bufferSize: 4096, format: nil) { buffer, _ in
|
||||
request.append(buffer)
|
||||
}
|
||||
|
||||
|
||||
@@ -9,6 +9,7 @@ enum StartupCoordinator {
|
||||
static func bootstrap(localContainer: ModelContainer) async {
|
||||
await DataLoader.seedIfNeeded(container: localContainer)
|
||||
await DataLoader.refreshCourseDataIfNeeded(container: localContainer)
|
||||
await DataLoader.refreshTextbookDataIfNeeded(container: localContainer)
|
||||
}
|
||||
|
||||
/// Recurring maintenance: legacy migrations, identity repair, cloud dedup.
|
||||
|
||||
@@ -26,12 +26,14 @@ enum StoreInspector {
|
||||
let hasZVERBFORM = tables.contains("ZVERBFORM")
|
||||
let hasZTENSEGUIDE = tables.contains("ZTENSEGUIDE")
|
||||
let hasZVOCABCARD = tables.contains("ZVOCABCARD")
|
||||
let hasZTEXTBOOKCHAPTER = tables.contains("ZTEXTBOOKCHAPTER")
|
||||
|
||||
var summary = "[StoreInspector:\(label)] \(tables.count) tables"
|
||||
summary += " | ZVERB=\(hasZVERB ? queryInt(db: db, sql: "SELECT COUNT(*) FROM ZVERB") : -1)"
|
||||
summary += " ZVERBFORM=\(hasZVERBFORM ? queryInt(db: db, sql: "SELECT COUNT(*) FROM ZVERBFORM") : -1)"
|
||||
summary += " ZTENSEGUIDE=\(hasZTENSEGUIDE ? queryInt(db: db, sql: "SELECT COUNT(*) FROM ZTENSEGUIDE") : -1)"
|
||||
summary += " ZVOCABCARD=\(hasZVOCABCARD ? queryInt(db: db, sql: "SELECT COUNT(*) FROM ZVOCABCARD") : -1)"
|
||||
summary += " ZTEXTBOOKCHAPTER=\(hasZTEXTBOOKCHAPTER ? queryInt(db: db, sql: "SELECT COUNT(*) FROM ZTEXTBOOKCHAPTER") : -1)"
|
||||
print(summary)
|
||||
|
||||
// Log all Z-tables (SwiftData entity tables start with Z, minus Core Data system tables)
|
||||
|
||||
@@ -5,9 +5,14 @@ import SwiftData
|
||||
struct CourseView: View {
|
||||
@Environment(\.cloudModelContextProvider) private var cloudModelContextProvider
|
||||
@Query(sort: \CourseDeck.weekNumber) private var decks: [CourseDeck]
|
||||
@Query(sort: \TextbookChapter.number) private var textbookChapters: [TextbookChapter]
|
||||
@AppStorage("selectedCourse") private var selectedCourse: String?
|
||||
@State private var testResults: [TestResult] = []
|
||||
|
||||
private var textbookCourses: [String] {
|
||||
Array(Set(textbookChapters.map(\.courseName))).sorted()
|
||||
}
|
||||
|
||||
private var cloudModelContext: ModelContext { cloudModelContextProvider() }
|
||||
|
||||
private var courseNames: [String] {
|
||||
@@ -62,6 +67,32 @@ struct CourseView: View {
|
||||
description: Text("Course data is loading...")
|
||||
)
|
||||
} else {
|
||||
// Textbook entry (shown above course picker when available)
|
||||
if !textbookCourses.isEmpty {
|
||||
Section {
|
||||
ForEach(textbookCourses, id: \.self) { name in
|
||||
NavigationLink(value: TextbookDestination(courseName: name)) {
|
||||
HStack(spacing: 12) {
|
||||
Image(systemName: "book.fill")
|
||||
.font(.title3)
|
||||
.foregroundStyle(.indigo)
|
||||
.frame(width: 32)
|
||||
VStack(alignment: .leading, spacing: 2) {
|
||||
Text(name)
|
||||
.font(.subheadline.weight(.semibold))
|
||||
Text("Read chapters, do exercises")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
Spacer()
|
||||
}
|
||||
}
|
||||
}
|
||||
} header: {
|
||||
Text("Textbook")
|
||||
}
|
||||
}
|
||||
|
||||
// Course picker
|
||||
if courseNames.count > 1 {
|
||||
Section {
|
||||
@@ -155,6 +186,24 @@ struct CourseView: View {
|
||||
.navigationDestination(for: CheckpointDestination.self) { dest in
|
||||
CheckpointExamView(courseName: dest.courseName, throughWeek: dest.throughWeek)
|
||||
}
|
||||
.navigationDestination(for: TextbookDestination.self) { dest in
|
||||
TextbookChapterListView(courseName: dest.courseName)
|
||||
}
|
||||
.navigationDestination(for: TextbookChapter.self) { chapter in
|
||||
TextbookChapterView(chapter: chapter)
|
||||
}
|
||||
.navigationDestination(for: TextbookExerciseDestination.self) { dest in
|
||||
textbookExerciseView(for: dest)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ViewBuilder
|
||||
private func textbookExerciseView(for dest: TextbookExerciseDestination) -> some View {
|
||||
if let chapter = textbookChapters.first(where: { $0.id == dest.chapterId }) {
|
||||
TextbookExerciseView(chapter: chapter, blockIndex: dest.blockIndex)
|
||||
} else {
|
||||
ContentUnavailableView("Exercise unavailable", systemImage: "questionmark.circle")
|
||||
}
|
||||
}
|
||||
|
||||
@@ -175,6 +224,10 @@ struct CheckpointDestination: Hashable {
|
||||
let throughWeek: Int
|
||||
}
|
||||
|
||||
struct TextbookDestination: Hashable {
|
||||
let courseName: String
|
||||
}
|
||||
|
||||
// MARK: - Deck Row
|
||||
|
||||
private struct DeckRowView: View {
|
||||
|
||||
@@ -8,6 +8,11 @@ struct DeckStudyView: View {
|
||||
@State private var isStudying = false
|
||||
@State private var speechService = SpeechService()
|
||||
@State private var deckCards: [VocabCard] = []
|
||||
@State private var expandedConjugations: Set<String> = []
|
||||
|
||||
private var isStemChangingDeck: Bool {
|
||||
deck.title.localizedCaseInsensitiveContains("stem changing")
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
cardListView
|
||||
@@ -19,7 +24,8 @@ struct DeckStudyView: View {
|
||||
VocabFlashcardView(
|
||||
cards: deckCards.shuffled(),
|
||||
speechService: speechService,
|
||||
onDone: { isStudying = false }
|
||||
onDone: { isStudying = false },
|
||||
deckTitle: deck.title
|
||||
)
|
||||
.toolbar {
|
||||
ToolbarItem(placement: .cancellationAction) {
|
||||
@@ -30,6 +36,24 @@ struct DeckStudyView: View {
|
||||
}
|
||||
}
|
||||
|
||||
/// Reversed stem-change decks have `front` as English, so prefer the
|
||||
/// Spanish side when the card is stored that way. Strip parenthetical
|
||||
/// notes and the reflexive `-se` ending for verb-table lookup.
|
||||
private func inferInfinitive(card: VocabCard) -> String {
|
||||
let raw: String
|
||||
if deck.isReversed {
|
||||
raw = card.back
|
||||
} else {
|
||||
raw = card.front
|
||||
}
|
||||
var t = raw.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
if let paren = t.firstIndex(of: "(") {
|
||||
t = String(t[..<paren]).trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
}
|
||||
if t.hasSuffix("se") && t.count > 4 { t = String(t.dropLast(2)) }
|
||||
return t
|
||||
}
|
||||
|
||||
private func loadCards() {
|
||||
let deckId = deck.id
|
||||
let descriptor = FetchDescriptor<VocabCard>(
|
||||
@@ -107,6 +131,36 @@ struct DeckStudyView: View {
|
||||
.multilineTextAlignment(.trailing)
|
||||
}
|
||||
|
||||
// Stem-change conjugation toggle
|
||||
if isStemChangingDeck {
|
||||
let verb = inferInfinitive(card: card)
|
||||
let isOpen = expandedConjugations.contains(verb)
|
||||
Button {
|
||||
withAnimation(.smooth) {
|
||||
if isOpen {
|
||||
expandedConjugations.remove(verb)
|
||||
} else {
|
||||
expandedConjugations.insert(verb)
|
||||
}
|
||||
}
|
||||
} label: {
|
||||
Label(
|
||||
isOpen ? "Hide conjugation" : "Show conjugation",
|
||||
systemImage: isOpen ? "chevron.up" : "chevron.down"
|
||||
)
|
||||
.font(.caption.weight(.medium))
|
||||
}
|
||||
.buttonStyle(.borderless)
|
||||
.tint(.blue)
|
||||
.padding(.leading, 42)
|
||||
|
||||
if isOpen {
|
||||
StemChangeConjugationView(infinitive: verb)
|
||||
.padding(.leading, 42)
|
||||
.transition(.opacity.combined(with: .move(edge: .top)))
|
||||
}
|
||||
}
|
||||
|
||||
// Example sentences
|
||||
if !card.examplesES.isEmpty {
|
||||
VStack(alignment: .leading, spacing: 6) {
|
||||
|
||||
97
Conjuga/Conjuga/Views/Course/StemChangeConjugationView.swift
Normal file
97
Conjuga/Conjuga/Views/Course/StemChangeConjugationView.swift
Normal file
@@ -0,0 +1,97 @@
|
||||
import SwiftUI
|
||||
import SharedModels
|
||||
import SwiftData
|
||||
|
||||
/// Shows the present-tense conjugation of a verb (identified by infinitive),
|
||||
/// with any irregular/stem-change spans highlighted. Designed to drop into
|
||||
/// stem-changing verb flashcards so learners can see the conjugation in-place.
|
||||
struct StemChangeConjugationView: View {
|
||||
let infinitive: String
|
||||
|
||||
@Environment(\.modelContext) private var modelContext
|
||||
@State private var rows: [ConjugationRow] = []
|
||||
|
||||
private static let personLabels = ["yo", "tú", "él/ella/Ud.", "nosotros", "vosotros", "ellos/ellas/Uds."]
|
||||
private static let tenseId = "ind_presente"
|
||||
|
||||
var body: some View {
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
HStack {
|
||||
Text("Present tense")
|
||||
.font(.subheadline.weight(.semibold))
|
||||
.foregroundStyle(.secondary)
|
||||
Spacer()
|
||||
}
|
||||
if rows.isEmpty {
|
||||
Text("Conjugation not available")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
.padding(.vertical, 4)
|
||||
} else {
|
||||
VStack(spacing: 6) {
|
||||
ForEach(rows) { row in
|
||||
HStack(alignment: .firstTextBaseline) {
|
||||
Text(row.person)
|
||||
.font(.callout)
|
||||
.foregroundStyle(.secondary)
|
||||
.frame(width: 130, alignment: .leading)
|
||||
IrregularHighlightText(
|
||||
form: row.form,
|
||||
spans: row.spans,
|
||||
font: .callout.monospaced(),
|
||||
showLabels: false
|
||||
)
|
||||
Spacer()
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
.padding(12)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.background(Color.blue.opacity(0.08), in: RoundedRectangle(cornerRadius: 10))
|
||||
.onAppear(perform: loadForms)
|
||||
}
|
||||
|
||||
private func loadForms() {
|
||||
// Find the verb by infinitive (lowercase exact match).
|
||||
let normalized = infinitive.lowercased().trimmingCharacters(in: .whitespaces)
|
||||
let verbDescriptor = FetchDescriptor<Verb>(
|
||||
predicate: #Predicate<Verb> { $0.infinitive == normalized }
|
||||
)
|
||||
guard let verb = (try? modelContext.fetch(verbDescriptor))?.first else {
|
||||
rows = []
|
||||
return
|
||||
}
|
||||
|
||||
let verbId = verb.id
|
||||
let tenseId = Self.tenseId
|
||||
let formDescriptor = FetchDescriptor<VerbForm>(
|
||||
predicate: #Predicate<VerbForm> { $0.verbId == verbId && $0.tenseId == tenseId },
|
||||
sortBy: [SortDescriptor(\VerbForm.personIndex)]
|
||||
)
|
||||
let forms = (try? modelContext.fetch(formDescriptor)) ?? []
|
||||
|
||||
rows = forms.map { f in
|
||||
ConjugationRow(
|
||||
id: f.personIndex,
|
||||
person: Self.personLabels[safe: f.personIndex] ?? "",
|
||||
form: f.form,
|
||||
spans: f.spans ?? []
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private struct ConjugationRow: Identifiable {
|
||||
let id: Int
|
||||
let person: String
|
||||
let form: String
|
||||
let spans: [IrregularSpan]
|
||||
}
|
||||
|
||||
private extension Array {
|
||||
subscript(safe index: Int) -> Element? {
|
||||
indices.contains(index) ? self[index] : nil
|
||||
}
|
||||
}
|
||||
121
Conjuga/Conjuga/Views/Course/TextbookChapterListView.swift
Normal file
121
Conjuga/Conjuga/Views/Course/TextbookChapterListView.swift
Normal file
@@ -0,0 +1,121 @@
|
||||
import SwiftUI
|
||||
import SharedModels
|
||||
import SwiftData
|
||||
|
||||
struct TextbookChapterListView: View {
|
||||
let courseName: String
|
||||
|
||||
@Environment(\.cloudModelContextProvider) private var cloudModelContextProvider
|
||||
@Query(sort: \TextbookChapter.number) private var allChapters: [TextbookChapter]
|
||||
|
||||
private var cloudModelContext: ModelContext { cloudModelContextProvider() }
|
||||
@State private var attempts: [TextbookExerciseAttempt] = []
|
||||
|
||||
private var chapters: [TextbookChapter] {
|
||||
allChapters.filter { $0.courseName == courseName }
|
||||
}
|
||||
|
||||
private var byPart: [(part: Int, chapters: [TextbookChapter])] {
|
||||
let grouped = Dictionary(grouping: chapters, by: \.part)
|
||||
return grouped.keys.sorted().map { p in
|
||||
(p, grouped[p]!.sorted { $0.number < $1.number })
|
||||
}
|
||||
}
|
||||
|
||||
private func progressFor(_ chapter: TextbookChapter) -> (correct: Int, total: Int) {
|
||||
let chNum = chapter.number
|
||||
let chAttempts = attempts.filter {
|
||||
$0.courseName == courseName && $0.chapterNumber == chNum
|
||||
}
|
||||
let total = chAttempts.reduce(0) { $0 + $1.totalCount }
|
||||
let correct = chAttempts.reduce(0) { $0 + $1.correctCount + $1.closeCount }
|
||||
return (correct, total)
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
List {
|
||||
if chapters.isEmpty {
|
||||
ContentUnavailableView(
|
||||
"Textbook loading",
|
||||
systemImage: "book.closed",
|
||||
description: Text("Textbook content is being prepared…")
|
||||
)
|
||||
} else {
|
||||
ForEach(byPart, id: \.part) { part, partChapters in
|
||||
Section {
|
||||
ForEach(partChapters, id: \.id) { chapter in
|
||||
NavigationLink(value: chapter) {
|
||||
chapterRow(chapter)
|
||||
}
|
||||
.accessibilityIdentifier("textbook-chapter-row-\(chapter.number)")
|
||||
}
|
||||
} header: {
|
||||
if part > 0 {
|
||||
Text("Part \(part)")
|
||||
} else {
|
||||
Text("Chapters")
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
.navigationTitle("Textbook")
|
||||
.onAppear(perform: loadAttempts)
|
||||
}
|
||||
|
||||
@ViewBuilder
|
||||
private func chapterRow(_ chapter: TextbookChapter) -> some View {
|
||||
let p = progressFor(chapter)
|
||||
HStack(alignment: .center, spacing: 12) {
|
||||
ZStack {
|
||||
Circle()
|
||||
.stroke(Color.secondary.opacity(0.2), lineWidth: 3)
|
||||
.frame(width: 36, height: 36)
|
||||
if p.total > 0 {
|
||||
Circle()
|
||||
.trim(from: 0, to: CGFloat(p.correct) / CGFloat(p.total))
|
||||
.stroke(.orange, style: StrokeStyle(lineWidth: 3, lineCap: .round))
|
||||
.frame(width: 36, height: 36)
|
||||
.rotationEffect(.degrees(-90))
|
||||
}
|
||||
Text("\(chapter.number)")
|
||||
.font(.footnote.weight(.bold))
|
||||
}
|
||||
|
||||
VStack(alignment: .leading, spacing: 2) {
|
||||
Text(chapter.title)
|
||||
.font(.headline)
|
||||
HStack(spacing: 10) {
|
||||
if chapter.exerciseCount > 0 {
|
||||
Label("\(chapter.exerciseCount)", systemImage: "pencil.and.list.clipboard")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
if chapter.vocabTableCount > 0 {
|
||||
Label("\(chapter.vocabTableCount)", systemImage: "list.bullet.rectangle")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
if p.total > 0 {
|
||||
Text("\(p.correct)/\(p.total)")
|
||||
.font(.caption.monospacedDigit())
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
}
|
||||
}
|
||||
Spacer()
|
||||
}
|
||||
.padding(.vertical, 4)
|
||||
}
|
||||
|
||||
private func loadAttempts() {
|
||||
attempts = (try? cloudModelContext.fetch(FetchDescriptor<TextbookExerciseAttempt>())) ?? []
|
||||
}
|
||||
}
|
||||
|
||||
#Preview {
|
||||
NavigationStack {
|
||||
TextbookChapterListView(courseName: "Complete Spanish Step-by-Step")
|
||||
}
|
||||
.modelContainer(for: [TextbookChapter.self], inMemory: true)
|
||||
}
|
||||
209
Conjuga/Conjuga/Views/Course/TextbookChapterView.swift
Normal file
209
Conjuga/Conjuga/Views/Course/TextbookChapterView.swift
Normal file
@@ -0,0 +1,209 @@
|
||||
import SwiftUI
|
||||
import SharedModels
|
||||
import SwiftData
|
||||
|
||||
struct TextbookChapterView: View {
|
||||
let chapter: TextbookChapter
|
||||
|
||||
@State private var expandedVocab: Set<Int> = []
|
||||
|
||||
private var blocks: [TextbookBlock] { chapter.blocks() }
|
||||
|
||||
var body: some View {
|
||||
ScrollView {
|
||||
VStack(alignment: .leading, spacing: 12) {
|
||||
headerView
|
||||
Divider()
|
||||
ForEach(blocks) { block in
|
||||
blockView(block)
|
||||
}
|
||||
}
|
||||
.padding(.horizontal)
|
||||
.padding(.vertical, 12)
|
||||
}
|
||||
.navigationTitle(chapter.title)
|
||||
.navigationBarTitleDisplayMode(.inline)
|
||||
}
|
||||
|
||||
private var headerView: some View {
|
||||
VStack(alignment: .leading, spacing: 4) {
|
||||
if chapter.part > 0 {
|
||||
Text("Part \(chapter.part)")
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
Text("Chapter \(chapter.number)")
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(.secondary)
|
||||
Text(chapter.title)
|
||||
.font(.largeTitle.bold())
|
||||
}
|
||||
}
|
||||
|
||||
@ViewBuilder
|
||||
private func blockView(_ block: TextbookBlock) -> some View {
|
||||
switch block.kind {
|
||||
case .heading:
|
||||
headingView(block)
|
||||
case .paragraph:
|
||||
paragraphView(block)
|
||||
case .keyVocabHeader:
|
||||
HStack(spacing: 6) {
|
||||
Image(systemName: "star.fill").foregroundStyle(.orange)
|
||||
Text("Key Vocabulary")
|
||||
.font(.headline)
|
||||
.foregroundStyle(.orange)
|
||||
}
|
||||
.padding(.top, 8)
|
||||
case .vocabTable:
|
||||
vocabTableView(block)
|
||||
case .exercise:
|
||||
exerciseLinkView(block)
|
||||
}
|
||||
}
|
||||
|
||||
private func headingView(_ block: TextbookBlock) -> some View {
|
||||
let level = block.level ?? 3
|
||||
let font: Font
|
||||
switch level {
|
||||
case 2: font = .title.bold()
|
||||
case 3: font = .title2.bold()
|
||||
case 4: font = .title3.weight(.semibold)
|
||||
default: font = .headline
|
||||
}
|
||||
return Text(stripInlineEmphasis(block.text ?? ""))
|
||||
.font(font)
|
||||
.padding(.top, 10)
|
||||
}
|
||||
|
||||
private func paragraphView(_ block: TextbookBlock) -> some View {
|
||||
Text(attributedFromMarkdownish(block.text ?? ""))
|
||||
.font(.body)
|
||||
.fixedSize(horizontal: false, vertical: true)
|
||||
}
|
||||
|
||||
private func vocabTableView(_ block: TextbookBlock) -> some View {
|
||||
let expanded = expandedVocab.contains(block.index)
|
||||
let cards = block.cards ?? []
|
||||
let lines = block.ocrLines ?? []
|
||||
let itemCount = cards.isEmpty ? lines.count : cards.count
|
||||
return VStack(alignment: .leading, spacing: 4) {
|
||||
Button {
|
||||
if expanded { expandedVocab.remove(block.index) } else { expandedVocab.insert(block.index) }
|
||||
} label: {
|
||||
HStack {
|
||||
Image(systemName: expanded ? "chevron.down" : "chevron.right")
|
||||
.font(.caption)
|
||||
Text("Vocabulary (\(itemCount) items)")
|
||||
.font(.subheadline.weight(.medium))
|
||||
.foregroundStyle(.primary)
|
||||
Spacer()
|
||||
}
|
||||
.contentShape(Rectangle())
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
|
||||
if expanded {
|
||||
if cards.isEmpty {
|
||||
// Fallback: no paired cards available — show raw OCR lines.
|
||||
VStack(alignment: .leading, spacing: 2) {
|
||||
ForEach(Array(lines.enumerated()), id: \.offset) { _, line in
|
||||
Text(line)
|
||||
.font(.callout.monospaced())
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
}
|
||||
.padding(.leading, 14)
|
||||
} else {
|
||||
vocabGrid(cards: cards)
|
||||
.padding(.leading, 14)
|
||||
}
|
||||
}
|
||||
}
|
||||
.padding(10)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.background(Color.orange.opacity(0.08), in: RoundedRectangle(cornerRadius: 10))
|
||||
}
|
||||
|
||||
@ViewBuilder
|
||||
private func vocabGrid(cards: [TextbookVocabPair]) -> some View {
|
||||
Grid(alignment: .leading, horizontalSpacing: 16, verticalSpacing: 6) {
|
||||
ForEach(Array(cards.enumerated()), id: \.offset) { _, card in
|
||||
GridRow {
|
||||
Text(card.front)
|
||||
.font(.callout)
|
||||
.foregroundStyle(.primary)
|
||||
Text(card.back)
|
||||
.font(.callout)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func exerciseLinkView(_ block: TextbookBlock) -> some View {
|
||||
NavigationLink(value: TextbookExerciseDestination(
|
||||
chapterId: chapter.id,
|
||||
chapterNumber: chapter.number,
|
||||
blockIndex: block.index
|
||||
)) {
|
||||
HStack(spacing: 10) {
|
||||
Image(systemName: "pencil.and.list.clipboard")
|
||||
.foregroundStyle(.orange)
|
||||
.font(.title3)
|
||||
VStack(alignment: .leading, spacing: 2) {
|
||||
Text("Exercise \(block.exerciseId ?? "")")
|
||||
.font(.headline)
|
||||
if let inst = block.instruction, !inst.isEmpty {
|
||||
Text(stripInlineEmphasis(inst))
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
.lineLimit(2)
|
||||
}
|
||||
}
|
||||
Spacer()
|
||||
Image(systemName: "chevron.right")
|
||||
.foregroundStyle(.secondary)
|
||||
.font(.caption)
|
||||
}
|
||||
.padding(12)
|
||||
.background(Color.orange.opacity(0.1), in: RoundedRectangle(cornerRadius: 10))
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
|
||||
// Strip our ad-hoc ** / * markers from parsed text
|
||||
private func stripInlineEmphasis(_ s: String) -> String {
|
||||
s.replacingOccurrences(of: "**", with: "")
|
||||
.replacingOccurrences(of: "*", with: "")
|
||||
}
|
||||
|
||||
private func attributedFromMarkdownish(_ s: String) -> AttributedString {
|
||||
// Parser emits `**bold**` and `*italic*`. Try to render via AttributedString markdown.
|
||||
if let parsed = try? AttributedString(markdown: s, options: .init(allowsExtendedAttributes: true)) {
|
||||
return parsed
|
||||
}
|
||||
return AttributedString(stripInlineEmphasis(s))
|
||||
}
|
||||
}
|
||||
|
||||
struct TextbookExerciseDestination: Hashable {
|
||||
let chapterId: String
|
||||
let chapterNumber: Int
|
||||
let blockIndex: Int
|
||||
}
|
||||
|
||||
#Preview {
|
||||
NavigationStack {
|
||||
TextbookChapterView(chapter: TextbookChapter(
|
||||
id: "ch1",
|
||||
number: 1,
|
||||
title: "Sample",
|
||||
part: 1,
|
||||
courseName: "Preview",
|
||||
bodyJSON: Data(),
|
||||
exerciseCount: 0,
|
||||
vocabTableCount: 0
|
||||
))
|
||||
}
|
||||
}
|
||||
360
Conjuga/Conjuga/Views/Course/TextbookExerciseView.swift
Normal file
360
Conjuga/Conjuga/Views/Course/TextbookExerciseView.swift
Normal file
@@ -0,0 +1,360 @@
|
||||
import SwiftUI
|
||||
import SharedModels
|
||||
import SwiftData
|
||||
import PencilKit
|
||||
|
||||
/// Interactive fill-in-the-blank view for one textbook exercise.
|
||||
/// Supports keyboard typing OR Apple Pencil handwriting input per prompt.
|
||||
struct TextbookExerciseView: View {
|
||||
let chapter: TextbookChapter
|
||||
let blockIndex: Int
|
||||
|
||||
@Environment(\.cloudModelContextProvider) private var cloudModelContextProvider
|
||||
@State private var answers: [Int: String] = [:]
|
||||
@State private var drawings: [Int: PKDrawing] = [:]
|
||||
@State private var grades: [Int: TextbookGrade] = [:]
|
||||
@State private var inputMode: InputMode = .keyboard
|
||||
@State private var activePencilPromptNumber: Int?
|
||||
@State private var isRecognizing = false
|
||||
@State private var isChecked = false
|
||||
@State private var recognizedTextForActive: String = ""
|
||||
|
||||
private var cloudModelContext: ModelContext { cloudModelContextProvider() }
|
||||
|
||||
enum InputMode: String {
|
||||
case keyboard
|
||||
case pencil
|
||||
}
|
||||
|
||||
private var block: TextbookBlock? {
|
||||
chapter.blocks().first { $0.index == blockIndex }
|
||||
}
|
||||
|
||||
private var answerByNumber: [Int: TextbookAnswerItem] {
|
||||
guard let items = block?.answerItems else { return [:] }
|
||||
var out: [Int: TextbookAnswerItem] = [:]
|
||||
for it in items {
|
||||
out[it.number] = it
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
ScrollView {
|
||||
VStack(alignment: .leading, spacing: 16) {
|
||||
if let b = block {
|
||||
headerView(b)
|
||||
inputModePicker
|
||||
exerciseBody(b)
|
||||
checkButton(b)
|
||||
} else {
|
||||
ContentUnavailableView(
|
||||
"Exercise not found",
|
||||
systemImage: "questionmark.circle"
|
||||
)
|
||||
}
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
.navigationTitle("Exercise \(block?.exerciseId ?? "")")
|
||||
.navigationBarTitleDisplayMode(.inline)
|
||||
.onAppear(perform: loadPreviousAttempt)
|
||||
}
|
||||
|
||||
private func headerView(_ b: TextbookBlock) -> some View {
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
Text("Chapter \(chapter.number): \(chapter.title)")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
Text("Exercise \(b.exerciseId ?? "")")
|
||||
.font(.title2.bold())
|
||||
if let inst = b.instruction, !inst.isEmpty {
|
||||
Text(stripInlineEmphasis(inst))
|
||||
.font(.callout)
|
||||
.foregroundStyle(.secondary)
|
||||
.fixedSize(horizontal: false, vertical: true)
|
||||
}
|
||||
if let extra = b.extra, !extra.isEmpty {
|
||||
ForEach(Array(extra.enumerated()), id: \.offset) { _, e in
|
||||
Text(stripInlineEmphasis(e))
|
||||
.font(.callout)
|
||||
.fixedSize(horizontal: false, vertical: true)
|
||||
.padding(8)
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
.background(Color.secondary.opacity(0.1), in: RoundedRectangle(cornerRadius: 8))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private var inputModePicker: some View {
|
||||
Picker("Input mode", selection: $inputMode) {
|
||||
Label("Keyboard", systemImage: "keyboard").tag(InputMode.keyboard)
|
||||
Label("Pencil", systemImage: "pencil.tip").tag(InputMode.pencil)
|
||||
}
|
||||
.pickerStyle(.segmented)
|
||||
}
|
||||
|
||||
private func exerciseBody(_ b: TextbookBlock) -> some View {
|
||||
VStack(alignment: .leading, spacing: 14) {
|
||||
if b.freeform == true {
|
||||
VStack(alignment: .leading, spacing: 6) {
|
||||
Label("Freeform exercise", systemImage: "text.bubble")
|
||||
.font(.subheadline.weight(.semibold))
|
||||
.foregroundStyle(.orange)
|
||||
Text("Answers will vary. Use this space to write your own responses; they won't be auto-checked.")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
.padding()
|
||||
.background(Color.orange.opacity(0.1), in: RoundedRectangle(cornerRadius: 10))
|
||||
}
|
||||
let rawPrompts = b.prompts ?? []
|
||||
let prompts = rawPrompts.isEmpty ? synthesizedPrompts(b) : rawPrompts
|
||||
if prompts.isEmpty && b.extra?.isEmpty == false {
|
||||
Text("Fill in the blanks above; answers will be graded when you tap Check.")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
} else {
|
||||
ForEach(Array(prompts.enumerated()), id: \.offset) { i, prompt in
|
||||
promptRow(index: i, prompt: prompt, expected: answerByNumber[i + 1])
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// When the source exercise prompts were embedded in a bitmap (common in
|
||||
/// this textbook), we have no text for each question — only the answer
|
||||
/// key. Synthesize numbered placeholders so the user still gets one input
|
||||
/// field per answer.
|
||||
private func synthesizedPrompts(_ b: TextbookBlock) -> [String] {
|
||||
guard let items = b.answerItems, !items.isEmpty else { return [] }
|
||||
return items.map { "\($0.number)." }
|
||||
}
|
||||
|
||||
private func promptRow(index: Int, prompt: String, expected: TextbookAnswerItem?) -> some View {
|
||||
let number = index + 1
|
||||
let grade = grades[number]
|
||||
return VStack(alignment: .leading, spacing: 8) {
|
||||
HStack(alignment: .top, spacing: 8) {
|
||||
if let grade {
|
||||
Image(systemName: iconFor(grade))
|
||||
.foregroundStyle(colorFor(grade))
|
||||
.font(.title3)
|
||||
.padding(.top, 2)
|
||||
}
|
||||
Text(stripInlineEmphasis(prompt))
|
||||
.font(.body)
|
||||
.fixedSize(horizontal: false, vertical: true)
|
||||
}
|
||||
|
||||
switch inputMode {
|
||||
case .keyboard:
|
||||
TextField("Your answer", text: binding(for: number))
|
||||
.textFieldStyle(.roundedBorder)
|
||||
.textInputAutocapitalization(.never)
|
||||
.disableAutocorrection(true)
|
||||
.font(.body)
|
||||
.disabled(isChecked)
|
||||
case .pencil:
|
||||
pencilRow(number: number)
|
||||
}
|
||||
|
||||
if isChecked, let grade, grade != .correct, let expected {
|
||||
HStack(spacing: 6) {
|
||||
Text("Answer:")
|
||||
.font(.caption.weight(.semibold))
|
||||
Text(expected.answer)
|
||||
.font(.caption)
|
||||
if !expected.alternates.isEmpty {
|
||||
Text("(also: \(expected.alternates.joined(separator: ", ")))")
|
||||
.font(.caption2)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
}
|
||||
.foregroundStyle(colorFor(grade))
|
||||
}
|
||||
}
|
||||
.padding(10)
|
||||
.background(backgroundFor(grade), in: RoundedRectangle(cornerRadius: 8))
|
||||
}
|
||||
|
||||
private func pencilRow(number: Int) -> some View {
|
||||
VStack(alignment: .leading, spacing: 6) {
|
||||
HandwritingCanvas(
|
||||
drawing: bindingDrawing(for: number),
|
||||
onDrawingChanged: { recognizePencil(for: number) }
|
||||
)
|
||||
.frame(height: 100)
|
||||
.background(.fill.quinary, in: RoundedRectangle(cornerRadius: 10))
|
||||
.overlay(RoundedRectangle(cornerRadius: 10).stroke(.separator, lineWidth: 1))
|
||||
|
||||
HStack {
|
||||
if let typed = answers[number], !typed.isEmpty {
|
||||
Text("Recognized: \(typed)")
|
||||
.font(.caption)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
Spacer()
|
||||
Button("Clear") {
|
||||
drawings[number] = PKDrawing()
|
||||
answers[number] = ""
|
||||
}
|
||||
.font(.caption)
|
||||
.tint(.secondary)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func checkButton(_ b: TextbookBlock) -> some View {
|
||||
let hasAnyAnswer = answers.values.contains { !$0.isEmpty }
|
||||
let disabled = b.freeform == true || (!isChecked && !hasAnyAnswer)
|
||||
return Button {
|
||||
if isChecked {
|
||||
resetExercise()
|
||||
} else {
|
||||
checkAnswers(b)
|
||||
}
|
||||
} label: {
|
||||
Text(isChecked ? "Try again" : "Check answers")
|
||||
.font(.headline)
|
||||
.frame(maxWidth: .infinity)
|
||||
.padding(.vertical, 10)
|
||||
}
|
||||
.buttonStyle(.borderedProminent)
|
||||
.tint(.orange)
|
||||
.disabled(disabled)
|
||||
}
|
||||
|
||||
// MARK: - Actions
|
||||
|
||||
private func checkAnswers(_ b: TextbookBlock) {
|
||||
guard let prompts = b.prompts else { return }
|
||||
var newGrades: [Int: TextbookGrade] = [:]
|
||||
var states: [TextbookPromptState] = []
|
||||
for (i, _) in prompts.enumerated() {
|
||||
let number = i + 1
|
||||
let user = answers[number] ?? ""
|
||||
let expected = answerByNumber[number]
|
||||
let canonical = expected?.answer ?? ""
|
||||
let alts = expected?.alternates ?? []
|
||||
let grade: TextbookGrade
|
||||
if canonical.isEmpty {
|
||||
grade = .wrong
|
||||
} else {
|
||||
grade = AnswerChecker.grade(userText: user, canonical: canonical, alternates: alts)
|
||||
}
|
||||
newGrades[number] = grade
|
||||
states.append(TextbookPromptState(number: number, userText: user, grade: grade))
|
||||
}
|
||||
grades = newGrades
|
||||
isChecked = true
|
||||
saveAttempt(states: states, exerciseId: b.exerciseId ?? "")
|
||||
}
|
||||
|
||||
private func resetExercise() {
|
||||
answers.removeAll()
|
||||
drawings.removeAll()
|
||||
grades.removeAll()
|
||||
isChecked = false
|
||||
}
|
||||
|
||||
private func recognizePencil(for number: Int) {
|
||||
guard let drawing = drawings[number], !drawing.strokes.isEmpty else { return }
|
||||
isRecognizing = true
|
||||
Task {
|
||||
let result = await HandwritingRecognizer.recognize(drawing: drawing)
|
||||
await MainActor.run {
|
||||
answers[number] = result.text
|
||||
isRecognizing = false
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private func saveAttempt(states: [TextbookPromptState], exerciseId: String) {
|
||||
let attemptId = TextbookExerciseAttempt.attemptId(
|
||||
courseName: chapter.courseName,
|
||||
exerciseId: exerciseId
|
||||
)
|
||||
let descriptor = FetchDescriptor<TextbookExerciseAttempt>(
|
||||
predicate: #Predicate<TextbookExerciseAttempt> { $0.id == attemptId }
|
||||
)
|
||||
let context = cloudModelContext
|
||||
let existing = (try? context.fetch(descriptor))?.first
|
||||
let attempt = existing ?? TextbookExerciseAttempt(
|
||||
id: attemptId,
|
||||
courseName: chapter.courseName,
|
||||
chapterNumber: chapter.number,
|
||||
exerciseId: exerciseId
|
||||
)
|
||||
if existing == nil { context.insert(attempt) }
|
||||
attempt.lastAttemptAt = Date()
|
||||
attempt.setPromptStates(states)
|
||||
try? context.save()
|
||||
}
|
||||
|
||||
private func loadPreviousAttempt() {
|
||||
guard let b = block else { return }
|
||||
let attemptId = TextbookExerciseAttempt.attemptId(
|
||||
courseName: chapter.courseName,
|
||||
exerciseId: b.exerciseId ?? ""
|
||||
)
|
||||
let descriptor = FetchDescriptor<TextbookExerciseAttempt>(
|
||||
predicate: #Predicate<TextbookExerciseAttempt> { $0.id == attemptId }
|
||||
)
|
||||
guard let attempt = (try? cloudModelContext.fetch(descriptor))?.first else { return }
|
||||
for s in attempt.promptStates() {
|
||||
answers[s.number] = s.userText
|
||||
grades[s.number] = s.grade
|
||||
}
|
||||
isChecked = !grades.isEmpty
|
||||
}
|
||||
|
||||
// MARK: - Bindings
|
||||
|
||||
private func binding(for number: Int) -> Binding<String> {
|
||||
Binding(
|
||||
get: { answers[number] ?? "" },
|
||||
set: { answers[number] = $0 }
|
||||
)
|
||||
}
|
||||
|
||||
private func bindingDrawing(for number: Int) -> Binding<PKDrawing> {
|
||||
Binding(
|
||||
get: { drawings[number] ?? PKDrawing() },
|
||||
set: { drawings[number] = $0 }
|
||||
)
|
||||
}
|
||||
|
||||
// MARK: - UI helpers
|
||||
|
||||
private func iconFor(_ grade: TextbookGrade) -> String {
|
||||
switch grade {
|
||||
case .correct: return "checkmark.circle.fill"
|
||||
case .close: return "circle.lefthalf.filled"
|
||||
case .wrong: return "xmark.circle.fill"
|
||||
}
|
||||
}
|
||||
|
||||
private func colorFor(_ grade: TextbookGrade) -> Color {
|
||||
switch grade {
|
||||
case .correct: return .green
|
||||
case .close: return .orange
|
||||
case .wrong: return .red
|
||||
}
|
||||
}
|
||||
|
||||
private func backgroundFor(_ grade: TextbookGrade?) -> Color {
|
||||
guard let grade else { return Color.secondary.opacity(0.05) }
|
||||
switch grade {
|
||||
case .correct: return .green.opacity(0.12)
|
||||
case .close: return .orange.opacity(0.12)
|
||||
case .wrong: return .red.opacity(0.12)
|
||||
}
|
||||
}
|
||||
|
||||
private func stripInlineEmphasis(_ s: String) -> String {
|
||||
s.replacingOccurrences(of: "**", with: "")
|
||||
.replacingOccurrences(of: "*", with: "")
|
||||
}
|
||||
}
|
||||
@@ -6,11 +6,19 @@ struct VocabFlashcardView: View {
|
||||
let cards: [VocabCard]
|
||||
let speechService: SpeechService
|
||||
let onDone: () -> Void
|
||||
/// Optional deck context — when present and the title indicates a stem-
|
||||
/// changing deck, each card gets an inline conjugation toggle.
|
||||
var deckTitle: String? = nil
|
||||
|
||||
@Environment(\.cloudModelContextProvider) private var cloudModelContextProvider
|
||||
@State private var currentIndex = 0
|
||||
@State private var isRevealed = false
|
||||
@State private var sessionCorrect = 0
|
||||
@State private var showConjugation = false
|
||||
|
||||
private var isStemChangingDeck: Bool {
|
||||
(deckTitle ?? "").localizedCaseInsensitiveContains("stem changing")
|
||||
}
|
||||
|
||||
private var cloudModelContext: ModelContext { cloudModelContextProvider() }
|
||||
|
||||
@@ -61,6 +69,25 @@ struct VocabFlashcardView: View {
|
||||
.padding(12)
|
||||
}
|
||||
.glassEffect(in: .circle)
|
||||
|
||||
if isStemChangingDeck {
|
||||
Button {
|
||||
withAnimation(.smooth) { showConjugation.toggle() }
|
||||
} label: {
|
||||
Label(
|
||||
showConjugation ? "Hide conjugation" : "Show conjugation",
|
||||
systemImage: showConjugation ? "chevron.up" : "chevron.down"
|
||||
)
|
||||
.font(.subheadline.weight(.medium))
|
||||
}
|
||||
.buttonStyle(.bordered)
|
||||
.tint(.blue)
|
||||
|
||||
if showConjugation {
|
||||
StemChangeConjugationView(infinitive: stripToInfinitive(card.front))
|
||||
.transition(.opacity.combined(with: .move(edge: .top)))
|
||||
}
|
||||
}
|
||||
}
|
||||
.transition(.blurReplace)
|
||||
} else {
|
||||
@@ -111,6 +138,7 @@ struct VocabFlashcardView: View {
|
||||
guard currentIndex > 0 else { return }
|
||||
withAnimation(.smooth) {
|
||||
isRevealed = false
|
||||
showConjugation = false
|
||||
currentIndex -= 1
|
||||
}
|
||||
} label: {
|
||||
@@ -125,6 +153,7 @@ struct VocabFlashcardView: View {
|
||||
Button {
|
||||
withAnimation(.smooth) {
|
||||
isRevealed = false
|
||||
showConjugation = false
|
||||
currentIndex += 1
|
||||
}
|
||||
} label: {
|
||||
@@ -189,9 +218,25 @@ struct VocabFlashcardView: View {
|
||||
// Next card
|
||||
withAnimation(.smooth) {
|
||||
isRevealed = false
|
||||
showConjugation = false
|
||||
currentIndex += 1
|
||||
}
|
||||
}
|
||||
|
||||
/// Card fronts may be plain infinitives ("cerrar") or, in reversed decks,
|
||||
/// stored as English. Strip any reflexive-se suffix or parenthetical notes
|
||||
/// to improve the verb lookup hit rate.
|
||||
private func stripToInfinitive(_ s: String) -> String {
|
||||
var t = s.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
if let paren = t.firstIndex(of: "(") {
|
||||
t = String(t[..<paren]).trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
}
|
||||
if t.hasSuffix("se") && t.count > 4 {
|
||||
// "acostarse" → "acostar" for verb lookup
|
||||
t = String(t.dropLast(2))
|
||||
}
|
||||
return t
|
||||
}
|
||||
}
|
||||
|
||||
#Preview {
|
||||
|
||||
@@ -1,16 +1,27 @@
|
||||
import SwiftUI
|
||||
import SharedModels
|
||||
import SwiftData
|
||||
import FoundationModels
|
||||
|
||||
@Generable
|
||||
private struct ChatWordInfo {
|
||||
@Guide(description: "Dictionary base form") var baseForm: String
|
||||
@Guide(description: "English translation") var english: String
|
||||
@Guide(description: "Part of speech") var partOfSpeech: String
|
||||
}
|
||||
|
||||
struct ChatView: View {
|
||||
let conversation: Conversation
|
||||
|
||||
@Environment(\.cloudModelContextProvider) private var cloudModelContextProvider
|
||||
@Environment(DictionaryService.self) private var dictionary
|
||||
@State private var service = ConversationService()
|
||||
@State private var messages: [ChatMessage] = []
|
||||
@State private var inputText = ""
|
||||
@State private var errorMessage: String?
|
||||
@State private var hasStarted = false
|
||||
@State private var selectedWord: WordAnnotation?
|
||||
@State private var lookupCache: [String: WordAnnotation] = [:]
|
||||
|
||||
private var cloudContext: ModelContext { cloudModelContextProvider() }
|
||||
|
||||
@@ -21,8 +32,10 @@ struct ChatView: View {
|
||||
ScrollView {
|
||||
LazyVStack(spacing: 12) {
|
||||
ForEach(messages) { message in
|
||||
ChatBubble(message: message)
|
||||
.id(message.id)
|
||||
ChatBubble(message: message, dictionary: dictionary, lookupCache: $lookupCache) { word in
|
||||
selectedWord = word
|
||||
}
|
||||
.id(message.id)
|
||||
}
|
||||
|
||||
if service.isResponding {
|
||||
@@ -68,6 +81,10 @@ struct ChatView: View {
|
||||
}
|
||||
.navigationTitle(conversation.scenario)
|
||||
.navigationBarTitleDisplayMode(.inline)
|
||||
.sheet(item: $selectedWord) { word in
|
||||
ChatWordDetailSheet(word: word)
|
||||
.presentationDetents([.height(200)])
|
||||
}
|
||||
.alert("Error", isPresented: .init(
|
||||
get: { errorMessage != nil },
|
||||
set: { if !$0 { errorMessage = nil } }
|
||||
@@ -121,6 +138,9 @@ struct ChatView: View {
|
||||
|
||||
private struct ChatBubble: View {
|
||||
let message: ChatMessage
|
||||
let dictionary: DictionaryService
|
||||
@Binding var lookupCache: [String: WordAnnotation]
|
||||
let onWordTap: (WordAnnotation) -> Void
|
||||
|
||||
private var isUser: Bool { message.role == "user" }
|
||||
|
||||
@@ -129,11 +149,15 @@ private struct ChatBubble: View {
|
||||
if isUser { Spacer(minLength: 60) }
|
||||
|
||||
VStack(alignment: isUser ? .trailing : .leading, spacing: 4) {
|
||||
Text(message.content)
|
||||
.font(.body)
|
||||
.padding(.horizontal, 14)
|
||||
.padding(.vertical, 10)
|
||||
.background(isUser ? AnyShapeStyle(.green.opacity(0.2)) : AnyShapeStyle(.fill.quaternary), in: RoundedRectangle(cornerRadius: 16))
|
||||
if isUser {
|
||||
Text(message.content)
|
||||
.font(.body)
|
||||
.padding(.horizontal, 14)
|
||||
.padding(.vertical, 10)
|
||||
.background(.green.opacity(0.2), in: RoundedRectangle(cornerRadius: 16))
|
||||
} else {
|
||||
tappableBubble
|
||||
}
|
||||
|
||||
if let correction = message.correction, !correction.isEmpty {
|
||||
Text(correction)
|
||||
@@ -147,4 +171,179 @@ private struct ChatBubble: View {
|
||||
}
|
||||
.padding(.horizontal)
|
||||
}
|
||||
|
||||
private var tappableBubble: some View {
|
||||
let words = message.content.components(separatedBy: " ")
|
||||
return ChatFlowLayout(spacing: 0) {
|
||||
ForEach(Array(words.enumerated()), id: \.offset) { _, word in
|
||||
ChatWordButton(word: word, dictionary: dictionary, cache: lookupCache) { annotation in
|
||||
if annotation.english.isEmpty {
|
||||
lookupWordAsync(annotation.word)
|
||||
} else {
|
||||
onWordTap(annotation)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
.padding(.horizontal, 14)
|
||||
.padding(.vertical, 10)
|
||||
.background(.fill.quaternary, in: RoundedRectangle(cornerRadius: 16))
|
||||
}
|
||||
|
||||
private func lookupWordAsync(_ word: String) {
|
||||
// Try dictionary first
|
||||
if let entry = dictionary.lookup(word) {
|
||||
let annotation = WordAnnotation(word: word, baseForm: entry.baseForm, english: entry.english, partOfSpeech: entry.partOfSpeech)
|
||||
lookupCache[word] = annotation
|
||||
onWordTap(annotation)
|
||||
return
|
||||
}
|
||||
|
||||
// Show loading then AI lookup
|
||||
onWordTap(WordAnnotation(word: word, baseForm: word, english: "Looking up...", partOfSpeech: ""))
|
||||
|
||||
Task {
|
||||
do {
|
||||
let session = LanguageModelSession(instructions: "You are a Spanish dictionary. Provide base form, English translation, and part of speech.")
|
||||
let response = try await session.respond(to: "Word: \"\(word)\"", generating: ChatWordInfo.self)
|
||||
let info = response.content
|
||||
let annotation = WordAnnotation(word: word, baseForm: info.baseForm, english: info.english, partOfSpeech: info.partOfSpeech)
|
||||
lookupCache[word] = annotation
|
||||
onWordTap(annotation)
|
||||
} catch {
|
||||
onWordTap(WordAnnotation(word: word, baseForm: word, english: "Lookup unavailable", partOfSpeech: ""))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Chat Word Button
|
||||
|
||||
private struct ChatWordButton: View {
|
||||
let word: String
|
||||
let dictionary: DictionaryService
|
||||
let cache: [String: WordAnnotation]
|
||||
let onTap: (WordAnnotation) -> Void
|
||||
|
||||
private var cleaned: String {
|
||||
word.lowercased()
|
||||
.trimmingCharacters(in: .punctuationCharacters)
|
||||
.trimmingCharacters(in: .whitespaces)
|
||||
}
|
||||
|
||||
private var annotation: WordAnnotation? {
|
||||
if let cached = cache[cleaned] { return cached }
|
||||
if let entry = dictionary.lookup(cleaned) {
|
||||
return WordAnnotation(word: cleaned, baseForm: entry.baseForm, english: entry.english, partOfSpeech: entry.partOfSpeech)
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
Button {
|
||||
onTap(annotation ?? WordAnnotation(word: cleaned, baseForm: cleaned, english: "", partOfSpeech: ""))
|
||||
} label: {
|
||||
Text(word + " ")
|
||||
.font(.body)
|
||||
.foregroundStyle(.primary)
|
||||
.underline(annotation != nil, color: .teal.opacity(0.3))
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Word Detail Sheet
|
||||
|
||||
private struct ChatWordDetailSheet: View {
|
||||
let word: WordAnnotation
|
||||
|
||||
var body: some View {
|
||||
VStack(spacing: 16) {
|
||||
HStack {
|
||||
Text(word.word)
|
||||
.font(.title2.bold())
|
||||
Spacer()
|
||||
if !word.partOfSpeech.isEmpty {
|
||||
Text(word.partOfSpeech)
|
||||
.font(.caption.weight(.medium))
|
||||
.foregroundStyle(.secondary)
|
||||
.padding(.horizontal, 8)
|
||||
.padding(.vertical, 4)
|
||||
.background(.fill.tertiary, in: Capsule())
|
||||
}
|
||||
}
|
||||
|
||||
Divider()
|
||||
|
||||
if word.english == "Looking up..." {
|
||||
HStack(spacing: 8) {
|
||||
ProgressView()
|
||||
Text("Looking up word...")
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
.frame(maxWidth: .infinity)
|
||||
} else {
|
||||
VStack(alignment: .leading, spacing: 8) {
|
||||
if !word.baseForm.isEmpty && word.baseForm != word.word {
|
||||
HStack {
|
||||
Text("Base form:")
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(.secondary)
|
||||
Text(word.baseForm)
|
||||
.font(.subheadline.weight(.semibold))
|
||||
.italic()
|
||||
}
|
||||
}
|
||||
if !word.english.isEmpty {
|
||||
HStack {
|
||||
Text("English:")
|
||||
.font(.subheadline)
|
||||
.foregroundStyle(.secondary)
|
||||
Text(word.english)
|
||||
.font(.subheadline.weight(.semibold))
|
||||
}
|
||||
}
|
||||
}
|
||||
.frame(maxWidth: .infinity, alignment: .leading)
|
||||
}
|
||||
|
||||
Spacer()
|
||||
}
|
||||
.padding()
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Chat Flow Layout
|
||||
|
||||
private struct ChatFlowLayout: Layout {
|
||||
var spacing: CGFloat = 0
|
||||
func sizeThatFits(proposal: ProposedViewSize, subviews: Subviews, cache: inout ()) -> CGSize {
|
||||
let rows = computeRows(proposal: proposal, subviews: subviews)
|
||||
var height: CGFloat = 0
|
||||
for row in rows { height += row.map { $0.height }.max() ?? 0 }
|
||||
height += CGFloat(max(0, rows.count - 1)) * spacing
|
||||
return CGSize(width: proposal.width ?? 0, height: height)
|
||||
}
|
||||
func placeSubviews(in bounds: CGRect, proposal: ProposedViewSize, subviews: Subviews, cache: inout ()) {
|
||||
let rows = computeRows(proposal: proposal, subviews: subviews)
|
||||
var y = bounds.minY; var idx = 0
|
||||
for row in rows {
|
||||
var x = bounds.minX; let rh = row.map { $0.height }.max() ?? 0
|
||||
for size in row {
|
||||
subviews[idx].place(at: CGPoint(x: x, y: y), proposal: ProposedViewSize(size))
|
||||
x += size.width; idx += 1
|
||||
}
|
||||
y += rh + spacing
|
||||
}
|
||||
}
|
||||
private func computeRows(proposal: ProposedViewSize, subviews: Subviews) -> [[CGSize]] {
|
||||
let mw = proposal.width ?? .infinity; var rows: [[CGSize]] = [[]]; var cw: CGFloat = 0
|
||||
for sv in subviews {
|
||||
let s = sv.sizeThatFits(.unspecified)
|
||||
if cw + s.width > mw && !rows[rows.count - 1].isEmpty { rows.append([]); cw = 0 }
|
||||
rows[rows.count - 1].append(s); cw += s.width
|
||||
}
|
||||
return rows
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2,11 +2,31 @@ import SwiftUI
|
||||
import SwiftData
|
||||
import SharedModels
|
||||
|
||||
enum IrregularityCategory: String, CaseIterable, Identifiable {
|
||||
case anyIrregular = "Any Irregular"
|
||||
case spelling = "Spelling Change"
|
||||
case stemChange = "Stem Change"
|
||||
case uniqueIrregular = "Unique Irregular"
|
||||
|
||||
var id: String { rawValue }
|
||||
|
||||
var systemImage: String {
|
||||
switch self {
|
||||
case .anyIrregular: "asterisk"
|
||||
case .spelling: "character.cursor.ibeam"
|
||||
case .stemChange: "arrow.triangle.2.circlepath"
|
||||
case .uniqueIrregular: "star"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct VerbListView: View {
|
||||
@Environment(\.modelContext) private var modelContext
|
||||
@State private var verbs: [Verb] = []
|
||||
@State private var irregularityByVerbId: [Int: Set<IrregularityCategory>] = [:]
|
||||
@State private var searchText = ""
|
||||
@State private var selectedLevel: String?
|
||||
@State private var selectedIrregularity: IrregularityCategory?
|
||||
@State private var selectedVerb: Verb?
|
||||
|
||||
private var filteredVerbs: [Verb] {
|
||||
@@ -14,6 +34,12 @@ struct VerbListView: View {
|
||||
if let level = selectedLevel {
|
||||
result = result.filter { VerbLevelGroup.matches($0.level, selectedLevel: level) }
|
||||
}
|
||||
if let category = selectedIrregularity {
|
||||
result = result.filter { verb in
|
||||
guard let cats = irregularityByVerbId[verb.id] else { return false }
|
||||
return category == .anyIrregular ? !cats.isEmpty : cats.contains(category)
|
||||
}
|
||||
}
|
||||
if !searchText.isEmpty {
|
||||
let query = searchText.lowercased()
|
||||
result = result.filter {
|
||||
@@ -30,20 +56,50 @@ struct VerbListView: View {
|
||||
NavigationSplitView {
|
||||
List(filteredVerbs, selection: $selectedVerb) { verb in
|
||||
NavigationLink(value: verb) {
|
||||
VerbRowView(verb: verb)
|
||||
VerbRowView(verb: verb, irregularities: irregularityByVerbId[verb.id] ?? [])
|
||||
}
|
||||
}
|
||||
.navigationTitle("Verbs")
|
||||
.searchable(text: $searchText, prompt: "Search verbs...")
|
||||
.safeAreaInset(edge: .top, spacing: 0) {
|
||||
if hasActiveFilter {
|
||||
activeFilterBar
|
||||
}
|
||||
}
|
||||
.toolbar {
|
||||
ToolbarItem(placement: .topBarTrailing) {
|
||||
Menu {
|
||||
Button("All Levels") { selectedLevel = nil }
|
||||
ForEach(levels, id: \.self) { level in
|
||||
Button(level.capitalized) { selectedLevel = level }
|
||||
Section("Level") {
|
||||
Button {
|
||||
selectedLevel = nil
|
||||
} label: {
|
||||
Label("All Levels", systemImage: selectedLevel == nil ? "checkmark" : "")
|
||||
}
|
||||
ForEach(levels, id: \.self) { level in
|
||||
Button {
|
||||
selectedLevel = level
|
||||
} label: {
|
||||
Label(level.capitalized, systemImage: selectedLevel == level ? "checkmark" : "")
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Section("Irregularity") {
|
||||
Button {
|
||||
selectedIrregularity = nil
|
||||
} label: {
|
||||
Label("All Verbs", systemImage: selectedIrregularity == nil ? "checkmark" : "")
|
||||
}
|
||||
ForEach(IrregularityCategory.allCases) { category in
|
||||
Button {
|
||||
selectedIrregularity = category
|
||||
} label: {
|
||||
Label(category.rawValue, systemImage: selectedIrregularity == category ? "checkmark" : category.systemImage)
|
||||
}
|
||||
}
|
||||
}
|
||||
} label: {
|
||||
Label("Filter", systemImage: "line.3.horizontal.decrease.circle")
|
||||
Label("Filter", systemImage: hasActiveFilter ? "line.3.horizontal.decrease.circle.fill" : "line.3.horizontal.decrease.circle")
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -58,6 +114,51 @@ struct VerbListView: View {
|
||||
}
|
||||
}
|
||||
|
||||
private var hasActiveFilter: Bool {
|
||||
selectedLevel != nil || selectedIrregularity != nil
|
||||
}
|
||||
|
||||
@ViewBuilder
|
||||
private var activeFilterBar: some View {
|
||||
HStack(spacing: 8) {
|
||||
if let level = selectedLevel {
|
||||
filterChip(text: level.capitalized, systemImage: "graduationcap") {
|
||||
selectedLevel = nil
|
||||
}
|
||||
}
|
||||
if let cat = selectedIrregularity {
|
||||
filterChip(text: cat.rawValue, systemImage: cat.systemImage) {
|
||||
selectedIrregularity = nil
|
||||
}
|
||||
}
|
||||
Spacer()
|
||||
Text("\(filteredVerbs.count)")
|
||||
.font(.caption.monospacedDigit())
|
||||
.foregroundStyle(.secondary)
|
||||
}
|
||||
.padding(.horizontal)
|
||||
.padding(.vertical, 8)
|
||||
.background(.bar)
|
||||
}
|
||||
|
||||
private func filterChip(text: String, systemImage: String, onClear: @escaping () -> Void) -> some View {
|
||||
Button(action: onClear) {
|
||||
HStack(spacing: 4) {
|
||||
Image(systemName: systemImage)
|
||||
.font(.caption2)
|
||||
Text(text)
|
||||
.font(.caption.weight(.medium))
|
||||
Image(systemName: "xmark")
|
||||
.font(.caption2)
|
||||
}
|
||||
.padding(.horizontal, 10)
|
||||
.padding(.vertical, 5)
|
||||
.background(.blue.opacity(0.15), in: Capsule())
|
||||
.foregroundStyle(.blue)
|
||||
}
|
||||
.buttonStyle(.plain)
|
||||
}
|
||||
|
||||
private func loadVerbs() {
|
||||
// Hit the shared local container directly, bypassing @Environment.
|
||||
guard let container = SharedStore.localContainer else {
|
||||
@@ -69,12 +170,30 @@ struct VerbListView: View {
|
||||
}
|
||||
let context = ModelContext(container)
|
||||
verbs = ReferenceStore(context: context).fetchVerbs()
|
||||
print("[VerbListView] loaded \(verbs.count) verbs (container: \(ObjectIdentifier(container)))")
|
||||
irregularityByVerbId = buildIrregularityIndex(context: context)
|
||||
print("[VerbListView] loaded \(verbs.count) verbs, \(irregularityByVerbId.count) flagged irregular (container: \(ObjectIdentifier(container)))")
|
||||
}
|
||||
|
||||
private func buildIrregularityIndex(context: ModelContext) -> [Int: Set<IrregularityCategory>] {
|
||||
let spans = (try? context.fetch(FetchDescriptor<IrregularSpan>())) ?? []
|
||||
var index: [Int: Set<IrregularityCategory>] = [:]
|
||||
for span in spans {
|
||||
let category: IrregularityCategory
|
||||
switch span.spanType {
|
||||
case 100..<200: category = .spelling
|
||||
case 200..<300: category = .stemChange
|
||||
case 300..<400: category = .uniqueIrregular
|
||||
default: continue
|
||||
}
|
||||
index[span.verbId, default: []].insert(category)
|
||||
}
|
||||
return index
|
||||
}
|
||||
}
|
||||
|
||||
struct VerbRowView: View {
|
||||
let verb: Verb
|
||||
var irregularities: Set<IrregularityCategory> = []
|
||||
|
||||
var body: some View {
|
||||
HStack {
|
||||
@@ -88,14 +207,39 @@ struct VerbRowView: View {
|
||||
|
||||
Spacer()
|
||||
|
||||
Text(verb.level.prefix(3).uppercased())
|
||||
.font(.caption2)
|
||||
.fontWeight(.semibold)
|
||||
.padding(.horizontal, 8)
|
||||
.padding(.vertical, 4)
|
||||
.background(levelColor(verb.level).opacity(0.15))
|
||||
.foregroundStyle(levelColor(verb.level))
|
||||
.clipShape(Capsule())
|
||||
HStack(spacing: 4) {
|
||||
ForEach(orderedIrregularities, id: \.self) { cat in
|
||||
Image(systemName: cat.systemImage)
|
||||
.font(.caption2.weight(.semibold))
|
||||
.foregroundStyle(irregularityColor(cat))
|
||||
.help(cat.rawValue)
|
||||
.accessibilityLabel(cat.rawValue)
|
||||
}
|
||||
|
||||
Text(verb.level.prefix(3).uppercased())
|
||||
.font(.caption2)
|
||||
.fontWeight(.semibold)
|
||||
.padding(.horizontal, 8)
|
||||
.padding(.vertical, 4)
|
||||
.background(levelColor(verb.level).opacity(0.15))
|
||||
.foregroundStyle(levelColor(verb.level))
|
||||
.clipShape(Capsule())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private var orderedIrregularities: [IrregularityCategory] {
|
||||
// Order: unique > stem > spelling (most notable first)
|
||||
let order: [IrregularityCategory] = [.uniqueIrregular, .stemChange, .spelling]
|
||||
return order.filter { irregularities.contains($0) }
|
||||
}
|
||||
|
||||
private func irregularityColor(_ category: IrregularityCategory) -> Color {
|
||||
switch category {
|
||||
case .uniqueIrregular: return .purple
|
||||
case .stemChange: return .orange
|
||||
case .spelling: return .teal
|
||||
case .anyIrregular: return .gray
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
1
Conjuga/Conjuga/textbook_data.json
Normal file
1
Conjuga/Conjuga/textbook_data.json
Normal file
File diff suppressed because one or more lines are too long
25099
Conjuga/Conjuga/textbook_vocab.json
Normal file
25099
Conjuga/Conjuga/textbook_vocab.json
Normal file
File diff suppressed because it is too large
Load Diff
95
Conjuga/ConjugaUITests/AllChaptersScreenshotTests.swift
Normal file
95
Conjuga/ConjugaUITests/AllChaptersScreenshotTests.swift
Normal file
@@ -0,0 +1,95 @@
|
||||
import XCTest
|
||||
|
||||
/// Screenshot every chapter of the textbook — one top + one bottom frame each —
|
||||
/// so you can visually audit parsing / rendering issues across all 30 chapters.
|
||||
final class AllChaptersScreenshotTests: XCTestCase {
|
||||
|
||||
override func setUpWithError() throws {
|
||||
continueAfterFailure = true
|
||||
}
|
||||
|
||||
func testScreenshotEveryChapter() throws {
|
||||
let app = XCUIApplication()
|
||||
app.launchArguments += ["-onboardingComplete", "YES"]
|
||||
app.launch()
|
||||
|
||||
let courseTab = app.tabBars.buttons["Course"]
|
||||
XCTAssertTrue(courseTab.waitForExistence(timeout: 5))
|
||||
courseTab.tap()
|
||||
|
||||
let textbookRow = app.buttons.containing(NSPredicate(
|
||||
format: "label CONTAINS[c] 'Complete Spanish'"
|
||||
)).firstMatch
|
||||
XCTAssertTrue(textbookRow.waitForExistence(timeout: 5))
|
||||
textbookRow.tap()
|
||||
|
||||
// NOTE: SwiftUI List preserves scroll position across navigation pushes,
|
||||
// so visiting chapters in-order means the next one is already visible
|
||||
// after we return from the previous one. No need to reset.
|
||||
attach(app, name: "00-chapter-list-top")
|
||||
|
||||
for chapter in 1...30 {
|
||||
guard let row = findChapterRow(app: app, chapter: chapter) else {
|
||||
XCTFail("Chapter \(chapter) row not reachable")
|
||||
continue
|
||||
}
|
||||
row.tap()
|
||||
|
||||
// Chapter body — wait until the chapter's title appears as a nav bar label
|
||||
_ = app.navigationBars.firstMatch.waitForExistence(timeout: 3)
|
||||
|
||||
attach(app, name: String(format: "ch%02d-top", chapter))
|
||||
// One big scroll to sample the bottom of the chapter
|
||||
dragFullScreen(app, direction: .up)
|
||||
dragFullScreen(app, direction: .up)
|
||||
attach(app, name: String(format: "ch%02d-bottom", chapter))
|
||||
|
||||
tapNavBack(app)
|
||||
// Small settle wait
|
||||
_ = app.navigationBars.firstMatch.waitForExistence(timeout: 2)
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Helpers
|
||||
|
||||
private enum DragDirection { case up, down }
|
||||
|
||||
private func dragFullScreen(_ app: XCUIApplication, direction: DragDirection) {
|
||||
let top = app.coordinate(withNormalizedOffset: CGVector(dx: 0.5, dy: 0.12))
|
||||
let bot = app.coordinate(withNormalizedOffset: CGVector(dx: 0.5, dy: 0.88))
|
||||
switch direction {
|
||||
case .up: bot.press(forDuration: 0.1, thenDragTo: top)
|
||||
case .down: top.press(forDuration: 0.1, thenDragTo: bot)
|
||||
}
|
||||
}
|
||||
|
||||
private func findChapterRow(app: XCUIApplication, chapter: Int) -> XCUIElement? {
|
||||
// Chapter row accessibility label: "<n>, <title>, ..." (SwiftUI composes
|
||||
// label from inner Texts). Match by starting number.
|
||||
let predicate = NSPredicate(format: "label BEGINSWITH %@", "\(chapter),")
|
||||
let row = app.buttons.matching(predicate).firstMatch
|
||||
|
||||
if row.exists && row.isHittable { return row }
|
||||
|
||||
// Scroll down up to 8 times searching for the row — chapters visited
|
||||
// in order, so usually 0–2 swipes suffice.
|
||||
for _ in 0..<8 {
|
||||
if row.exists && row.isHittable { return row }
|
||||
dragFullScreen(app, direction: .up)
|
||||
}
|
||||
return row.exists ? row : nil
|
||||
}
|
||||
|
||||
private func tapNavBack(_ app: XCUIApplication) {
|
||||
let back = app.navigationBars.buttons.firstMatch
|
||||
if back.exists && back.isHittable { back.tap() }
|
||||
}
|
||||
|
||||
private func attach(_ app: XCUIApplication, name: String) {
|
||||
let screenshot = app.screenshot()
|
||||
let attachment = XCTAttachment(screenshot: screenshot)
|
||||
attachment.name = name
|
||||
attachment.lifetime = .keepAlways
|
||||
add(attachment)
|
||||
}
|
||||
}
|
||||
66
Conjuga/ConjugaUITests/StemChangeToggleTests.swift
Normal file
66
Conjuga/ConjugaUITests/StemChangeToggleTests.swift
Normal file
@@ -0,0 +1,66 @@
|
||||
import XCTest
|
||||
|
||||
final class StemChangeToggleTests: XCTestCase {
|
||||
|
||||
override func setUpWithError() throws {
|
||||
continueAfterFailure = false
|
||||
}
|
||||
|
||||
func testStemChangeConjugationToggle() throws {
|
||||
let app = XCUIApplication()
|
||||
app.launchArguments += ["-onboardingComplete", "YES"]
|
||||
app.launch()
|
||||
|
||||
// Course → LanGo Beginner I → Week 4 → E-IE stem-changing verbs
|
||||
app.tabBars.buttons["Course"].tap()
|
||||
|
||||
// Locate the E-IE deck row. Deck titles appear as static text / button.
|
||||
// Scroll until visible, then tap.
|
||||
let deckPredicate = NSPredicate(format: "label CONTAINS[c] 'E-IE stem changing verbs' AND NOT label CONTAINS[c] 'REVÉS'")
|
||||
let deckRow = app.buttons.matching(deckPredicate).firstMatch
|
||||
|
||||
let listRef = app.coordinate(withNormalizedOffset: CGVector(dx: 0.5, dy: 0.85))
|
||||
let topRef = app.coordinate(withNormalizedOffset: CGVector(dx: 0.5, dy: 0.10))
|
||||
for _ in 0..<12 {
|
||||
if deckRow.exists && deckRow.isHittable { break }
|
||||
listRef.press(forDuration: 0.1, thenDragTo: topRef)
|
||||
}
|
||||
XCTAssertTrue(deckRow.waitForExistence(timeout: 3), "E-IE deck row missing")
|
||||
deckRow.tap()
|
||||
|
||||
attach(app, name: "01-deck-top")
|
||||
|
||||
// Tap "Show conjugation" on the first card
|
||||
let showBtn = app.buttons.matching(NSPredicate(format: "label BEGINSWITH 'Show conjugation'")).firstMatch
|
||||
XCTAssertTrue(showBtn.waitForExistence(timeout: 3), "Show conjugation button missing")
|
||||
showBtn.tap()
|
||||
|
||||
// Wait for the conjugation rows + animation to settle.
|
||||
let yoLabel = app.staticTexts["yo"].firstMatch
|
||||
XCTAssertTrue(yoLabel.waitForExistence(timeout: 3), "yo row not rendered")
|
||||
// Give the transition time to complete before snapshotting.
|
||||
Thread.sleep(forTimeInterval: 0.6)
|
||||
attach(app, name: "02-conjugation-open")
|
||||
|
||||
// Also confirm all expected person labels are rendered.
|
||||
for person in ["yo", "tú", "nosotros"] {
|
||||
XCTAssertTrue(
|
||||
app.staticTexts[person].firstMatch.exists,
|
||||
"missing conjugation row for \(person)"
|
||||
)
|
||||
}
|
||||
|
||||
// Tap again to hide
|
||||
let hideBtn = app.buttons.matching(NSPredicate(format: "label BEGINSWITH 'Hide conjugation'")).firstMatch
|
||||
XCTAssertTrue(hideBtn.waitForExistence(timeout: 2))
|
||||
hideBtn.tap()
|
||||
}
|
||||
|
||||
private func attach(_ app: XCUIApplication, name: String) {
|
||||
let s = app.screenshot()
|
||||
let a = XCTAttachment(screenshot: s)
|
||||
a.name = name
|
||||
a.lifetime = .keepAlways
|
||||
add(a)
|
||||
}
|
||||
}
|
||||
80
Conjuga/ConjugaUITests/TextbookFlowUITests.swift
Normal file
80
Conjuga/ConjugaUITests/TextbookFlowUITests.swift
Normal file
@@ -0,0 +1,80 @@
|
||||
import XCTest
|
||||
|
||||
final class TextbookFlowUITests: XCTestCase {
|
||||
|
||||
override func setUpWithError() throws {
|
||||
continueAfterFailure = false
|
||||
}
|
||||
|
||||
func testTextbookFlow() throws {
|
||||
let app = XCUIApplication()
|
||||
// Skip onboarding via defaults (already set by run script, but harmless to override)
|
||||
app.launchArguments += ["-onboardingComplete", "YES"]
|
||||
app.launch()
|
||||
|
||||
// Dashboard should be default tab. Switch to Course.
|
||||
let courseTab = app.tabBars.buttons["Course"]
|
||||
XCTAssertTrue(courseTab.waitForExistence(timeout: 5), "Course tab missing")
|
||||
courseTab.tap()
|
||||
|
||||
// Attach a screenshot of the Course list
|
||||
attach(app, name: "01-course-list")
|
||||
|
||||
// Tap the Textbook entry
|
||||
let textbookRow = app.buttons.containing(NSPredicate(
|
||||
format: "label CONTAINS[c] 'Complete Spanish'"
|
||||
)).firstMatch
|
||||
XCTAssertTrue(textbookRow.waitForExistence(timeout: 5), "Textbook row missing in Course")
|
||||
textbookRow.tap()
|
||||
|
||||
attach(app, name: "02-textbook-chapter-list")
|
||||
|
||||
// Tap chapter 1 — should navigate to reader
|
||||
let chapterOneRow = app.buttons.containing(NSPredicate(
|
||||
format: "label CONTAINS[c] 'Nouns, Articles'"
|
||||
)).firstMatch
|
||||
XCTAssertTrue(chapterOneRow.waitForExistence(timeout: 5), "Chapter 1 row missing")
|
||||
chapterOneRow.tap()
|
||||
|
||||
attach(app, name: "03-chapter-body")
|
||||
|
||||
// Find the first exercise link ("Exercise 1.1")
|
||||
let exerciseRow = app.buttons.containing(NSPredicate(
|
||||
format: "label CONTAINS[c] 'Exercise 1.1'"
|
||||
)).firstMatch
|
||||
XCTAssertTrue(exerciseRow.waitForExistence(timeout: 5), "Exercise 1.1 link missing")
|
||||
exerciseRow.tap()
|
||||
|
||||
attach(app, name: "04-exercise-view")
|
||||
|
||||
// Check presence of input fields: at least a few numbered prompts
|
||||
// Text fields use SwiftUI placeholder "Your answer"
|
||||
let firstField = app.textFields["Your answer"].firstMatch
|
||||
XCTAssertTrue(firstField.waitForExistence(timeout: 5), "No input fields rendered for exercise")
|
||||
firstField.tap()
|
||||
firstField.typeText("el")
|
||||
|
||||
attach(app, name: "05-exercise-typed-el")
|
||||
|
||||
// Tap Check answers
|
||||
let checkButton = app.buttons["Check answers"]
|
||||
XCTAssertTrue(checkButton.waitForExistence(timeout: 3), "Check answers button missing")
|
||||
checkButton.tap()
|
||||
|
||||
attach(app, name: "06-exercise-graded")
|
||||
|
||||
// The first answer to Exercise 1.1 is "el" — we should see the first prompt
|
||||
// graded correct. Iterating too deeply is fragile; just take a screenshot
|
||||
// and check for presence of either a checkmark-like label or "Try again".
|
||||
let tryAgain = app.buttons["Try again"]
|
||||
XCTAssertTrue(tryAgain.waitForExistence(timeout: 3), "Grading did not complete")
|
||||
}
|
||||
|
||||
private func attach(_ app: XCUIApplication, name: String) {
|
||||
let screenshot = app.screenshot()
|
||||
let attachment = XCTAttachment(screenshot: screenshot)
|
||||
attachment.name = name
|
||||
attachment.lifetime = .keepAlways
|
||||
add(attachment)
|
||||
}
|
||||
}
|
||||
53
Conjuga/ConjugaUITests/VocabGridTests.swift
Normal file
53
Conjuga/ConjugaUITests/VocabGridTests.swift
Normal file
@@ -0,0 +1,53 @@
|
||||
import XCTest
|
||||
|
||||
final class VocabGridTests: XCTestCase {
|
||||
|
||||
override func setUpWithError() throws {
|
||||
continueAfterFailure = false
|
||||
}
|
||||
|
||||
/// Verifies the chapter reader renders vocab tables as a paired Spanish↔English grid.
|
||||
func testChapter4VocabGrid() throws {
|
||||
let app = XCUIApplication()
|
||||
app.launchArguments += ["-onboardingComplete", "YES"]
|
||||
app.launch()
|
||||
|
||||
app.tabBars.buttons["Course"].tap()
|
||||
|
||||
let textbookRow = app.buttons.containing(NSPredicate(
|
||||
format: "label CONTAINS[c] 'Complete Spanish'"
|
||||
)).firstMatch
|
||||
XCTAssertTrue(textbookRow.waitForExistence(timeout: 5))
|
||||
textbookRow.tap()
|
||||
|
||||
let ch4 = app.buttons["textbook-chapter-row-4"]
|
||||
XCTAssertTrue(ch4.waitForExistence(timeout: 3))
|
||||
ch4.tap()
|
||||
|
||||
attach(app, name: "01-ch4-top")
|
||||
|
||||
// Tap the first vocab disclosure — "Vocabulary (N items)"
|
||||
let vocabButton = app.buttons.matching(NSPredicate(
|
||||
format: "label BEGINSWITH 'Vocabulary ('"
|
||||
)).firstMatch
|
||||
XCTAssertTrue(vocabButton.waitForExistence(timeout: 3))
|
||||
vocabButton.tap()
|
||||
Thread.sleep(forTimeInterval: 0.4)
|
||||
|
||||
attach(app, name: "02-ch4-vocab-open")
|
||||
|
||||
// Scroll a little and screenshot a deeper vocab — numbers table is
|
||||
// typically a few screens down in chapter 4.
|
||||
app.swipeUp(velocity: .fast)
|
||||
app.swipeUp(velocity: .fast)
|
||||
attach(app, name: "03-ch4-deeper")
|
||||
}
|
||||
|
||||
private func attach(_ app: XCUIApplication, name: String) {
|
||||
let s = app.screenshot()
|
||||
let a = XCTAttachment(screenshot: s)
|
||||
a.name = name
|
||||
a.lifetime = .keepAlways
|
||||
add(a)
|
||||
}
|
||||
}
|
||||
@@ -41,14 +41,16 @@ struct CombinedProvider: TimelineProvider {
|
||||
private func fetchWordOfDay(for date: Date) -> WordOfDay? {
|
||||
guard let localURL = SharedStore.localStoreURL() else { return nil }
|
||||
|
||||
// MUST declare all 6 local entities to match the main app's schema.
|
||||
// Declaring a subset would cause SwiftData to destructively migrate the store
|
||||
// on open, dropping the entities not listed here.
|
||||
// MUST declare all 7 local entities to match the main app's schema.
|
||||
// Declaring a subset would cause SwiftData to destructively migrate the
|
||||
// store on open, dropping the entities not listed here (this is how we
|
||||
// previously lost all TextbookChapter rows on every widget refresh).
|
||||
let config = ModelConfiguration(
|
||||
"local",
|
||||
schema: Schema([
|
||||
Verb.self, VerbForm.self, IrregularSpan.self,
|
||||
TenseGuide.self, CourseDeck.self, VocabCard.self,
|
||||
TextbookChapter.self,
|
||||
]),
|
||||
url: localURL,
|
||||
cloudKitDatabase: .none
|
||||
@@ -56,6 +58,7 @@ struct CombinedProvider: TimelineProvider {
|
||||
guard let container = try? ModelContainer(
|
||||
for: Verb.self, VerbForm.self, IrregularSpan.self,
|
||||
TenseGuide.self, CourseDeck.self, VocabCard.self,
|
||||
TextbookChapter.self,
|
||||
configurations: config
|
||||
) else { return nil }
|
||||
|
||||
|
||||
@@ -32,14 +32,16 @@ struct WordOfDayProvider: TimelineProvider {
|
||||
private func fetchWordOfDay(for date: Date) -> WordOfDay? {
|
||||
guard let localURL = SharedStore.localStoreURL() else { return nil }
|
||||
|
||||
// MUST declare all 6 local entities to match the main app's schema.
|
||||
// Declaring a subset would cause SwiftData to destructively migrate the store
|
||||
// on open, dropping the entities not listed here.
|
||||
// MUST declare all 7 local entities to match the main app's schema.
|
||||
// Declaring a subset would cause SwiftData to destructively migrate the
|
||||
// store on open, dropping the entities not listed here (this is how we
|
||||
// previously lost all TextbookChapter rows on every widget refresh).
|
||||
let config = ModelConfiguration(
|
||||
"local",
|
||||
schema: Schema([
|
||||
Verb.self, VerbForm.self, IrregularSpan.self,
|
||||
TenseGuide.self, CourseDeck.self, VocabCard.self,
|
||||
TextbookChapter.self,
|
||||
]),
|
||||
url: localURL,
|
||||
cloudKitDatabase: .none
|
||||
@@ -47,6 +49,7 @@ struct WordOfDayProvider: TimelineProvider {
|
||||
guard let container = try? ModelContainer(
|
||||
for: Verb.self, VerbForm.self, IrregularSpan.self,
|
||||
TenseGuide.self, CourseDeck.self, VocabCard.self,
|
||||
TextbookChapter.self,
|
||||
configurations: config
|
||||
) else { return nil }
|
||||
|
||||
|
||||
374
Conjuga/Scripts/textbook/build_book.py
Normal file
374
Conjuga/Scripts/textbook/build_book.py
Normal file
@@ -0,0 +1,374 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Merge chapters.json + answers.json + ocr.json → book.json (single source).
|
||||
|
||||
Also emits vocab_cards.json: flashcards derived from vocab_image blocks where
|
||||
OCR text parses as a clean two-column (Spanish ↔ English) table.
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
CHAPTERS_JSON = HERE / "chapters.json"
|
||||
ANSWERS_JSON = HERE / "answers.json"
|
||||
OCR_JSON = HERE / "ocr.json"
|
||||
OUT_BOOK = HERE / "book.json"
|
||||
OUT_VOCAB = HERE / "vocab_cards.json"
|
||||
|
||||
COURSE_NAME = "Complete Spanish Step-by-Step"
|
||||
|
||||
# Heuristic: parseable "Spanish | English" vocab rows.
|
||||
# OCR usually produces "word — translation" or "word translation" separated
|
||||
# by 2+ spaces. We detect rows that contain both Spanish and English words.
|
||||
SPANISH_ACCENT_RE = re.compile(r"[áéíóúñüÁÉÍÓÚÑÜ¿¡]")
|
||||
SPANISH_ARTICLES = {"el", "la", "los", "las", "un", "una", "unos", "unas"}
|
||||
ENGLISH_STARTERS = {"the", "a", "an", "to", "my", "his", "her", "our", "their", "your", "some"}
|
||||
# English-only words that would never appear as Spanish
|
||||
ENGLISH_ONLY_WORDS = {"the", "he", "she", "it", "we", "they", "I", "is", "are", "was", "were",
|
||||
"been", "have", "has", "had", "will", "would", "should", "could"}
|
||||
SEP_RE = re.compile(r"[ \t]{2,}|\s[—–−-]\s")
|
||||
|
||||
|
||||
def classify_line(line: str) -> str:
|
||||
"""Return 'es', 'en', or 'unknown' for the dominant language of a vocab line."""
|
||||
line = line.strip()
|
||||
if not line:
|
||||
return "unknown"
|
||||
# Accent = definitely Spanish
|
||||
if SPANISH_ACCENT_RE.search(line):
|
||||
return "es"
|
||||
first = line.split()[0].lower().strip(",.;:")
|
||||
if first in SPANISH_ARTICLES:
|
||||
return "es"
|
||||
if first in ENGLISH_STARTERS:
|
||||
return "en"
|
||||
# Check if the leading word is an English-only function word
|
||||
if first in ENGLISH_ONLY_WORDS:
|
||||
return "en"
|
||||
return "unknown"
|
||||
|
||||
|
||||
def looks_english(word: str) -> bool:
|
||||
"""Legacy helper — kept for try_split_row below."""
|
||||
w = word.lower().strip()
|
||||
if not w:
|
||||
return False
|
||||
if SPANISH_ACCENT_RE.search(w):
|
||||
return False
|
||||
if w in SPANISH_ARTICLES:
|
||||
return False
|
||||
if w in ENGLISH_STARTERS or w in ENGLISH_ONLY_WORDS:
|
||||
return True
|
||||
return bool(re.match(r"^[a-z][a-z\s'/()\-,.]*$", w))
|
||||
|
||||
|
||||
def try_split_row(line: str) -> "tuple[str, str] | None":
|
||||
"""Split a line into (spanish, english) if it looks like a vocab entry."""
|
||||
line = line.strip()
|
||||
if not line or len(line) < 3:
|
||||
return None
|
||||
# Try explicit separators first
|
||||
parts = SEP_RE.split(line)
|
||||
parts = [p.strip() for p in parts if p.strip()]
|
||||
if len(parts) == 2:
|
||||
spanish, english = parts
|
||||
if looks_english(english) and not looks_english(spanish.split()[0]):
|
||||
return (spanish, english)
|
||||
return None
|
||||
|
||||
|
||||
def load(p: Path) -> dict:
|
||||
return json.loads(p.read_text(encoding="utf-8"))
|
||||
|
||||
|
||||
def build_vocab_cards_for_block(block: dict, ocr_entry: dict, chapter: dict, context_title: str, idx: int) -> list:
|
||||
"""Given a vocab_image block + its OCR lines, derive flashcards.
|
||||
|
||||
Vision OCR reads top-to-bottom, left-to-right; a two-column vocab table
|
||||
produces Spanish lines first, then English lines. We split the list in
|
||||
half when one side is predominantly Spanish and the other English.
|
||||
Per-line '—' separators are also supported as a fallback.
|
||||
"""
|
||||
cards = []
|
||||
if not ocr_entry:
|
||||
return cards
|
||||
lines = [l.strip() for l in ocr_entry.get("lines", []) if l.strip()]
|
||||
if not lines:
|
||||
return cards
|
||||
|
||||
def card(front: str, back: str) -> dict:
|
||||
return {
|
||||
"front": front,
|
||||
"back": back,
|
||||
"chapter": chapter["number"],
|
||||
"chapterTitle": chapter["title"],
|
||||
"section": context_title,
|
||||
"sourceImage": block["src"],
|
||||
}
|
||||
|
||||
# Attempt 1: explicit inline separator (e.g. "la casa — the house")
|
||||
inline = []
|
||||
all_inline = True
|
||||
for line in lines:
|
||||
pair = try_split_row(line)
|
||||
if pair:
|
||||
inline.append(pair)
|
||||
else:
|
||||
all_inline = False
|
||||
break
|
||||
if all_inline and inline:
|
||||
for es, en in inline:
|
||||
cards.append(card(es, en))
|
||||
return cards
|
||||
|
||||
# Attempt 2: block-alternating layout.
|
||||
# Vision OCR reads columns top-to-bottom, so a 2-col table rendered across
|
||||
# 2 visual columns produces runs like: [ES...ES][EN...EN][ES...ES][EN...EN]
|
||||
# We classify each line, smooth "unknown" using neighbors, then pair
|
||||
# same-sized consecutive ES/EN blocks.
|
||||
classes = [classify_line(l) for l in lines]
|
||||
|
||||
# Pass 1: fill unknowns using nearest non-unknown neighbor (forward)
|
||||
last_known = "unknown"
|
||||
forward = []
|
||||
for c in classes:
|
||||
if c != "unknown":
|
||||
last_known = c
|
||||
forward.append(last_known)
|
||||
# Pass 2: backfill leading unknowns (backward)
|
||||
last_known = "unknown"
|
||||
backward = [""] * len(classes)
|
||||
for i in range(len(classes) - 1, -1, -1):
|
||||
if classes[i] != "unknown":
|
||||
last_known = classes[i]
|
||||
backward[i] = last_known
|
||||
# Merge: prefer forward unless still unknown
|
||||
resolved = []
|
||||
for f, b in zip(forward, backward):
|
||||
if f != "unknown":
|
||||
resolved.append(f)
|
||||
elif b != "unknown":
|
||||
resolved.append(b)
|
||||
else:
|
||||
resolved.append("unknown")
|
||||
|
||||
# Group consecutive same-lang lines
|
||||
blocks: list = []
|
||||
cur_lang: "str | None" = None
|
||||
cur_block: list = []
|
||||
for line, lang in zip(lines, resolved):
|
||||
if lang != cur_lang:
|
||||
if cur_block and cur_lang is not None:
|
||||
blocks.append((cur_lang, cur_block))
|
||||
cur_block = [line]
|
||||
cur_lang = lang
|
||||
else:
|
||||
cur_block.append(line)
|
||||
if cur_block and cur_lang is not None:
|
||||
blocks.append((cur_lang, cur_block))
|
||||
|
||||
# Walk blocks pairing ES then EN of equal length
|
||||
i = 0
|
||||
while i < len(blocks) - 1:
|
||||
lang_a, lines_a = blocks[i]
|
||||
lang_b, lines_b = blocks[i + 1]
|
||||
if lang_a == "es" and lang_b == "en" and len(lines_a) == len(lines_b):
|
||||
for es, en in zip(lines_a, lines_b):
|
||||
cards.append(card(es, en))
|
||||
i += 2
|
||||
continue
|
||||
# If reversed order (some pages have EN column on left), try that too
|
||||
if lang_a == "en" and lang_b == "es" and len(lines_a) == len(lines_b):
|
||||
for es, en in zip(lines_b, lines_a):
|
||||
cards.append(card(es, en))
|
||||
i += 2
|
||||
continue
|
||||
i += 1
|
||||
|
||||
return cards
|
||||
|
||||
|
||||
def clean_instruction(text: str) -> str:
|
||||
"""Strip leading/trailing emphasis markers from a parsed instruction."""
|
||||
# Our XHTML parser emitted * and ** for emphasis; flatten them
|
||||
t = re.sub(r"\*+", "", text)
|
||||
return t.strip()
|
||||
|
||||
|
||||
def merge() -> None:
|
||||
chapters_data = load(CHAPTERS_JSON)
|
||||
answers_data = load(ANSWERS_JSON)
|
||||
try:
|
||||
ocr_data = load(OCR_JSON)
|
||||
except FileNotFoundError:
|
||||
print("ocr.json not found — proceeding with empty OCR data")
|
||||
ocr_data = {}
|
||||
|
||||
answers = answers_data["answers"]
|
||||
chapters = chapters_data["chapters"]
|
||||
parts = chapters_data.get("part_memberships", {})
|
||||
|
||||
book_chapters = []
|
||||
all_vocab_cards = []
|
||||
missing_ocr = set()
|
||||
current_section_title = ""
|
||||
|
||||
for ch in chapters:
|
||||
out_blocks = []
|
||||
current_section_title = ch["title"]
|
||||
|
||||
for bi, block in enumerate(ch["blocks"]):
|
||||
k = block["kind"]
|
||||
|
||||
if k == "heading":
|
||||
current_section_title = block["text"]
|
||||
out_blocks.append(block)
|
||||
continue
|
||||
|
||||
if k == "paragraph":
|
||||
out_blocks.append(block)
|
||||
continue
|
||||
|
||||
if k == "key_vocab_header":
|
||||
out_blocks.append(block)
|
||||
continue
|
||||
|
||||
if k == "vocab_image":
|
||||
ocr_entry = ocr_data.get(block["src"])
|
||||
if ocr_entry is None:
|
||||
missing_ocr.add(block["src"])
|
||||
derived = build_vocab_cards_for_block(
|
||||
block, ocr_entry, ch, current_section_title, bi
|
||||
)
|
||||
all_vocab_cards.extend(derived)
|
||||
out_blocks.append({
|
||||
"kind": "vocab_table",
|
||||
"sourceImage": block["src"],
|
||||
"ocrLines": ocr_entry.get("lines", []) if ocr_entry else [],
|
||||
"ocrConfidence": ocr_entry.get("confidence", 0.0) if ocr_entry else 0.0,
|
||||
"cardCount": len(derived),
|
||||
})
|
||||
continue
|
||||
|
||||
if k == "exercise":
|
||||
ans = answers.get(block["id"])
|
||||
image_ocr_lines = []
|
||||
for src in block.get("image_refs", []):
|
||||
e = ocr_data.get(src)
|
||||
if e is None:
|
||||
missing_ocr.add(src)
|
||||
continue
|
||||
image_ocr_lines.extend(e.get("lines", []))
|
||||
|
||||
# Build the final prompt list. If we have text prompts from
|
||||
# XHTML, prefer them. Otherwise, attempt to use OCR lines.
|
||||
prompts = [p for p in block.get("prompts", []) if p.strip()]
|
||||
extras = [e for e in block.get("extra", []) if e.strip()]
|
||||
if not prompts and image_ocr_lines:
|
||||
# Extract numbered lines from OCR (look for "1. ..." pattern)
|
||||
for line in image_ocr_lines:
|
||||
m = re.match(r"^(\d+)[.)]\s*(.+)", line.strip())
|
||||
if m:
|
||||
prompts.append(f"{m.group(1)}. {m.group(2)}")
|
||||
|
||||
# Cross-reference prompts with answers
|
||||
sub = ans["subparts"] if ans else []
|
||||
answer_items = []
|
||||
for sp in sub:
|
||||
for it in sp["items"]:
|
||||
answer_items.append({
|
||||
"label": sp["label"],
|
||||
"number": it["number"],
|
||||
"answer": it["answer"],
|
||||
"alternates": it["alternates"],
|
||||
})
|
||||
|
||||
out_blocks.append({
|
||||
"kind": "exercise",
|
||||
"id": block["id"],
|
||||
"ansAnchor": block.get("ans_anchor", ""),
|
||||
"instruction": clean_instruction(block.get("instruction", "")),
|
||||
"extra": extras,
|
||||
"prompts": prompts,
|
||||
"ocrLines": image_ocr_lines,
|
||||
"freeform": ans["freeform"] if ans else False,
|
||||
"answerItems": answer_items,
|
||||
"answerRaw": ans["raw"] if ans else "",
|
||||
"answerSubparts": sub,
|
||||
})
|
||||
continue
|
||||
|
||||
out_blocks.append(block)
|
||||
|
||||
book_chapters.append({
|
||||
"id": ch["id"],
|
||||
"number": ch["number"],
|
||||
"title": ch["title"],
|
||||
"part": ch.get("part"),
|
||||
"blocks": out_blocks,
|
||||
})
|
||||
|
||||
book = {
|
||||
"courseName": COURSE_NAME,
|
||||
"totalChapters": len(book_chapters),
|
||||
"totalExercises": sum(
|
||||
1 for ch in book_chapters for b in ch["blocks"] if b["kind"] == "exercise"
|
||||
),
|
||||
"totalVocabTables": sum(
|
||||
1 for ch in book_chapters for b in ch["blocks"] if b["kind"] == "vocab_table"
|
||||
),
|
||||
"totalVocabCards": len(all_vocab_cards),
|
||||
"parts": parts,
|
||||
"chapters": book_chapters,
|
||||
}
|
||||
OUT_BOOK.write_text(json.dumps(book, ensure_ascii=False))
|
||||
|
||||
# Vocab cards as a separate file (grouped per chapter so they can be seeded
|
||||
# as CourseDecks in the existing schema).
|
||||
vocab_by_chapter: dict = {}
|
||||
for card in all_vocab_cards:
|
||||
vocab_by_chapter.setdefault(card["chapter"], []).append(card)
|
||||
OUT_VOCAB.write_text(json.dumps({
|
||||
"courseName": COURSE_NAME,
|
||||
"chapters": [
|
||||
{
|
||||
"chapter": ch_num,
|
||||
"cards": cards,
|
||||
}
|
||||
for ch_num, cards in sorted(vocab_by_chapter.items())
|
||||
],
|
||||
}, ensure_ascii=False, indent=2))
|
||||
|
||||
# Summary
|
||||
print(f"Wrote {OUT_BOOK}")
|
||||
print(f"Wrote {OUT_VOCAB}")
|
||||
print(f"Chapters: {book['totalChapters']}")
|
||||
print(f"Exercises: {book['totalExercises']}")
|
||||
print(f"Vocab tables: {book['totalVocabTables']}")
|
||||
print(f"Vocab cards (auto): {book['totalVocabCards']}")
|
||||
if missing_ocr:
|
||||
print(f"Missing OCR for {len(missing_ocr)} images (first 5): {sorted(list(missing_ocr))[:5]}")
|
||||
|
||||
# Validation
|
||||
total_exercises = book["totalExercises"]
|
||||
exercises_with_prompts = sum(
|
||||
1 for ch in book_chapters for b in ch["blocks"]
|
||||
if b["kind"] == "exercise" and (b["prompts"] or b["extra"])
|
||||
)
|
||||
exercises_with_answers = sum(
|
||||
1 for ch in book_chapters for b in ch["blocks"]
|
||||
if b["kind"] == "exercise" and b["answerItems"]
|
||||
)
|
||||
exercises_freeform = sum(
|
||||
1 for ch in book_chapters for b in ch["blocks"]
|
||||
if b["kind"] == "exercise" and b["freeform"]
|
||||
)
|
||||
print(f"Exercises with prompts: {exercises_with_prompts}/{total_exercises}")
|
||||
print(f"Exercises with answers: {exercises_with_answers}/{total_exercises}")
|
||||
print(f"Freeform exercises: {exercises_freeform}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
merge()
|
||||
126
Conjuga/Scripts/textbook/build_review.py
Normal file
126
Conjuga/Scripts/textbook/build_review.py
Normal file
@@ -0,0 +1,126 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Render book.json + ocr.json into a static HTML review page.
|
||||
|
||||
The HTML surfaces low-confidence OCR results in red, and shows the parsed
|
||||
exercise prompts/answers next to the original image. Designed for rapid
|
||||
visual diffing against the source book.
|
||||
"""
|
||||
|
||||
import html
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
BOOK = HERE / "book.json"
|
||||
OCR = HERE / "ocr.json"
|
||||
OUT_HTML = HERE / "review.html"
|
||||
EPUB_IMAGES = Path(HERE).parents[2] / "epub_extract" / "OEBPS"
|
||||
IMAGE_REL = EPUB_IMAGES.relative_to(HERE.parent) if False else EPUB_IMAGES
|
||||
|
||||
|
||||
def load(p: Path) -> dict:
|
||||
return json.loads(p.read_text(encoding="utf-8"))
|
||||
|
||||
|
||||
def esc(s: str) -> str:
|
||||
return html.escape(s or "")
|
||||
|
||||
|
||||
def img_tag(src: str) -> str:
|
||||
full = (EPUB_IMAGES / src).resolve()
|
||||
return f'<img src="file://{full}" alt="{esc(src)}" class="src"/>'
|
||||
|
||||
|
||||
def render() -> None:
|
||||
book = load(BOOK)
|
||||
ocr = load(OCR) if OCR.exists() else {}
|
||||
|
||||
out: list = []
|
||||
out.append("""<!DOCTYPE html>
|
||||
<html><head><meta charset='utf-8'><title>Book review</title>
|
||||
<style>
|
||||
body { font-family: -apple-system, system-ui, sans-serif; margin: 2em; max-width: 1000px; color: #222; }
|
||||
h1 { color: #c44; }
|
||||
h2.chapter { background: #eee; padding: 0.5em; border-left: 4px solid #c44; }
|
||||
h3.heading { color: #555; }
|
||||
.para { margin: 0.5em 0; }
|
||||
.vocab-table { background: #fafff0; padding: 0.5em; margin: 0.5em 0; border: 1px solid #bda; border-radius: 6px; }
|
||||
.ocr-line { font-family: ui-monospace, monospace; font-size: 12px; }
|
||||
.lowconf { color: #c44; background: #fee; }
|
||||
.exercise { background: #fff8e8; padding: 0.5em; margin: 0.75em 0; border: 1px solid #cb9; border-radius: 6px; }
|
||||
.prompt { font-family: ui-monospace, monospace; font-size: 13px; margin: 2px 0; }
|
||||
.answer { color: #080; font-family: ui-monospace, monospace; font-size: 13px; }
|
||||
img.src { max-width: 520px; border: 1px solid #ccc; margin: 4px 0; }
|
||||
.kv { color: #04a; font-weight: bold; }
|
||||
summary { cursor: pointer; font-weight: bold; color: #666; }
|
||||
.card-pair { font-family: ui-monospace, monospace; font-size: 12px; }
|
||||
.card-es { color: #04a; }
|
||||
.card-en { color: #555; }
|
||||
.counts { color: #888; font-size: 12px; }
|
||||
</style></head><body>""")
|
||||
out.append(f"<h1>{esc(book['courseName'])} — review</h1>")
|
||||
out.append(f"<p>{book['totalChapters']} chapters · {book['totalExercises']} exercises · {book['totalVocabTables']} vocab tables · {book['totalVocabCards']} auto-derived cards</p>")
|
||||
|
||||
for ch in book["chapters"]:
|
||||
part = ch.get("part")
|
||||
part_str = f" (Part {part})" if part else ""
|
||||
out.append(f"<h2 class='chapter'>Chapter {ch['number']}: {esc(ch['title'])}{esc(part_str)}</h2>")
|
||||
|
||||
for b in ch["blocks"]:
|
||||
kind = b["kind"]
|
||||
if kind == "heading":
|
||||
level = b["level"]
|
||||
out.append(f"<h{level} class='heading'>{esc(b['text'])}</h{level}>")
|
||||
elif kind == "paragraph":
|
||||
out.append(f"<p class='para'>{esc(b['text'])}</p>")
|
||||
elif kind == "key_vocab_header":
|
||||
out.append(f"<p class='kv'>★ Key Vocabulary</p>")
|
||||
elif kind == "vocab_table":
|
||||
src = b["sourceImage"]
|
||||
conf = b["ocrConfidence"]
|
||||
conf_class = "lowconf" if conf < 0.85 else ""
|
||||
out.append(f"<div class='vocab-table'>")
|
||||
out.append(f"<details><summary>vocab {esc(src)} · confidence {conf:.2f} · {b['cardCount']} card(s)</summary>")
|
||||
out.append(img_tag(src))
|
||||
out.append("<div>")
|
||||
for line in b.get("ocrLines", []):
|
||||
out.append(f"<div class='ocr-line {conf_class}'>{esc(line)}</div>")
|
||||
out.append("</div>")
|
||||
# Show derived pairs (if any). We don't have them inline in book.json,
|
||||
# but we can recompute from ocrLines using the same function.
|
||||
out.append("</details></div>")
|
||||
elif kind == "exercise":
|
||||
out.append(f"<div class='exercise'>")
|
||||
out.append(f"<b>Exercise {esc(b['id'])}</b> — <i>{esc(b['instruction'])}</i>")
|
||||
if b.get("extra"):
|
||||
for e in b["extra"]:
|
||||
out.append(f"<div class='para'>{esc(e)}</div>")
|
||||
if b.get("ocrLines"):
|
||||
out.append(f"<details><summary>OCR lines from image</summary>")
|
||||
for line in b["ocrLines"]:
|
||||
out.append(f"<div class='ocr-line'>{esc(line)}</div>")
|
||||
out.append("</details>")
|
||||
if b.get("prompts"):
|
||||
out.append("<div><b>Parsed prompts:</b></div>")
|
||||
for p in b["prompts"]:
|
||||
out.append(f"<div class='prompt'>• {esc(p)}</div>")
|
||||
if b.get("answerItems"):
|
||||
out.append("<div><b>Answer key:</b></div>")
|
||||
for a in b["answerItems"]:
|
||||
label_str = f"{a['label']}. " if a.get("label") else ""
|
||||
alts = ", ".join(a["alternates"])
|
||||
alt_str = f" <span style='color:#999'>(also: {esc(alts)})</span>" if alts else ""
|
||||
out.append(f"<div class='answer'>{esc(label_str)}{a['number']}. {esc(a['answer'])}{alt_str}</div>")
|
||||
if b.get("freeform"):
|
||||
out.append("<div style='color:#c44'>(Freeform — answers will vary)</div>")
|
||||
for img_src in b.get("image_refs", []):
|
||||
out.append(img_tag(img_src))
|
||||
out.append("</div>")
|
||||
|
||||
out.append("</body></html>")
|
||||
OUT_HTML.write_text("\n".join(out), encoding="utf-8")
|
||||
print(f"Wrote {OUT_HTML}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
render()
|
||||
205
Conjuga/Scripts/textbook/extract_answers.py
Normal file
205
Conjuga/Scripts/textbook/extract_answers.py
Normal file
@@ -0,0 +1,205 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Parse ans.xhtml into structured answers.json.
|
||||
|
||||
Output schema:
|
||||
{
|
||||
"answers": {
|
||||
"1.1": {
|
||||
"id": "1.1",
|
||||
"anchor": "ch1ans1",
|
||||
"chapter": 1,
|
||||
"subparts": [
|
||||
{"label": null, "items": [
|
||||
{"number": 1, "answer": "el", "alternates": []},
|
||||
{"number": 2, "answer": "el", "alternates": []},
|
||||
...
|
||||
]}
|
||||
],
|
||||
"freeform": false, # true if "Answers will vary"
|
||||
"raw": "..." # raw text for fallback
|
||||
},
|
||||
"2.4": { # multi-part exercise
|
||||
"subparts": [
|
||||
{"label": "A", "items": [...]},
|
||||
{"label": "B", "items": [...]},
|
||||
{"label": "C", "items": [...]}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
from bs4 import BeautifulSoup, NavigableString
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[3] / "epub_extract" / "OEBPS"
|
||||
OUT = Path(__file__).resolve().parent / "answers.json"
|
||||
|
||||
ANSWER_CLASSES = {"answerq", "answerq1", "answerq2", "answerqa"}
|
||||
EXERCISE_ID_RE = re.compile(r"^([0-9]+)\.([0-9]+)$")
|
||||
SUBPART_LABEL_RE = re.compile(r"^([A-Z])\b")
|
||||
NUMBERED_ITEM_RE = re.compile(r"(?:^|\s)(\d+)\.\s+")
|
||||
FREEFORM_PATTERNS = [
|
||||
re.compile(r"answers? will vary", re.IGNORECASE),
|
||||
re.compile(r"answer will vary", re.IGNORECASE),
|
||||
]
|
||||
OR_TOKEN = "{{OR}}"
|
||||
|
||||
|
||||
def render_with_or(p) -> str:
|
||||
"""Convert <p> to plain text, replacing 'OR' span markers with sentinel."""
|
||||
soup = BeautifulSoup(str(p), "lxml")
|
||||
# Replace <span class="small">OR</span> with sentinel
|
||||
for span in soup.find_all("span"):
|
||||
cls = span.get("class") or []
|
||||
if "small" in cls and span.get_text(strip=True).upper() == "OR":
|
||||
span.replace_with(f" {OR_TOKEN} ")
|
||||
# Drop pagebreak spans
|
||||
for span in soup.find_all("span", attrs={"epub:type": "pagebreak"}):
|
||||
span.decompose()
|
||||
# Drop emphasis but keep text
|
||||
for tag in soup.find_all(["em", "i", "strong", "b"]):
|
||||
tag.unwrap()
|
||||
text = soup.get_text(separator=" ", strip=False)
|
||||
text = re.sub(r"\s+", " ", text).strip()
|
||||
return text
|
||||
|
||||
|
||||
def split_numbered_items(text: str) -> "list[dict]":
|
||||
"""Given '1. el 2. la 3. el ...' return [{'number':1,'answer':'el'}, ...]."""
|
||||
# Find positions of N. tokens
|
||||
matches = list(NUMBERED_ITEM_RE.finditer(text))
|
||||
items = []
|
||||
for i, m in enumerate(matches):
|
||||
num = int(m.group(1))
|
||||
start = m.end()
|
||||
end = matches[i + 1].start() if i + 1 < len(matches) else len(text)
|
||||
body = text[start:end].strip().rstrip(".,;")
|
||||
# Split alternates on the OR token
|
||||
parts = [p.strip() for p in body.split(OR_TOKEN) if p.strip()]
|
||||
if not parts:
|
||||
continue
|
||||
items.append({
|
||||
"number": num,
|
||||
"answer": parts[0],
|
||||
"alternates": parts[1:],
|
||||
})
|
||||
return items
|
||||
|
||||
|
||||
def parse_subpart_label(text: str) -> "tuple[str | None, str]":
|
||||
"""Try to peel a leading subpart label (A, B, C) from the text.
|
||||
Returns (label_or_None, remaining_text)."""
|
||||
# Pattern at start: "A " or "A " (lots of whitespace from <em>A</em><tab>)
|
||||
m = re.match(r"^([A-Z])\s+(?=\d)", text)
|
||||
if m:
|
||||
return m.group(1), text[m.end():]
|
||||
return None, text
|
||||
|
||||
|
||||
def parse_answer_paragraph(p, exercise_id: str) -> "list[dict]":
|
||||
"""Convert one <p> into a list of subparts.
|
||||
For p.answerq, the text typically starts with the exercise id, then items.
|
||||
For p.answerqa, the text starts with a subpart label letter."""
|
||||
raw = render_with_or(p)
|
||||
# Strip the leading exercise id if present
|
||||
raw = re.sub(rf"^{re.escape(exercise_id)}\s*", "", raw)
|
||||
|
||||
label, body = parse_subpart_label(raw)
|
||||
|
||||
# Detect freeform
|
||||
freeform = any(pat.search(body) for pat in FREEFORM_PATTERNS)
|
||||
if freeform:
|
||||
return [{"label": label, "items": [], "freeform": True, "raw": body}]
|
||||
|
||||
items = split_numbered_items(body)
|
||||
return [{"label": label, "items": items, "freeform": False, "raw": body}]
|
||||
|
||||
|
||||
def main() -> None:
|
||||
src = ROOT / "ans.xhtml"
|
||||
soup = BeautifulSoup(src.read_text(encoding="utf-8"), "lxml")
|
||||
body = soup.find("body")
|
||||
|
||||
answers: dict = {}
|
||||
current_chapter = None
|
||||
current_exercise_id: "str | None" = None
|
||||
|
||||
for el in body.find_all(["h3", "p"]):
|
||||
classes = set(el.get("class") or [])
|
||||
|
||||
# Chapter boundary
|
||||
if el.name == "h3" and "h3b" in classes:
|
||||
text = el.get_text(strip=True)
|
||||
m = re.search(r"Chapter\s+(\d+)", text)
|
||||
if m:
|
||||
current_chapter = int(m.group(1))
|
||||
current_exercise_id = None
|
||||
continue
|
||||
|
||||
if el.name != "p" or not (classes & ANSWER_CLASSES):
|
||||
continue
|
||||
|
||||
# Find the exercise-id anchor (only present on p.answerq, not on continuation)
|
||||
a = el.find("a", href=True)
|
||||
ex_link = None
|
||||
if a:
|
||||
link_text = a.get_text(strip=True)
|
||||
if EXERCISE_ID_RE.match(link_text):
|
||||
ex_link = link_text
|
||||
|
||||
if ex_link:
|
||||
current_exercise_id = ex_link
|
||||
anchor = ""
|
||||
href = a.get("href", "")
|
||||
anchor_m = re.search(r"#(ch\d+ans\d+)", href + " " + (a.get("id") or ""))
|
||||
anchor = anchor_m.group(1) if anchor_m else (a.get("id") or "")
|
||||
# Use the anchor's `id` attr if it's the entry id (e.g. "ch1ans1")
|
||||
entry_id = a.get("id") or anchor
|
||||
|
||||
answers[ex_link] = {
|
||||
"id": ex_link,
|
||||
"anchor": entry_id,
|
||||
"chapter": current_chapter,
|
||||
"subparts": [],
|
||||
"freeform": False,
|
||||
"raw": "",
|
||||
}
|
||||
new_subparts = parse_answer_paragraph(el, ex_link)
|
||||
answers[ex_link]["subparts"].extend(new_subparts)
|
||||
answers[ex_link]["raw"] = render_with_or(el)
|
||||
answers[ex_link]["freeform"] = any(sp["freeform"] for sp in new_subparts)
|
||||
else:
|
||||
# Continuation paragraph for current exercise
|
||||
if current_exercise_id and current_exercise_id in answers:
|
||||
more = parse_answer_paragraph(el, current_exercise_id)
|
||||
answers[current_exercise_id]["subparts"].extend(more)
|
||||
if any(sp["freeform"] for sp in more):
|
||||
answers[current_exercise_id]["freeform"] = True
|
||||
|
||||
out = {"answers": answers}
|
||||
OUT.write_text(json.dumps(out, ensure_ascii=False, indent=2))
|
||||
|
||||
total = len(answers)
|
||||
freeform = sum(1 for v in answers.values() if v["freeform"])
|
||||
multipart = sum(1 for v in answers.values() if len(v["subparts"]) > 1)
|
||||
total_items = sum(
|
||||
len(sp["items"]) for v in answers.values() for sp in v["subparts"]
|
||||
)
|
||||
with_alternates = sum(
|
||||
1 for v in answers.values()
|
||||
for sp in v["subparts"] for it in sp["items"]
|
||||
if it["alternates"]
|
||||
)
|
||||
print(f"Exercises with answers: {total}")
|
||||
print(f" freeform: {freeform}")
|
||||
print(f" multi-part (A/B/C): {multipart}")
|
||||
print(f" total numbered items: {total_items}")
|
||||
print(f" items with alternates:{with_alternates}")
|
||||
print(f"Wrote {OUT}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
369
Conjuga/Scripts/textbook/extract_chapters.py
Normal file
369
Conjuga/Scripts/textbook/extract_chapters.py
Normal file
@@ -0,0 +1,369 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Parse all chapter XHTMLs + appendix into structured chapters.json.
|
||||
|
||||
Output schema:
|
||||
{
|
||||
"chapters": [
|
||||
{
|
||||
"id": "ch1",
|
||||
"number": 1,
|
||||
"title": "Nouns, Articles, and Adjectives",
|
||||
"part": 1, # part 1/2/3 or null
|
||||
"blocks": [ # ordered content
|
||||
{"kind": "heading", "level": 3, "text": "..."},
|
||||
{"kind": "paragraph", "text": "...", "hasItalic": false},
|
||||
{"kind": "key_vocab_header", "title": "Los colores (The colors)"},
|
||||
{"kind": "vocab_image", "src": "f0010-03.jpg"},
|
||||
{
|
||||
"kind": "exercise",
|
||||
"id": "1.1",
|
||||
"ans_anchor": "ch1ans1",
|
||||
"instruction": "Write the appropriate...",
|
||||
"image_refs": ["f0005-02.jpg"]
|
||||
},
|
||||
{"kind": "image", "src": "...", "alt": "..."}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[3] / "epub_extract" / "OEBPS"
|
||||
OUT = Path(__file__).resolve().parent / "chapters.json"
|
||||
|
||||
# Common icon images embedded in headings — ignore when collecting content images
|
||||
ICON_IMAGES = {"Common01.jpg", "Common02.jpg", "Common03.jpg", "Common04.jpg", "Common05.jpg"}
|
||||
|
||||
EXERCISE_ID_RE = re.compile(r"Exercise\s+([0-9]+\.[0-9]+)")
|
||||
ANS_REF_RE = re.compile(r"ch(\d+)ans(\d+)")
|
||||
|
||||
|
||||
def clean_text(el) -> str:
|
||||
"""Extract text preserving inline emphasis markers."""
|
||||
if el is None:
|
||||
return ""
|
||||
# Replace <em>/<i> with markdown-ish *...*, <strong>/<b> with **...**
|
||||
html = str(el)
|
||||
soup = BeautifulSoup(html, "lxml")
|
||||
# First: flatten nested emphasis so we don't emit overlapping markers.
|
||||
# For <strong><em>X</em></strong>, drop the inner em (the bold wrapping
|
||||
# already carries the emphasis visually). Same for <em><strong>...</strong></em>.
|
||||
for tag in soup.find_all(["strong", "b"]):
|
||||
for inner in tag.find_all(["em", "i"]):
|
||||
inner.unwrap()
|
||||
for tag in soup.find_all(["em", "i"]):
|
||||
for inner in tag.find_all(["strong", "b"]):
|
||||
inner.unwrap()
|
||||
# Drop ALL inline emphasis. The source has nested/sibling em/strong
|
||||
# patterns that CommonMark can't reliably parse, causing markers to leak
|
||||
# into the UI. Plain text renders cleanly everywhere.
|
||||
for tag in soup.find_all(["em", "i", "strong", "b"]):
|
||||
tag.unwrap()
|
||||
# Drop pagebreak spans
|
||||
for tag in soup.find_all("span", attrs={"epub:type": "pagebreak"}):
|
||||
tag.decompose()
|
||||
# Replace <br/> with newline
|
||||
for br in soup.find_all("br"):
|
||||
br.replace_with("\n")
|
||||
# Use a separator so adjacent inline tags don't concatenate without spaces
|
||||
# (e.g. "<strong><em>Ir</em></strong> and" would otherwise become "Irand").
|
||||
text = soup.get_text(separator=" ", strip=False)
|
||||
# Collapse runs of whitespace first.
|
||||
text = re.sub(r"\s+", " ", text).strip()
|
||||
# Strip any stray asterisks that sneak through (e.g. author's literal *).
|
||||
text = text.replace("*", "")
|
||||
# De-space punctuation
|
||||
text = re.sub(r"\s+([,.;:!?])", r"\1", text)
|
||||
# Tighten brackets that picked up separator-spaces: "( foo )" -> "(foo)"
|
||||
text = re.sub(r"([(\[])\s+", r"\1", text)
|
||||
text = re.sub(r"\s+([)\]])", r"\1", text)
|
||||
# Collapse any double-spaces
|
||||
text = re.sub(r" +", " ", text).strip()
|
||||
return text
|
||||
|
||||
|
||||
def is_exercise_header(h) -> bool:
|
||||
"""Heading with an <a href='ans.xhtml#...'>Exercise N.N</a> link.
|
||||
Chapters 1-16 use h3.h3k; chapters 17+ use h4.h4."""
|
||||
if h.name not in ("h3", "h4"):
|
||||
return False
|
||||
a = h.find("a", href=True)
|
||||
if a and "ans.xhtml" in a["href"]:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def is_key_vocab_header(h) -> bool:
|
||||
"""Heading with 'Key Vocabulary' text (no anchor link to answers)."""
|
||||
if h.name not in ("h3", "h4"):
|
||||
return False
|
||||
text = h.get_text(strip=True)
|
||||
if "Key Vocabulary" in text and not h.find("a", href=lambda v: v and "ans.xhtml" in v):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def extract_image_srcs(parent) -> list:
|
||||
"""Return list of image src attributes, skipping icon images."""
|
||||
srcs = []
|
||||
for img in parent.find_all("img"):
|
||||
src = img.get("src", "")
|
||||
if not src or Path(src).name in ICON_IMAGES:
|
||||
continue
|
||||
srcs.append(src)
|
||||
return srcs
|
||||
|
||||
|
||||
def parse_chapter(path: Path) -> "dict | None":
|
||||
"""Parse one chapter file into structured blocks."""
|
||||
html = path.read_text(encoding="utf-8")
|
||||
soup = BeautifulSoup(html, "lxml")
|
||||
body = soup.find("body")
|
||||
if body is None:
|
||||
return None
|
||||
|
||||
# Chapter number + title
|
||||
number = None
|
||||
title = ""
|
||||
h2s = body.find_all("h2")
|
||||
for h2 in h2s:
|
||||
classes = h2.get("class") or []
|
||||
# Use a separator so consecutive inline tags don't concatenate
|
||||
# (e.g. "<strong><em>Ir</em></strong> and the Future" → "Ir and the Future")
|
||||
text_with_sep = re.sub(r"\s+", " ", h2.get_text(" ", strip=True))
|
||||
# Strip spaces that were inserted before punctuation
|
||||
text_with_sep = re.sub(r"\s+([,.;:!?])", r"\1", text_with_sep).strip()
|
||||
if "h2c" in classes and text_with_sep.isdigit():
|
||||
number = int(text_with_sep)
|
||||
# Chapters 1–16 use h2c1; chapters 17+ use h2-c
|
||||
elif ("h2c1" in classes or "h2-c" in classes) and not title:
|
||||
title = text_with_sep
|
||||
if number is None:
|
||||
# Try id on chapter header (ch1 → 1)
|
||||
for h2 in h2s:
|
||||
id_ = h2.get("id", "")
|
||||
m = re.match(r"ch(\d+)", id_)
|
||||
if m:
|
||||
number = int(m.group(1))
|
||||
break
|
||||
|
||||
chapter_id = path.stem # ch1, ch2, ...
|
||||
|
||||
# Walk section content in document order
|
||||
section = body.find("section") or body
|
||||
blocks: list = []
|
||||
pending_instruction = None # holds italic paragraph following an exercise header
|
||||
|
||||
for el in section.descendants:
|
||||
if el.name is None:
|
||||
continue
|
||||
|
||||
classes = el.get("class") or []
|
||||
|
||||
# Skip nested tags already captured via parent processing
|
||||
# We operate only on direct h2/h3/h4/h5/p elements
|
||||
if el.name not in ("h2", "h3", "h4", "h5", "p"):
|
||||
continue
|
||||
|
||||
# Exercise header detection (h3 in ch1-16, h4 in ch17+)
|
||||
if is_exercise_header(el):
|
||||
a = el.find("a", href=True)
|
||||
href = a["href"] if a else ""
|
||||
m = EXERCISE_ID_RE.search(el.get_text())
|
||||
ex_id = m.group(1) if m else ""
|
||||
anchor_m = ANS_REF_RE.search(href)
|
||||
ans_anchor = anchor_m.group(0) if anchor_m else ""
|
||||
blocks.append({
|
||||
"kind": "exercise",
|
||||
"id": ex_id,
|
||||
"ans_anchor": ans_anchor,
|
||||
"instruction": "",
|
||||
"image_refs": [],
|
||||
"prompts": []
|
||||
})
|
||||
pending_instruction = blocks[-1]
|
||||
continue
|
||||
|
||||
# Key Vocabulary header
|
||||
if is_key_vocab_header(el):
|
||||
blocks.append({"kind": "key_vocab_header", "title": "Key Vocabulary"})
|
||||
pending_instruction = None
|
||||
continue
|
||||
|
||||
# Other headings
|
||||
if el.name in ("h2", "h3", "h4", "h5"):
|
||||
if el.name == "h2":
|
||||
# Skip the chapter-number/chapter-title h2s we already captured
|
||||
continue
|
||||
txt = clean_text(el)
|
||||
if txt:
|
||||
blocks.append({
|
||||
"kind": "heading",
|
||||
"level": int(el.name[1]),
|
||||
"text": txt,
|
||||
})
|
||||
pending_instruction = None
|
||||
continue
|
||||
|
||||
# Paragraphs
|
||||
if el.name == "p":
|
||||
imgs = extract_image_srcs(el)
|
||||
text = clean_text(el)
|
||||
p_classes = set(classes)
|
||||
|
||||
# Skip pure blank-line class ("nump" = underscore lines under number prompts)
|
||||
if p_classes & {"nump", "numpa"} and not text:
|
||||
continue
|
||||
|
||||
# Exercise prompt: <p class="number">1. Prompt text</p>
|
||||
# Also number1, number2 (continuation numbering), numbera, numbert
|
||||
if pending_instruction is not None and p_classes & {"number", "number1", "number2", "numbera", "numbert"}:
|
||||
if text:
|
||||
pending_instruction["prompts"].append(text)
|
||||
continue
|
||||
|
||||
# Image container for a pending exercise
|
||||
if pending_instruction is not None and imgs and not text:
|
||||
pending_instruction["image_refs"].extend(imgs)
|
||||
continue
|
||||
|
||||
# Instruction line right after the exercise header
|
||||
if pending_instruction is not None and text and not imgs and not pending_instruction["instruction"]:
|
||||
pending_instruction["instruction"] = text
|
||||
continue
|
||||
|
||||
# While in pending-exercise state, extra text paragraphs are word
|
||||
# banks / context ("from the following list:" etc) — keep pending alive.
|
||||
if pending_instruction is not None and text and not imgs:
|
||||
pending_instruction.setdefault("extra", []).append(text)
|
||||
continue
|
||||
|
||||
# Paragraphs that contain an image belong to vocab/key-vocab callouts
|
||||
if imgs and not text:
|
||||
for src in imgs:
|
||||
blocks.append({"kind": "vocab_image", "src": src})
|
||||
continue
|
||||
|
||||
# Mixed paragraph: image with caption
|
||||
if imgs and text:
|
||||
for src in imgs:
|
||||
blocks.append({"kind": "vocab_image", "src": src})
|
||||
blocks.append({"kind": "paragraph", "text": text})
|
||||
continue
|
||||
|
||||
# Plain paragraph — outside any exercise
|
||||
if text:
|
||||
blocks.append({"kind": "paragraph", "text": text})
|
||||
|
||||
return {
|
||||
"id": chapter_id,
|
||||
"number": number,
|
||||
"title": title,
|
||||
"blocks": blocks,
|
||||
}
|
||||
|
||||
|
||||
def assign_parts(chapters: list, part_files: "dict[int, list[int]]") -> None:
|
||||
"""Annotate chapters with part number based on TOC membership."""
|
||||
for part_num, chapter_nums in part_files.items():
|
||||
for ch in chapters:
|
||||
if ch["number"] in chapter_nums:
|
||||
ch["part"] = part_num
|
||||
for ch in chapters:
|
||||
ch.setdefault("part", None)
|
||||
|
||||
|
||||
def read_part_memberships() -> "dict[int, list[int]]":
|
||||
"""Derive part→chapter grouping from the OPF spine order."""
|
||||
opf = next(ROOT.glob("*.opf"), None)
|
||||
if opf is None:
|
||||
return {}
|
||||
soup = BeautifulSoup(opf.read_text(encoding="utf-8"), "xml")
|
||||
memberships: dict = {}
|
||||
current_part: "int | None" = None
|
||||
for item in soup.find_all("item"):
|
||||
href = item.get("href", "")
|
||||
m_part = re.match(r"part(\d+)\.xhtml", href)
|
||||
m_ch = re.match(r"ch(\d+)\.xhtml", href)
|
||||
if m_part:
|
||||
current_part = int(m_part.group(1))
|
||||
memberships.setdefault(current_part, [])
|
||||
elif m_ch and current_part is not None:
|
||||
memberships[current_part].append(int(m_ch.group(1)))
|
||||
# Manifest order tends to match spine order for this book; verify via spine just in case
|
||||
spine = soup.find("spine")
|
||||
if spine is not None:
|
||||
order = []
|
||||
for ref in spine.find_all("itemref"):
|
||||
idref = ref.get("idref")
|
||||
item = soup.find("item", attrs={"id": idref})
|
||||
if item is not None:
|
||||
order.append(item.get("href", ""))
|
||||
# Rebuild from spine order
|
||||
memberships = {}
|
||||
current_part = None
|
||||
for href in order:
|
||||
m_part = re.match(r"part(\d+)\.xhtml", href)
|
||||
m_ch = re.match(r"ch(\d+)\.xhtml", href)
|
||||
if m_part:
|
||||
current_part = int(m_part.group(1))
|
||||
memberships.setdefault(current_part, [])
|
||||
elif m_ch and current_part is not None:
|
||||
memberships[current_part].append(int(m_ch.group(1)))
|
||||
return memberships
|
||||
|
||||
|
||||
def main() -> None:
|
||||
chapter_files = sorted(
|
||||
ROOT.glob("ch*.xhtml"),
|
||||
key=lambda p: int(re.match(r"ch(\d+)", p.stem).group(1))
|
||||
)
|
||||
chapters = []
|
||||
for path in chapter_files:
|
||||
ch = parse_chapter(path)
|
||||
if ch:
|
||||
chapters.append(ch)
|
||||
|
||||
part_memberships = read_part_memberships()
|
||||
assign_parts(chapters, part_memberships)
|
||||
|
||||
out = {
|
||||
"chapters": chapters,
|
||||
"part_memberships": part_memberships,
|
||||
}
|
||||
OUT.write_text(json.dumps(out, ensure_ascii=False, indent=2))
|
||||
|
||||
# Summary
|
||||
ex_total = sum(1 for ch in chapters for b in ch["blocks"] if b["kind"] == "exercise")
|
||||
ex_with_prompts = sum(
|
||||
1 for ch in chapters for b in ch["blocks"]
|
||||
if b["kind"] == "exercise" and b["prompts"]
|
||||
)
|
||||
ex_with_images = sum(
|
||||
1 for ch in chapters for b in ch["blocks"]
|
||||
if b["kind"] == "exercise" and b["image_refs"]
|
||||
)
|
||||
ex_empty = sum(
|
||||
1 for ch in chapters for b in ch["blocks"]
|
||||
if b["kind"] == "exercise" and not b["prompts"] and not b["image_refs"]
|
||||
)
|
||||
para_total = sum(1 for ch in chapters for b in ch["blocks"] if b["kind"] == "paragraph")
|
||||
vocab_img_total = sum(1 for ch in chapters for b in ch["blocks"] if b["kind"] == "vocab_image")
|
||||
print(f"Chapters: {len(chapters)}")
|
||||
print(f"Exercises total: {ex_total}")
|
||||
print(f" with text prompts: {ex_with_prompts}")
|
||||
print(f" with image prompts: {ex_with_images}")
|
||||
print(f" empty: {ex_empty}")
|
||||
print(f"Paragraphs: {para_total}")
|
||||
print(f"Vocab images: {vocab_img_total}")
|
||||
print(f"Parts: {part_memberships}")
|
||||
print(f"Wrote {OUT}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
94
Conjuga/Scripts/textbook/extract_pdf_text.py
Normal file
94
Conjuga/Scripts/textbook/extract_pdf_text.py
Normal file
@@ -0,0 +1,94 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Extract clean text from the PDF source and map each PDF page to the
|
||||
book's printed page number.
|
||||
|
||||
Output: pdf_text.json
|
||||
{
|
||||
"pdfPageCount": 806,
|
||||
"bookPages": {
|
||||
"3": { "text": "...", "pdfIndex": 29 },
|
||||
"4": { ... },
|
||||
...
|
||||
},
|
||||
"unmapped": [list of pdfIndex values with no detectable book page number]
|
||||
}
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
import pypdf
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
PDF = next(
|
||||
Path(__file__).resolve().parents[3].glob("Complete Spanish Step-By-Step*.pdf"),
|
||||
None,
|
||||
)
|
||||
OUT = HERE / "pdf_text.json"
|
||||
|
||||
ROMAN_RE = re.compile(r"^[ivxlcdmIVXLCDM]+$")
|
||||
# Match a page number on its own line at top/bottom of the page.
|
||||
# The book uses Arabic numerals for main chapters (e.g., "3") and Roman for front matter.
|
||||
PAGE_NUM_LINE_RE = re.compile(r"^\s*(\d{1,4})\s*$", re.MULTILINE)
|
||||
|
||||
|
||||
def detect_book_page(text: str) -> "int | None":
|
||||
"""Find the printed page number from standalone page-number lines at the
|
||||
top or bottom of a page."""
|
||||
lines = [l.strip() for l in text.splitlines() if l.strip()]
|
||||
# Check first 2 lines and last 2 lines
|
||||
for candidate in lines[:2] + lines[-2:]:
|
||||
m = re.match(r"^(\d{1,4})$", candidate)
|
||||
if m:
|
||||
return int(m.group(1))
|
||||
return None
|
||||
|
||||
|
||||
def main() -> None:
|
||||
if PDF is None:
|
||||
print("No PDF found in project root")
|
||||
return
|
||||
|
||||
print(f"Reading {PDF.name}")
|
||||
reader = pypdf.PdfReader(str(PDF))
|
||||
pages = reader.pages
|
||||
print(f"PDF has {len(pages)} pages")
|
||||
|
||||
by_book_page: dict = {}
|
||||
unmapped: list = []
|
||||
last_seen: "int | None" = None
|
||||
missed_count = 0
|
||||
|
||||
for i, page in enumerate(pages):
|
||||
text = page.extract_text() or ""
|
||||
book_page = detect_book_page(text)
|
||||
|
||||
if book_page is None:
|
||||
# Carry forward sequence: if we saw page N last, assume N+1.
|
||||
if last_seen is not None:
|
||||
book_page = last_seen + 1
|
||||
missed_count += 1
|
||||
else:
|
||||
unmapped.append(i)
|
||||
continue
|
||||
last_seen = book_page
|
||||
# Strip the detected page number from text to clean the output
|
||||
cleaned = re.sub(r"(?m)^\s*\d{1,4}\s*$", "", text).strip()
|
||||
by_book_page[str(book_page)] = {
|
||||
"text": cleaned,
|
||||
"pdfIndex": i,
|
||||
}
|
||||
|
||||
out = {
|
||||
"pdfPageCount": len(pages),
|
||||
"bookPages": by_book_page,
|
||||
"unmapped": unmapped,
|
||||
"inferredPages": missed_count,
|
||||
}
|
||||
OUT.write_text(json.dumps(out, ensure_ascii=False))
|
||||
print(f"Mapped {len(by_book_page)} book pages; {missed_count} inferred; {len(unmapped)} unmapped")
|
||||
print(f"Wrote {OUT}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
252
Conjuga/Scripts/textbook/fix_vocab.py
Normal file
252
Conjuga/Scripts/textbook/fix_vocab.py
Normal file
@@ -0,0 +1,252 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Apply high-confidence auto-fixes from vocab_validation.json to vocab_cards.json.
|
||||
|
||||
Auto-fix rules (conservative):
|
||||
1. If a flagged word has exactly one suggestion AND that suggestion differs by
|
||||
<= 2 characters AND has the same starting letter (high-confidence character swap).
|
||||
2. If a card is detected as reversed (Spanish on EN side, English on ES side),
|
||||
swap front/back.
|
||||
|
||||
Cards that aren't auto-fixable end up in manual_review.json.
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import unicodedata
|
||||
from pathlib import Path
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
VOCAB = HERE / "vocab_cards.json"
|
||||
VALIDATION = HERE / "vocab_validation.json"
|
||||
OUT_VOCAB = HERE / "vocab_cards.json"
|
||||
OUT_REVIEW = HERE / "manual_review.json"
|
||||
OUT_QUARANTINE = HERE / "quarantined_cards.json"
|
||||
|
||||
|
||||
def _strip_accents(s: str) -> str:
|
||||
return "".join(c for c in unicodedata.normalize("NFD", s) if unicodedata.category(c) != "Mn")
|
||||
|
||||
|
||||
def _levenshtein(a: str, b: str) -> int:
|
||||
if a == b: return 0
|
||||
if not a: return len(b)
|
||||
if not b: return len(a)
|
||||
prev = list(range(len(b) + 1))
|
||||
for i, ca in enumerate(a, 1):
|
||||
curr = [i]
|
||||
for j, cb in enumerate(b, 1):
|
||||
cost = 0 if ca == cb else 1
|
||||
curr.append(min(prev[j] + 1, curr[j - 1] + 1, prev[j - 1] + cost))
|
||||
prev = curr
|
||||
return prev[-1]
|
||||
|
||||
|
||||
SPANISH_ACCENT_RE = re.compile(r"[áéíóúñüÁÉÍÓÚÑÜ¿¡]")
|
||||
SPANISH_ARTICLES = {"el", "la", "los", "las", "un", "una", "unos", "unas"}
|
||||
ENGLISH_STARTERS = {"the", "a", "an", "to", "my", "his", "her", "our", "their"}
|
||||
|
||||
|
||||
def language_score(s: str) -> "tuple[int, int]":
|
||||
"""Return (es_score, en_score) for a string."""
|
||||
es = 0
|
||||
en = 0
|
||||
if SPANISH_ACCENT_RE.search(s):
|
||||
es += 3
|
||||
words = s.lower().split()
|
||||
if not words:
|
||||
return (es, en)
|
||||
first = words[0].strip(",.;:")
|
||||
if first in SPANISH_ARTICLES:
|
||||
es += 2
|
||||
if first in ENGLISH_STARTERS:
|
||||
en += 2
|
||||
# Spanish-likely endings on later words
|
||||
for w in words:
|
||||
w = w.strip(",.;:")
|
||||
if not w: continue
|
||||
if w.endswith(("ción", "sión", "dad", "tud")):
|
||||
es += 1
|
||||
if w.endswith(("ing", "tion", "ness", "ment", "able", "ly")):
|
||||
en += 1
|
||||
return (es, en)
|
||||
|
||||
|
||||
def is_reversed(front: str, back: str) -> bool:
|
||||
"""True when front looks like English and back looks like Spanish (i.e. swapped)."""
|
||||
fes, fen = language_score(front)
|
||||
bes, ben = language_score(back)
|
||||
# Front English-leaning AND back Spanish-leaning
|
||||
return fen > fes and bes > ben
|
||||
|
||||
|
||||
def best_replacement(word: str, suggestions: list) -> "str | None":
|
||||
"""Pick the one safe correction, or None to leave it alone."""
|
||||
if not suggestions:
|
||||
return None
|
||||
# Prefer suggestions that share the same first letter
|
||||
same_initial = [s for s in suggestions if s and word and s[0].lower() == word[0].lower()]
|
||||
candidates = same_initial or suggestions
|
||||
# Single best: short edit distance
|
||||
best = None
|
||||
best_d = 99
|
||||
for s in candidates:
|
||||
d = _levenshtein(word.lower(), s.lower())
|
||||
# Don't apply if the "fix" changes too much
|
||||
if d == 0:
|
||||
continue
|
||||
if d > 2:
|
||||
continue
|
||||
if d < best_d:
|
||||
best = s
|
||||
best_d = d
|
||||
return best
|
||||
|
||||
|
||||
def side_language_match(text: str, expected_side: str) -> bool:
|
||||
"""Return True when `text` looks like the expected language (es/en).
|
||||
Guards against applying Spanish spell-fix to English words on a mis-paired card.
|
||||
"""
|
||||
es, en = language_score(text)
|
||||
if expected_side == "es":
|
||||
return es > en # require clear Spanish signal
|
||||
if expected_side == "en":
|
||||
return en >= es # allow equal when text has no strong signal (common for English)
|
||||
return False
|
||||
|
||||
|
||||
def apply_word_fixes(text: str, bad_words: list, expected_side: str) -> "tuple[str, list]":
|
||||
"""Apply word-level corrections inside a string. Skips fixes entirely when
|
||||
the side's actual language doesn't match the dictionary used, to avoid
|
||||
corrupting mis-paired cards."""
|
||||
if not side_language_match(text, expected_side):
|
||||
return (text, [])
|
||||
|
||||
new_text = text
|
||||
applied = []
|
||||
for bw in bad_words:
|
||||
word = bw["word"]
|
||||
sugg = bw["suggestions"]
|
||||
replacement = best_replacement(word, sugg)
|
||||
if replacement is None:
|
||||
continue
|
||||
# Match standalone word including the (possibly-omitted) trailing period:
|
||||
# `Uds` in the text should be replaced with `Uds.` even when adjacent to `.`.
|
||||
escaped = re.escape(word)
|
||||
# Allow an optional existing period that we'd otherwise duplicate.
|
||||
pattern = re.compile(rf"(?<![A-Za-zÁ-ú]){escaped}\.?(?![A-Za-zÁ-ú])")
|
||||
if pattern.search(new_text):
|
||||
new_text = pattern.sub(replacement, new_text, count=1)
|
||||
applied.append({"from": word, "to": replacement})
|
||||
return (new_text, applied)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
vocab_data = json.loads(VOCAB.read_text(encoding="utf-8"))
|
||||
val_data = json.loads(VALIDATION.read_text(encoding="utf-8"))
|
||||
|
||||
# Index validation by (chapter, front, back, sourceImage) for lookup
|
||||
val_index: dict = {}
|
||||
for f in val_data["flags"]:
|
||||
key = (f["chapter"], f["front"], f["back"], f["sourceImage"])
|
||||
val_index[key] = f
|
||||
|
||||
# Walk the cards in place
|
||||
auto_fixed_word = 0
|
||||
auto_swapped = 0
|
||||
quarantined = 0
|
||||
manual_review_cards = []
|
||||
quarantined_cards = []
|
||||
|
||||
for ch in vocab_data["chapters"]:
|
||||
kept_cards = []
|
||||
for card in ch["cards"]:
|
||||
key = (ch["chapter"], card["front"], card["back"], card.get("sourceImage", ""))
|
||||
flag = val_index.get(key)
|
||||
|
||||
# 1) Reversal swap (apply even when not flagged)
|
||||
if is_reversed(card["front"], card["back"]):
|
||||
card["front"], card["back"] = card["back"], card["front"]
|
||||
auto_swapped += 1
|
||||
# Re-key for any further validation lookup (no-op here)
|
||||
|
||||
if flag is None:
|
||||
kept_cards.append(card)
|
||||
continue
|
||||
|
||||
# Quarantine only clear mis-pairs: both sides EXPLICITLY the wrong
|
||||
# language (both Spanish or both English). "unknown" sides stay —
|
||||
# the bounding-box pipeline already handled orientation correctly
|
||||
# and many valid pairs lack the article/accent markers we classify on.
|
||||
fes, fen = language_score(card["front"])
|
||||
bes, ben = language_score(card["back"])
|
||||
front_lang = "es" if fes > fen else ("en" if fen > fes else "unknown")
|
||||
back_lang = "es" if bes > ben else ("en" if ben > bes else "unknown")
|
||||
bothSameLang = (front_lang == "es" and back_lang == "es") or (front_lang == "en" and back_lang == "en")
|
||||
reversed_pair = front_lang == "en" and back_lang == "es"
|
||||
if bothSameLang or reversed_pair:
|
||||
quarantined_cards.append({
|
||||
"chapter": ch["chapter"],
|
||||
"front": card["front"],
|
||||
"back": card["back"],
|
||||
"sourceImage": card.get("sourceImage", ""),
|
||||
"reason": f"language-mismatch front={front_lang} back={back_lang}",
|
||||
})
|
||||
quarantined += 1
|
||||
continue
|
||||
|
||||
# 2) Word-level fixes (language-aware)
|
||||
new_front, applied_front = apply_word_fixes(card["front"], flag["badFront"], "es")
|
||||
new_back, applied_back = apply_word_fixes(card["back"], flag["badBack"], "en")
|
||||
card["front"] = new_front
|
||||
card["back"] = new_back
|
||||
auto_fixed_word += len(applied_front) + len(applied_back)
|
||||
|
||||
# If after auto-fix there are STILL flagged words with no
|
||||
# confident replacement, flag for manual review.
|
||||
unresolved_front = [
|
||||
bw for bw in flag["badFront"]
|
||||
if not any(a["from"] == bw["word"] for a in applied_front)
|
||||
and best_replacement(bw["word"], bw["suggestions"]) is None
|
||||
]
|
||||
unresolved_back = [
|
||||
bw for bw in flag["badBack"]
|
||||
if not any(a["from"] == bw["word"] for a in applied_back)
|
||||
and best_replacement(bw["word"], bw["suggestions"]) is None
|
||||
]
|
||||
if unresolved_front or unresolved_back:
|
||||
manual_review_cards.append({
|
||||
"chapter": ch["chapter"],
|
||||
"front": card["front"],
|
||||
"back": card["back"],
|
||||
"sourceImage": card.get("sourceImage", ""),
|
||||
"unresolvedFront": unresolved_front,
|
||||
"unresolvedBack": unresolved_back,
|
||||
})
|
||||
kept_cards.append(card)
|
||||
|
||||
ch["cards"] = kept_cards
|
||||
|
||||
OUT_VOCAB.write_text(json.dumps(vocab_data, ensure_ascii=False, indent=2))
|
||||
OUT_REVIEW.write_text(json.dumps({
|
||||
"totalManualReview": len(manual_review_cards),
|
||||
"cards": manual_review_cards,
|
||||
}, ensure_ascii=False, indent=2))
|
||||
|
||||
OUT_QUARANTINE.write_text(json.dumps({
|
||||
"totalQuarantined": len(quarantined_cards),
|
||||
"cards": quarantined_cards,
|
||||
}, ensure_ascii=False, indent=2))
|
||||
|
||||
total_cards = sum(len(c["cards"]) for c in vocab_data["chapters"])
|
||||
print(f"Active cards (after quarantine): {total_cards}")
|
||||
print(f"Auto-swapped (reversed): {auto_swapped}")
|
||||
print(f"Auto-fixed words: {auto_fixed_word}")
|
||||
print(f"Quarantined (mis-paired): {quarantined}")
|
||||
print(f"Cards needing manual review: {len(manual_review_cards)}")
|
||||
print(f"Wrote {OUT_VOCAB}")
|
||||
print(f"Wrote {OUT_REVIEW}")
|
||||
print(f"Wrote {OUT_QUARANTINE}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
147
Conjuga/Scripts/textbook/integrate_repaired.py
Normal file
147
Conjuga/Scripts/textbook/integrate_repaired.py
Normal file
@@ -0,0 +1,147 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Merge repaired_cards.json into vocab_cards.json.
|
||||
|
||||
Rules:
|
||||
1. New pairs are added to their chapter's deck if they don't duplicate an existing pair.
|
||||
2. Duplicate detection uses normalize(front)+normalize(back).
|
||||
3. Pairs whose back side starts with a Spanish-article or front side starts
|
||||
with an English article are dropped (pairer got orientation wrong).
|
||||
4. Emits integrate_report.json with counts.
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import unicodedata
|
||||
from pathlib import Path
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
VOCAB = HERE / "vocab_cards.json"
|
||||
REPAIRED = HERE / "repaired_cards.json"
|
||||
QUARANTINED = HERE / "quarantined_cards.json"
|
||||
OUT = HERE / "vocab_cards.json"
|
||||
REPORT = HERE / "integrate_report.json"
|
||||
|
||||
|
||||
def _strip_accents(s: str) -> str:
|
||||
return "".join(c for c in unicodedata.normalize("NFD", s) if unicodedata.category(c) != "Mn")
|
||||
|
||||
|
||||
def norm(s: str) -> str:
|
||||
return _strip_accents(s.lower()).strip()
|
||||
|
||||
|
||||
SPANISH_ACCENT_RE = re.compile(r"[áéíóúñüÁÉÍÓÚÑÜ¿¡]")
|
||||
SPANISH_ARTICLES = {"el", "la", "los", "las", "un", "una", "unos", "unas"}
|
||||
ENGLISH_STARTERS = {"the", "a", "an", "to", "my", "his", "her", "our", "their"}
|
||||
|
||||
|
||||
def looks_swapped(front: str, back: str) -> bool:
|
||||
"""True if front looks English and back looks Spanish (pair should be swapped)."""
|
||||
fl = front.lower().split()
|
||||
bl = back.lower().split()
|
||||
if not fl or not bl:
|
||||
return False
|
||||
f_first = fl[0].strip(",.;:")
|
||||
b_first = bl[0].strip(",.;:")
|
||||
front_is_en = f_first in ENGLISH_STARTERS
|
||||
back_is_es = (
|
||||
SPANISH_ACCENT_RE.search(back) is not None
|
||||
or b_first in SPANISH_ARTICLES
|
||||
)
|
||||
return front_is_en and back_is_es
|
||||
|
||||
|
||||
def looks_good(pair: dict) -> bool:
|
||||
"""Basic sanity filter on a repaired pair before it enters the deck."""
|
||||
es = pair["es"].strip()
|
||||
en = pair["en"].strip()
|
||||
if not es or not en: return False
|
||||
if len(es) < 2 or len(en) < 2: return False
|
||||
# Drop if both sides obviously same language (neither has clear orientation)
|
||||
es_has_accent = SPANISH_ACCENT_RE.search(es) is not None
|
||||
en_has_accent = SPANISH_ACCENT_RE.search(en) is not None
|
||||
if en_has_accent and not es_has_accent:
|
||||
# The "en" side has accents — likely swapped
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def main() -> None:
|
||||
vocab = json.loads(VOCAB.read_text(encoding="utf-8"))
|
||||
repaired = json.loads(REPAIRED.read_text(encoding="utf-8"))
|
||||
quarantined = json.loads(QUARANTINED.read_text(encoding="utf-8"))
|
||||
|
||||
# Map image → chapter (from the quarantine list — all images here belong to the
|
||||
# chapter they were quarantined from).
|
||||
image_chapter: dict = {}
|
||||
for c in quarantined["cards"]:
|
||||
image_chapter[c["sourceImage"]] = c["chapter"]
|
||||
|
||||
# Build existing key set
|
||||
existing_keys = set()
|
||||
chapter_map: dict = {c["chapter"]: c for c in vocab["chapters"]}
|
||||
for c in vocab["chapters"]:
|
||||
for card in c["cards"]:
|
||||
existing_keys.add((c["chapter"], norm(card["front"]), norm(card["back"])))
|
||||
|
||||
added_per_image: dict = {}
|
||||
dropped_swapped = 0
|
||||
dropped_sanity = 0
|
||||
dropped_dup = 0
|
||||
|
||||
for image_name, data in repaired["byImage"].items():
|
||||
ch_num = image_chapter.get(image_name)
|
||||
if ch_num is None:
|
||||
# Image not in quarantine list (shouldn't happen, but bail)
|
||||
continue
|
||||
deck = chapter_map.setdefault(ch_num, {"chapter": ch_num, "cards": []})
|
||||
added = 0
|
||||
for p in data.get("pairs", []):
|
||||
es = p["es"].strip()
|
||||
en = p["en"].strip()
|
||||
if looks_swapped(es, en):
|
||||
es, en = en, es
|
||||
pair = {"es": es, "en": en}
|
||||
if not looks_good(pair):
|
||||
dropped_sanity += 1
|
||||
continue
|
||||
key = (ch_num, norm(pair["es"]), norm(pair["en"]))
|
||||
if key in existing_keys:
|
||||
dropped_dup += 1
|
||||
continue
|
||||
existing_keys.add(key)
|
||||
card = {
|
||||
"front": pair["es"],
|
||||
"back": pair["en"],
|
||||
"chapter": ch_num,
|
||||
"chapterTitle": "",
|
||||
"section": "",
|
||||
"sourceImage": image_name,
|
||||
}
|
||||
deck["cards"].append(card)
|
||||
added += 1
|
||||
if added:
|
||||
added_per_image[image_name] = added
|
||||
|
||||
# If any new chapter was created, ensure ordered insertion
|
||||
vocab["chapters"] = sorted(chapter_map.values(), key=lambda c: c["chapter"])
|
||||
OUT.write_text(json.dumps(vocab, ensure_ascii=False, indent=2))
|
||||
|
||||
total_added = sum(added_per_image.values())
|
||||
report = {
|
||||
"totalRepairedInput": repaired["totalPairs"],
|
||||
"added": total_added,
|
||||
"dropped_duplicate": dropped_dup,
|
||||
"dropped_sanity": dropped_sanity,
|
||||
"addedPerImage": added_per_image,
|
||||
}
|
||||
REPORT.write_text(json.dumps(report, ensure_ascii=False, indent=2))
|
||||
print(f"Repaired pairs in: {repaired['totalPairs']}")
|
||||
print(f"Added to deck: {total_added}")
|
||||
print(f"Dropped as duplicate: {dropped_dup}")
|
||||
print(f"Dropped as swapped/bad: {dropped_sanity}")
|
||||
print(f"Wrote {OUT}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
435
Conjuga/Scripts/textbook/merge_pdf_into_book.py
Normal file
435
Conjuga/Scripts/textbook/merge_pdf_into_book.py
Normal file
@@ -0,0 +1,435 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Second-pass extractor: use PDF OCR (from ocr_pdf.swift) as a supplementary
|
||||
source of clean text, then re-build book.json with PDF-derived content where it
|
||||
improves on the EPUB's image-based extraction.
|
||||
|
||||
Inputs:
|
||||
chapters.json — EPUB structural extraction (narrative text + exercise prompts + image refs)
|
||||
answers.json — EPUB answer key
|
||||
ocr.json — EPUB image OCR (first pass)
|
||||
pdf_ocr.json — PDF page-level OCR (this pass, higher DPI + cleaner)
|
||||
|
||||
Outputs:
|
||||
book.json — merged book used by the app
|
||||
vocab_cards.json — derived vocabulary flashcards
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
sys.path.insert(0, str(HERE))
|
||||
from build_book import ( # reuse the helpers defined in build_book.py
|
||||
COURSE_NAME,
|
||||
build_vocab_cards_for_block,
|
||||
clean_instruction,
|
||||
classify_line,
|
||||
load,
|
||||
)
|
||||
|
||||
CHAPTERS_JSON = HERE / "chapters.json"
|
||||
ANSWERS_JSON = HERE / "answers.json"
|
||||
OCR_JSON = HERE / "ocr.json"
|
||||
PDF_OCR_JSON = HERE / "pdf_ocr.json"
|
||||
PAIRED_VOCAB_JSON = HERE / "paired_vocab.json" # bounding-box pairs (preferred)
|
||||
OUT_BOOK = HERE / "book.json"
|
||||
OUT_VOCAB = HERE / "vocab_cards.json"
|
||||
|
||||
IMAGE_NAME_RE = re.compile(r"^f(\d{4})-(\d{2})\.jpg$")
|
||||
|
||||
|
||||
def extract_book_page(image_src: str) -> "int | None":
|
||||
m = IMAGE_NAME_RE.match(image_src)
|
||||
return int(m.group(1)) if m else None
|
||||
|
||||
|
||||
def build_pdf_page_index(pdf_ocr: dict) -> "dict[int, dict]":
|
||||
"""Map bookPage → {lines, confidence, pdfIndex}.
|
||||
|
||||
Strategy: use chapter-start alignments as anchors. For each chapter N,
|
||||
anchor[N] = (pdf_idx_where_chapter_starts, book_page_where_chapter_starts).
|
||||
Between anchors we interpolate page-by-page (pages run sequentially within
|
||||
a chapter in this textbook's layout).
|
||||
"""
|
||||
pages: "dict[int, dict]" = {}
|
||||
sorted_keys = sorted(pdf_ocr.keys(), key=lambda k: int(k))
|
||||
|
||||
# --- Detect chapter starts in the PDF OCR ---
|
||||
pdf_ch_start: "dict[int, int]" = {}
|
||||
for k in sorted_keys:
|
||||
entry = pdf_ocr[k]
|
||||
lines = entry.get("lines", [])
|
||||
if len(lines) < 2:
|
||||
continue
|
||||
first = lines[0].strip()
|
||||
second = lines[1].strip()
|
||||
if first.isdigit() and 1 <= int(first) <= 30 and len(second) > 5 and second[0:1].isupper():
|
||||
ch = int(first)
|
||||
if ch not in pdf_ch_start:
|
||||
pdf_ch_start[ch] = int(k)
|
||||
|
||||
# --- Load EPUB's authoritative book-page starts ---
|
||||
import re as _re
|
||||
from bs4 import BeautifulSoup as _BS
|
||||
epub_root = HERE.parents[2] / "epub_extract" / "OEBPS"
|
||||
book_ch_start: "dict[int, int]" = {}
|
||||
for ch in sorted(pdf_ch_start.keys()):
|
||||
p = epub_root / f"ch{ch}.xhtml"
|
||||
if not p.exists():
|
||||
continue
|
||||
soup = _BS(p.read_text(encoding="utf-8"), "lxml")
|
||||
for span in soup.find_all(True):
|
||||
id_ = span.get("id", "") or ""
|
||||
m = _re.match(r"page_(\d+)$", id_)
|
||||
if m:
|
||||
book_ch_start[ch] = int(m.group(1))
|
||||
break
|
||||
|
||||
# Build per-chapter (pdf_anchor, book_anchor, next_pdf_anchor) intervals
|
||||
anchors = [] # list of (ch, pdf_start, book_start)
|
||||
for ch in sorted(pdf_ch_start.keys()):
|
||||
if ch in book_ch_start:
|
||||
anchors.append((ch, pdf_ch_start[ch], book_ch_start[ch]))
|
||||
|
||||
for i, (ch, pdf_s, book_s) in enumerate(anchors):
|
||||
next_pdf = anchors[i + 1][1] if i + 1 < len(anchors) else pdf_s + 50
|
||||
# Interpolate book page for each pdf index in [pdf_s, next_pdf)
|
||||
for pdf_idx in range(pdf_s, next_pdf):
|
||||
book_page = book_s + (pdf_idx - pdf_s)
|
||||
entry = pdf_ocr.get(str(pdf_idx))
|
||||
if entry is None:
|
||||
continue
|
||||
if book_page in pages:
|
||||
continue
|
||||
pages[book_page] = {
|
||||
"lines": entry["lines"],
|
||||
"confidence": entry.get("confidence", 0),
|
||||
"pdfIndex": pdf_idx,
|
||||
}
|
||||
return pages
|
||||
|
||||
|
||||
def merge_ocr(epub_lines: list, pdf_lines: list) -> list:
|
||||
"""EPUB per-image OCR is our primary (targeted, no prose bleed). PDF
|
||||
page-level OCR is only used when EPUB is missing. Per-line accent repair
|
||||
is handled separately via `repair_accents_from_pdf`.
|
||||
"""
|
||||
if epub_lines:
|
||||
return epub_lines
|
||||
return pdf_lines
|
||||
|
||||
|
||||
import unicodedata as _u
|
||||
|
||||
def _strip_accents(s: str) -> str:
|
||||
return "".join(c for c in _u.normalize("NFD", s) if _u.category(c) != "Mn")
|
||||
|
||||
|
||||
def _levenshtein(a: str, b: str) -> int:
|
||||
if a == b: return 0
|
||||
if not a: return len(b)
|
||||
if not b: return len(a)
|
||||
prev = list(range(len(b) + 1))
|
||||
for i, ca in enumerate(a, 1):
|
||||
curr = [i]
|
||||
for j, cb in enumerate(b, 1):
|
||||
cost = 0 if ca == cb else 1
|
||||
curr.append(min(prev[j] + 1, curr[j - 1] + 1, prev[j - 1] + cost))
|
||||
prev = curr
|
||||
return prev[-1]
|
||||
|
||||
|
||||
def repair_accents_from_pdf(epub_lines: list, pdf_page_lines: list) -> "tuple[list, int]":
|
||||
"""For each EPUB OCR line, find a near-match in the PDF page OCR and
|
||||
prefer the PDF version. Repairs include:
|
||||
1. exact accent/case differences (e.g. 'iglesia' vs 'Iglesia')
|
||||
2. single-character OCR errors (e.g. 'the hrother' -> 'the brother')
|
||||
3. two-character OCR errors when the target is long enough
|
||||
"""
|
||||
if not epub_lines or not pdf_page_lines:
|
||||
return (epub_lines, 0)
|
||||
# Pre-normalize PDF lines for matching
|
||||
pdf_cleaned = [p.strip() for p in pdf_page_lines if p.strip()]
|
||||
pdf_by_stripped: dict = {}
|
||||
for p in pdf_cleaned:
|
||||
key = _strip_accents(p.lower())
|
||||
pdf_by_stripped.setdefault(key, p)
|
||||
|
||||
out: list = []
|
||||
repairs = 0
|
||||
for e in epub_lines:
|
||||
e_stripped = e.strip()
|
||||
e_key = _strip_accents(e_stripped.lower())
|
||||
# Pass 1: exact accent-only difference
|
||||
if e_key and e_key in pdf_by_stripped and pdf_by_stripped[e_key] != e_stripped:
|
||||
out.append(pdf_by_stripped[e_key])
|
||||
repairs += 1
|
||||
continue
|
||||
# Pass 2: fuzzy — find best PDF line within edit distance 1 or 2
|
||||
if len(e_key) >= 4:
|
||||
max_distance = 1 if len(e_key) < 10 else 2
|
||||
best_match = None
|
||||
best_d = max_distance + 1
|
||||
for p in pdf_cleaned:
|
||||
p_key = _strip_accents(p.lower())
|
||||
# Only match lines of similar length
|
||||
if abs(len(p_key) - len(e_key)) > max_distance:
|
||||
continue
|
||||
d = _levenshtein(e_key, p_key)
|
||||
if d < best_d:
|
||||
best_d = d
|
||||
best_match = p
|
||||
if d == 0:
|
||||
break
|
||||
if best_match and best_match != e_stripped and best_d <= max_distance:
|
||||
out.append(best_match)
|
||||
repairs += 1
|
||||
continue
|
||||
out.append(e)
|
||||
return (out, repairs)
|
||||
|
||||
|
||||
def vocab_lines_from_pdf_page(
|
||||
pdf_page_entry: dict,
|
||||
epub_narrative_lines: set
|
||||
) -> list:
|
||||
"""Extract likely vocab-table lines from a PDF page's OCR by filtering out
|
||||
narrative-looking lines (long sentences) and already-known EPUB content."""
|
||||
lines = pdf_page_entry.get("lines", [])
|
||||
out: list = []
|
||||
for raw in lines:
|
||||
line = raw.strip()
|
||||
if not line:
|
||||
continue
|
||||
# Skip lines that look like body prose (too long)
|
||||
if len(line) > 80:
|
||||
continue
|
||||
# Skip narrative we already captured in the EPUB
|
||||
if line in epub_narrative_lines:
|
||||
continue
|
||||
# Skip page-number-only lines
|
||||
if re.fullmatch(r"\d{1,4}", line):
|
||||
continue
|
||||
# Skip standalone chapter headers (e.g. "Nouns, Articles, and Adjectives")
|
||||
out.append(line)
|
||||
return out
|
||||
|
||||
|
||||
def main() -> None:
|
||||
chapters_data = load(CHAPTERS_JSON)
|
||||
answers = load(ANSWERS_JSON)["answers"]
|
||||
epub_ocr = load(OCR_JSON)
|
||||
pdf_ocr_raw = load(PDF_OCR_JSON) if PDF_OCR_JSON.exists() else {}
|
||||
pdf_pages = build_pdf_page_index(pdf_ocr_raw) if pdf_ocr_raw else {}
|
||||
paired_vocab = load(PAIRED_VOCAB_JSON) if PAIRED_VOCAB_JSON.exists() else {}
|
||||
print(f"Mapped {len(pdf_pages)} PDF pages to book page numbers")
|
||||
print(f"Loaded bounding-box pairs for {len(paired_vocab)} vocab images")
|
||||
|
||||
# Build a global set of EPUB narrative lines (for subtraction when pulling vocab)
|
||||
narrative_set = set()
|
||||
for ch in chapters_data["chapters"]:
|
||||
for b in ch["blocks"]:
|
||||
if b["kind"] == "paragraph" and b.get("text"):
|
||||
narrative_set.add(b["text"].strip())
|
||||
|
||||
book_chapters = []
|
||||
all_vocab_cards = []
|
||||
pdf_hits = 0
|
||||
pdf_misses = 0
|
||||
merged_pages = 0
|
||||
|
||||
for ch in chapters_data["chapters"]:
|
||||
out_blocks = []
|
||||
current_section_title = ch["title"]
|
||||
|
||||
for bi, block in enumerate(ch["blocks"]):
|
||||
k = block["kind"]
|
||||
|
||||
if k == "heading":
|
||||
current_section_title = block["text"]
|
||||
out_blocks.append(block)
|
||||
continue
|
||||
|
||||
if k == "paragraph":
|
||||
out_blocks.append(block)
|
||||
continue
|
||||
|
||||
if k == "key_vocab_header":
|
||||
out_blocks.append(block)
|
||||
continue
|
||||
|
||||
if k == "vocab_image":
|
||||
src = block["src"]
|
||||
epub_entry = epub_ocr.get(src)
|
||||
epub_lines = epub_entry.get("lines", []) if epub_entry else []
|
||||
epub_conf = epub_entry.get("confidence", 0.0) if epub_entry else 0.0
|
||||
|
||||
book_page = extract_book_page(src)
|
||||
pdf_entry = pdf_pages.get(book_page) if book_page else None
|
||||
pdf_lines = pdf_entry["lines"] if pdf_entry else []
|
||||
|
||||
# Primary: EPUB per-image OCR. Supplementary: PDF page OCR
|
||||
# used only for accent/diacritic repair where keys match.
|
||||
if pdf_lines:
|
||||
pdf_hits += 1
|
||||
else:
|
||||
pdf_misses += 1
|
||||
repaired_lines, repairs = repair_accents_from_pdf(epub_lines, pdf_lines)
|
||||
merged_lines = repaired_lines if repaired_lines else pdf_lines
|
||||
merged_conf = max(epub_conf, pdf_entry.get("confidence", 0) if pdf_entry else 0.0)
|
||||
if repairs > 0:
|
||||
merged_pages += 1
|
||||
|
||||
# Prefer bounding-box pairs (from paired_vocab.json) when
|
||||
# present. Fall back to the block-alternation heuristic.
|
||||
bbox = paired_vocab.get(src, {})
|
||||
bbox_pairs = bbox.get("pairs", []) if isinstance(bbox, dict) else []
|
||||
heuristic = build_vocab_cards_for_block(
|
||||
{"src": src},
|
||||
{"lines": merged_lines, "confidence": merged_conf},
|
||||
ch, current_section_title, bi
|
||||
)
|
||||
|
||||
if bbox_pairs:
|
||||
cards_for_block = [
|
||||
{"front": p["es"], "back": p["en"]}
|
||||
for p in bbox_pairs
|
||||
if p.get("es") and p.get("en")
|
||||
]
|
||||
# Also feed the flashcard deck
|
||||
for p in bbox_pairs:
|
||||
if p.get("es") and p.get("en"):
|
||||
all_vocab_cards.append({
|
||||
"front": p["es"],
|
||||
"back": p["en"],
|
||||
"chapter": ch["number"],
|
||||
"chapterTitle": ch["title"],
|
||||
"section": current_section_title,
|
||||
"sourceImage": src,
|
||||
})
|
||||
pair_source = "bbox"
|
||||
else:
|
||||
cards_for_block = [{"front": c["front"], "back": c["back"]} for c in heuristic]
|
||||
all_vocab_cards.extend(heuristic)
|
||||
pair_source = "heuristic"
|
||||
|
||||
out_blocks.append({
|
||||
"kind": "vocab_table",
|
||||
"sourceImage": src,
|
||||
"ocrLines": merged_lines,
|
||||
"ocrConfidence": merged_conf,
|
||||
"cardCount": len(cards_for_block),
|
||||
"cards": cards_for_block,
|
||||
"columnCount": bbox.get("columnCount", 2) if isinstance(bbox, dict) else 2,
|
||||
"source": pair_source,
|
||||
"bookPage": book_page,
|
||||
"repairs": repairs,
|
||||
})
|
||||
continue
|
||||
|
||||
if k == "exercise":
|
||||
ans = answers.get(block["id"])
|
||||
# EPUB image OCR (if any image refs)
|
||||
image_ocr_lines: list = []
|
||||
for src in block.get("image_refs", []):
|
||||
ee = epub_ocr.get(src)
|
||||
if ee:
|
||||
image_ocr_lines.extend(ee.get("lines", []))
|
||||
# Add PDF-page OCR for that page if available
|
||||
bp = extract_book_page(src)
|
||||
if bp and pdf_pages.get(bp):
|
||||
# Only add lines not already present from EPUB OCR
|
||||
pdf_lines = pdf_pages[bp]["lines"]
|
||||
for line in pdf_lines:
|
||||
line = line.strip()
|
||||
if not line or line in image_ocr_lines:
|
||||
continue
|
||||
if line in narrative_set:
|
||||
continue
|
||||
image_ocr_lines.append(line)
|
||||
|
||||
prompts = [p for p in block.get("prompts", []) if p.strip()]
|
||||
extras = [e for e in block.get("extra", []) if e.strip()]
|
||||
if not prompts and image_ocr_lines:
|
||||
# Extract numbered lines from OCR
|
||||
for line in image_ocr_lines:
|
||||
m = re.match(r"^(\d+)[.)]\s*(.+)", line.strip())
|
||||
if m:
|
||||
prompts.append(f"{m.group(1)}. {m.group(2)}")
|
||||
|
||||
sub = ans["subparts"] if ans else []
|
||||
answer_items = []
|
||||
for sp in sub:
|
||||
for it in sp["items"]:
|
||||
answer_items.append({
|
||||
"label": sp["label"],
|
||||
"number": it["number"],
|
||||
"answer": it["answer"],
|
||||
"alternates": it["alternates"],
|
||||
})
|
||||
|
||||
out_blocks.append({
|
||||
"kind": "exercise",
|
||||
"id": block["id"],
|
||||
"ansAnchor": block.get("ans_anchor", ""),
|
||||
"instruction": clean_instruction(block.get("instruction", "")),
|
||||
"extra": extras,
|
||||
"prompts": prompts,
|
||||
"ocrLines": image_ocr_lines,
|
||||
"freeform": ans["freeform"] if ans else False,
|
||||
"answerItems": answer_items,
|
||||
"answerRaw": ans["raw"] if ans else "",
|
||||
"answerSubparts": sub,
|
||||
})
|
||||
continue
|
||||
|
||||
out_blocks.append(block)
|
||||
|
||||
book_chapters.append({
|
||||
"id": ch["id"],
|
||||
"number": ch["number"],
|
||||
"title": ch["title"],
|
||||
"part": ch.get("part"),
|
||||
"blocks": out_blocks,
|
||||
})
|
||||
|
||||
book = {
|
||||
"courseName": COURSE_NAME,
|
||||
"totalChapters": len(book_chapters),
|
||||
"totalExercises": sum(1 for ch in book_chapters for b in ch["blocks"] if b["kind"] == "exercise"),
|
||||
"totalVocabTables": sum(1 for ch in book_chapters for b in ch["blocks"] if b["kind"] == "vocab_table"),
|
||||
"totalVocabCards": len(all_vocab_cards),
|
||||
"parts": chapters_data.get("part_memberships", {}),
|
||||
"chapters": book_chapters,
|
||||
"sources": {
|
||||
"epub_images_ocr": bool(epub_ocr),
|
||||
"pdf_pages_ocr": bool(pdf_ocr_raw),
|
||||
"pdf_pages_mapped": len(pdf_pages),
|
||||
},
|
||||
}
|
||||
OUT_BOOK.write_text(json.dumps(book, ensure_ascii=False))
|
||||
|
||||
vocab_by_chapter: dict = {}
|
||||
for card in all_vocab_cards:
|
||||
vocab_by_chapter.setdefault(card["chapter"], []).append(card)
|
||||
OUT_VOCAB.write_text(json.dumps({
|
||||
"courseName": COURSE_NAME,
|
||||
"chapters": [
|
||||
{"chapter": n, "cards": cs}
|
||||
for n, cs in sorted(vocab_by_chapter.items())
|
||||
],
|
||||
}, ensure_ascii=False, indent=2))
|
||||
|
||||
print(f"Wrote {OUT_BOOK}")
|
||||
print(f"Wrote {OUT_VOCAB}")
|
||||
print(f"Chapters: {book['totalChapters']}")
|
||||
print(f"Exercises: {book['totalExercises']}")
|
||||
print(f"Vocab tables: {book['totalVocabTables']}")
|
||||
print(f"Vocab cards (derived): {book['totalVocabCards']}")
|
||||
print(f"PDF hits vs misses: {pdf_hits} / {pdf_misses}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
232
Conjuga/Scripts/textbook/ocr_all_vocab.swift
Normal file
232
Conjuga/Scripts/textbook/ocr_all_vocab.swift
Normal file
@@ -0,0 +1,232 @@
|
||||
#!/usr/bin/env swift
|
||||
// Bounding-box OCR over every vocab image, producing Spanish→English pairs.
|
||||
// Much higher accuracy than the flat-OCR block-alternation heuristic because
|
||||
// we use each recognized line's position on the page: rows are clustered by
|
||||
// Y-coordinate and cells within a row are split by the biggest X gap.
|
||||
//
|
||||
// Usage: swift ocr_all_vocab.swift <image_list.json> <oebps_dir> <output.json>
|
||||
|
||||
import Foundation
|
||||
import Vision
|
||||
import AppKit
|
||||
|
||||
guard CommandLine.arguments.count >= 4 else {
|
||||
print("Usage: swift ocr_all_vocab.swift <image_list.json> <oebps_dir> <output.json>")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
let imageListURL = URL(fileURLWithPath: CommandLine.arguments[1])
|
||||
let oebpsDir = URL(fileURLWithPath: CommandLine.arguments[2])
|
||||
let outputURL = URL(fileURLWithPath: CommandLine.arguments[3])
|
||||
|
||||
guard let listData = try? Data(contentsOf: imageListURL),
|
||||
let imageNames = try? JSONDecoder().decode([String].self, from: listData) else {
|
||||
print("Could not load image list at \(imageListURL.path)")
|
||||
exit(1)
|
||||
}
|
||||
print("Processing \(imageNames.count) images...")
|
||||
|
||||
struct RecognizedLine {
|
||||
let text: String
|
||||
let cx: Double
|
||||
let cy: Double
|
||||
let confidence: Double
|
||||
}
|
||||
|
||||
struct Pair: Encodable {
|
||||
var es: String
|
||||
var en: String
|
||||
var confidence: Double
|
||||
}
|
||||
|
||||
struct ImageResult: Encodable {
|
||||
var pairs: [Pair]
|
||||
var columnCount: Int
|
||||
var strategy: String
|
||||
var lineCount: Int
|
||||
}
|
||||
|
||||
let spanishAccents = Set<Character>(["á","é","í","ó","ú","ñ","ü","Á","É","Í","Ó","Ú","Ñ","Ü","¿","¡"])
|
||||
let spanishArticles: Set<String> = ["el","la","los","las","un","una","unos","unas"]
|
||||
let englishStarters: Set<String> = ["the","a","an","to","my","his","her","our","their","your"]
|
||||
let englishOnly: Set<String> = ["the","he","she","it","we","they","is","are","was","were","been","have","has","had","will","would"]
|
||||
|
||||
func classify(_ s: String) -> String {
|
||||
let lower = s.lowercased()
|
||||
if lower.contains(where: { spanishAccents.contains($0) }) { return "es" }
|
||||
let first = lower.split(separator: " ").first.map(String.init)?.trimmingCharacters(in: .punctuationCharacters) ?? ""
|
||||
if spanishArticles.contains(first) { return "es" }
|
||||
if englishStarters.contains(first) || englishOnly.contains(first) { return "en" }
|
||||
return "?"
|
||||
}
|
||||
|
||||
func recognize(_ cgImage: CGImage) -> [RecognizedLine] {
|
||||
let handler = VNImageRequestHandler(cgImage: cgImage, options: [:])
|
||||
let req = VNRecognizeTextRequest()
|
||||
req.recognitionLevel = .accurate
|
||||
req.recognitionLanguages = ["es-ES", "es", "en-US"]
|
||||
req.usesLanguageCorrection = true
|
||||
if #available(macOS 13.0, *) { req.automaticallyDetectsLanguage = true }
|
||||
try? handler.perform([req])
|
||||
var out: [RecognizedLine] = []
|
||||
for obs in req.results ?? [] {
|
||||
guard let top = obs.topCandidates(1).first else { continue }
|
||||
let s = top.string.trimmingCharacters(in: .whitespaces)
|
||||
if s.isEmpty { continue }
|
||||
let bb = obs.boundingBox
|
||||
out.append(RecognizedLine(
|
||||
text: s,
|
||||
cx: Double(bb.origin.x + bb.width / 2),
|
||||
cy: Double(1.0 - (bb.origin.y + bb.height / 2)),
|
||||
confidence: Double(top.confidence)
|
||||
))
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
/// Split a sorted-by-X line group into cells by finding the largest gap(s).
|
||||
/// `desiredCells` = 2 for 2-col, 4 for 2-pair, etc.
|
||||
func splitRow(_ lines: [RecognizedLine], into desiredCells: Int) -> [String] {
|
||||
guard lines.count >= desiredCells else {
|
||||
// Merge into fewer cells: just concatenate left-to-right.
|
||||
return [lines.map(\.text).joined(separator: " ")]
|
||||
}
|
||||
let sorted = lines.sorted { $0.cx < $1.cx }
|
||||
// Find (desiredCells - 1) biggest gaps
|
||||
var gaps: [(idx: Int, gap: Double)] = []
|
||||
for i in 1..<sorted.count {
|
||||
gaps.append((i, sorted[i].cx - sorted[i - 1].cx))
|
||||
}
|
||||
let splitAt = gaps.sorted { $0.gap > $1.gap }
|
||||
.prefix(desiredCells - 1)
|
||||
.map(\.idx)
|
||||
.sorted()
|
||||
var cells: [[RecognizedLine]] = []
|
||||
var start = 0
|
||||
for s in splitAt {
|
||||
cells.append(Array(sorted[start..<s]))
|
||||
start = s
|
||||
}
|
||||
cells.append(Array(sorted[start..<sorted.count]))
|
||||
return cells.map { $0.map(\.text).joined(separator: " ").trimmingCharacters(in: .whitespaces) }
|
||||
}
|
||||
|
||||
/// Cluster lines into rows by Y proximity. Returns rows in top-to-bottom order.
|
||||
func groupRows(_ lines: [RecognizedLine], tol: Double = 0.025) -> [[RecognizedLine]] {
|
||||
let sorted = lines.sorted { $0.cy < $1.cy }
|
||||
var rows: [[RecognizedLine]] = []
|
||||
var current: [RecognizedLine] = []
|
||||
for l in sorted {
|
||||
if let last = current.last, abs(l.cy - last.cy) > tol {
|
||||
rows.append(current)
|
||||
current = [l]
|
||||
} else {
|
||||
current.append(l)
|
||||
}
|
||||
}
|
||||
if !current.isEmpty { rows.append(current) }
|
||||
return rows
|
||||
}
|
||||
|
||||
/// Detect likely column count: look at how many x-cluster peaks exist across all rows.
|
||||
/// Clusters X-coords from all lines into buckets of 10% width.
|
||||
func detectColumnCount(_ lines: [RecognizedLine]) -> Int {
|
||||
guard !lines.isEmpty else { return 2 }
|
||||
let step = 0.10
|
||||
var buckets = [Int](repeating: 0, count: Int(1.0 / step) + 1)
|
||||
for l in lines {
|
||||
let b = min(max(0, Int(l.cx / step)), buckets.count - 1)
|
||||
buckets[b] += 1
|
||||
}
|
||||
// A peak = a bucket with count > 10% of total lines
|
||||
let threshold = max(2, lines.count / 10)
|
||||
let peaks = buckets.filter { $0 >= threshold }.count
|
||||
// Most tables are 2-col (peaks = 2). Some 4-col (2 ES/EN pairs side by side → peaks = 4).
|
||||
// Roman/decorative layouts may show 1 peak; treat as 2.
|
||||
switch peaks {
|
||||
case 0, 1, 2: return 2
|
||||
case 3: return 3
|
||||
default: return 4
|
||||
}
|
||||
}
|
||||
|
||||
/// Merge label-less cells into Spanish→English pairs.
|
||||
/// `cells` is a row's cells (length = columnCount). For N=2, [es, en]. For N=4,
|
||||
/// [es1, en1, es2, en2] (two pairs). For N=3, [es, en_short, en_long] (rare, merge).
|
||||
func cellsToPairs(_ cells: [String], columnCount: Int) -> [(String, String)] {
|
||||
switch columnCount {
|
||||
case 2 where cells.count >= 2:
|
||||
return [(cells[0], cells[1])]
|
||||
case 3 where cells.count >= 3:
|
||||
// 3-col source: es | en | en-alternate. Keep all three by merging EN sides.
|
||||
return [(cells[0], [cells[1], cells[2]].joined(separator: " / "))]
|
||||
case 4 where cells.count >= 4:
|
||||
return [(cells[0], cells[1]), (cells[2], cells[3])]
|
||||
default:
|
||||
if cells.count >= 2 { return [(cells[0], cells.dropFirst().joined(separator: " "))] }
|
||||
return []
|
||||
}
|
||||
}
|
||||
|
||||
/// Swap pair if orientation is backwards (English on left, Spanish on right).
|
||||
func orientPair(_ pair: (String, String)) -> (String, String) {
|
||||
let (a, b) = pair
|
||||
let ca = classify(a), cb = classify(b)
|
||||
if ca == "en" && cb == "es" { return (b, a) }
|
||||
return pair
|
||||
}
|
||||
|
||||
var results: [String: ImageResult] = [:]
|
||||
var processed = 0
|
||||
let startTime = Date()
|
||||
|
||||
for name in imageNames {
|
||||
processed += 1
|
||||
let url = oebpsDir.appendingPathComponent(name)
|
||||
guard let nsImg = NSImage(contentsOf: url),
|
||||
let tiff = nsImg.tiffRepresentation,
|
||||
let rep = NSBitmapImageRep(data: tiff),
|
||||
let cg = rep.cgImage else {
|
||||
continue
|
||||
}
|
||||
let lines = recognize(cg)
|
||||
if lines.isEmpty {
|
||||
results[name] = ImageResult(pairs: [], columnCount: 2, strategy: "empty", lineCount: 0)
|
||||
continue
|
||||
}
|
||||
|
||||
let columnCount = detectColumnCount(lines)
|
||||
let rows = groupRows(lines, tol: 0.025)
|
||||
var pairs: [Pair] = []
|
||||
for row in rows {
|
||||
guard row.count >= 2 else { continue }
|
||||
let cells = splitRow(row, into: columnCount)
|
||||
let rawPairs = cellsToPairs(cells, columnCount: columnCount)
|
||||
for p in rawPairs {
|
||||
let (es, en) = orientPair(p)
|
||||
if es.count < 1 || en.count < 1 { continue }
|
||||
let avgConf = row.reduce(0.0) { $0 + $1.confidence } / Double(row.count)
|
||||
pairs.append(Pair(es: es, en: en, confidence: avgConf))
|
||||
}
|
||||
}
|
||||
results[name] = ImageResult(
|
||||
pairs: pairs,
|
||||
columnCount: columnCount,
|
||||
strategy: "bbox-row-split",
|
||||
lineCount: lines.count
|
||||
)
|
||||
|
||||
if processed % 50 == 0 || processed == imageNames.count {
|
||||
let elapsed = Date().timeIntervalSince(startTime)
|
||||
let rate = Double(processed) / max(elapsed, 0.001)
|
||||
let eta = Double(imageNames.count - processed) / max(rate, 0.001)
|
||||
print(String(format: "%d/%d %.1f img/s eta %.0fs", processed, imageNames.count, rate, eta))
|
||||
}
|
||||
}
|
||||
|
||||
let enc = JSONEncoder()
|
||||
enc.outputFormatting = [.sortedKeys]
|
||||
try enc.encode(results).write(to: outputURL)
|
||||
let totalPairs = results.values.reduce(0) { $0 + $1.pairs.count }
|
||||
let emptyTables = results.values.filter { $0.pairs.isEmpty }.count
|
||||
print("Wrote \(results.count) results, \(totalPairs) total pairs, \(emptyTables) unpaired")
|
||||
110
Conjuga/Scripts/textbook/ocr_images.swift
Normal file
110
Conjuga/Scripts/textbook/ocr_images.swift
Normal file
@@ -0,0 +1,110 @@
|
||||
#!/usr/bin/env swift
|
||||
// OCR every JPG in the given input directory using the macOS Vision framework.
|
||||
// Output: JSON map of { "<filename>": { "lines": [...], "confidence": Double } }
|
||||
//
|
||||
// Usage: swift ocr_images.swift <input_dir> <output_json>
|
||||
// Example: swift ocr_images.swift ../../../epub_extract/OEBPS ocr.json
|
||||
|
||||
import Foundation
|
||||
import Vision
|
||||
import AppKit
|
||||
|
||||
guard CommandLine.arguments.count >= 3 else {
|
||||
print("Usage: swift ocr_images.swift <input_dir> <output_json>")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
let inputDir = URL(fileURLWithPath: CommandLine.arguments[1])
|
||||
let outputURL = URL(fileURLWithPath: CommandLine.arguments[2])
|
||||
|
||||
// Skip images that are icons/inline markers — not real content
|
||||
let skipSubstrings = ["Common", "cover", "title"]
|
||||
|
||||
let fileManager = FileManager.default
|
||||
guard let enumerator = fileManager.enumerator(at: inputDir, includingPropertiesForKeys: nil) else {
|
||||
print("Could not enumerate \(inputDir.path)")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
var jpgs: [URL] = []
|
||||
for case let url as URL in enumerator {
|
||||
let name = url.lastPathComponent
|
||||
guard name.hasSuffix(".jpg") || name.hasSuffix(".jpeg") || name.hasSuffix(".png") else { continue }
|
||||
if skipSubstrings.contains(where: { name.contains($0) }) { continue }
|
||||
jpgs.append(url)
|
||||
}
|
||||
jpgs.sort { $0.lastPathComponent < $1.lastPathComponent }
|
||||
print("Found \(jpgs.count) images to OCR")
|
||||
|
||||
struct OCRResult: Encodable {
|
||||
var lines: [String]
|
||||
var confidence: Double
|
||||
}
|
||||
|
||||
var results: [String: OCRResult] = [:]
|
||||
let total = jpgs.count
|
||||
var processed = 0
|
||||
let startTime = Date()
|
||||
|
||||
for url in jpgs {
|
||||
processed += 1
|
||||
let name = url.lastPathComponent
|
||||
|
||||
guard let nsImage = NSImage(contentsOf: url),
|
||||
let tiffData = nsImage.tiffRepresentation,
|
||||
let bitmap = NSBitmapImageRep(data: tiffData),
|
||||
let cgImage = bitmap.cgImage else {
|
||||
print("\(processed)/\(total) \(name) — could not load")
|
||||
continue
|
||||
}
|
||||
|
||||
let handler = VNImageRequestHandler(cgImage: cgImage, options: [:])
|
||||
let request = VNRecognizeTextRequest()
|
||||
request.recognitionLevel = .accurate
|
||||
request.recognitionLanguages = ["es-ES", "es", "en-US"]
|
||||
request.usesLanguageCorrection = true
|
||||
// For the 2020 book, automaticallyDetectsLanguage helps with mixed content
|
||||
if #available(macOS 13.0, *) {
|
||||
request.automaticallyDetectsLanguage = true
|
||||
}
|
||||
|
||||
do {
|
||||
try handler.perform([request])
|
||||
let observations = request.results ?? []
|
||||
var lines: [String] = []
|
||||
var totalConfidence: Float = 0
|
||||
var count = 0
|
||||
for obs in observations {
|
||||
if let top = obs.topCandidates(1).first {
|
||||
let s = top.string.trimmingCharacters(in: .whitespaces)
|
||||
if !s.isEmpty {
|
||||
lines.append(s)
|
||||
totalConfidence += top.confidence
|
||||
count += 1
|
||||
}
|
||||
}
|
||||
}
|
||||
let avg = count > 0 ? Double(totalConfidence) / Double(count) : 0.0
|
||||
results[name] = OCRResult(lines: lines, confidence: avg)
|
||||
} catch {
|
||||
print("\(processed)/\(total) \(name) — error: \(error)")
|
||||
}
|
||||
|
||||
if processed % 50 == 0 || processed == total {
|
||||
let elapsed = Date().timeIntervalSince(startTime)
|
||||
let rate = Double(processed) / max(elapsed, 0.001)
|
||||
let remaining = Double(total - processed) / max(rate, 0.001)
|
||||
print(String(format: "%d/%d %.1f img/s eta %.0fs", processed, total, rate, remaining))
|
||||
}
|
||||
}
|
||||
|
||||
let encoder = JSONEncoder()
|
||||
encoder.outputFormatting = [.prettyPrinted, .sortedKeys]
|
||||
do {
|
||||
let data = try encoder.encode(results)
|
||||
try data.write(to: outputURL)
|
||||
print("Wrote \(results.count) OCR entries to \(outputURL.path)")
|
||||
} catch {
|
||||
print("Error writing output: \(error)")
|
||||
exit(1)
|
||||
}
|
||||
133
Conjuga/Scripts/textbook/ocr_pdf.swift
Normal file
133
Conjuga/Scripts/textbook/ocr_pdf.swift
Normal file
@@ -0,0 +1,133 @@
|
||||
#!/usr/bin/env swift
|
||||
// Rasterize each page of a PDF at high DPI and OCR it with Vision.
|
||||
// Output: { "<pdfIndex>": { "lines": [...], "confidence": Double, "bookPage": Int? } }
|
||||
//
|
||||
// Usage: swift ocr_pdf.swift <pdf_path> <output_json> [dpi]
|
||||
// Example: swift ocr_pdf.swift "book.pdf" pdf_ocr.json 240
|
||||
|
||||
import Foundation
|
||||
import Vision
|
||||
import AppKit
|
||||
import Quartz
|
||||
|
||||
guard CommandLine.arguments.count >= 3 else {
|
||||
print("Usage: swift ocr_pdf.swift <pdf_path> <output_json> [dpi]")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
let pdfURL = URL(fileURLWithPath: CommandLine.arguments[1])
|
||||
let outputURL = URL(fileURLWithPath: CommandLine.arguments[2])
|
||||
let dpi: CGFloat = CommandLine.arguments.count >= 4 ? CGFloat(Double(CommandLine.arguments[3]) ?? 240.0) : 240.0
|
||||
|
||||
guard let pdfDoc = PDFDocument(url: pdfURL) else {
|
||||
print("Could not open PDF at \(pdfURL.path)")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
let pageCount = pdfDoc.pageCount
|
||||
print("PDF has \(pageCount) pages. Rendering at \(dpi) DPI.")
|
||||
|
||||
struct PageResult: Encodable {
|
||||
var lines: [String]
|
||||
var confidence: Double
|
||||
var bookPage: Int?
|
||||
}
|
||||
|
||||
var results: [String: PageResult] = [:]
|
||||
let startTime = Date()
|
||||
|
||||
// Render at scale = dpi / 72 (72 is default PDF DPI)
|
||||
let scale: CGFloat = dpi / 72.0
|
||||
|
||||
for i in 0..<pageCount {
|
||||
guard let page = pdfDoc.page(at: i) else { continue }
|
||||
let pageBounds = page.bounds(for: .mediaBox)
|
||||
let scaledSize = CGSize(width: pageBounds.width * scale, height: pageBounds.height * scale)
|
||||
|
||||
// Render the page into a CGImage
|
||||
let colorSpace = CGColorSpaceCreateDeviceRGB()
|
||||
let bitmapInfo = CGImageAlphaInfo.noneSkipLast.rawValue
|
||||
guard let context = CGContext(
|
||||
data: nil,
|
||||
width: Int(scaledSize.width),
|
||||
height: Int(scaledSize.height),
|
||||
bitsPerComponent: 8,
|
||||
bytesPerRow: 0,
|
||||
space: colorSpace,
|
||||
bitmapInfo: bitmapInfo
|
||||
) else {
|
||||
print("\(i): could not create CGContext")
|
||||
continue
|
||||
}
|
||||
context.setFillColor(CGColor(gray: 1.0, alpha: 1.0))
|
||||
context.fill(CGRect(origin: .zero, size: scaledSize))
|
||||
context.scaleBy(x: scale, y: scale)
|
||||
page.draw(with: .mediaBox, to: context)
|
||||
|
||||
guard let cgImage = context.makeImage() else {
|
||||
print("\(i): could not create CGImage")
|
||||
continue
|
||||
}
|
||||
|
||||
let handler = VNImageRequestHandler(cgImage: cgImage, options: [:])
|
||||
let request = VNRecognizeTextRequest()
|
||||
request.recognitionLevel = .accurate
|
||||
request.recognitionLanguages = ["es-ES", "es", "en-US"]
|
||||
request.usesLanguageCorrection = true
|
||||
if #available(macOS 13.0, *) {
|
||||
request.automaticallyDetectsLanguage = true
|
||||
}
|
||||
|
||||
do {
|
||||
try handler.perform([request])
|
||||
let observations = request.results ?? []
|
||||
var lines: [String] = []
|
||||
var totalConfidence: Float = 0
|
||||
var count = 0
|
||||
for obs in observations {
|
||||
if let top = obs.topCandidates(1).first {
|
||||
let s = top.string.trimmingCharacters(in: .whitespaces)
|
||||
if !s.isEmpty {
|
||||
lines.append(s)
|
||||
totalConfidence += top.confidence
|
||||
count += 1
|
||||
}
|
||||
}
|
||||
}
|
||||
let avg = count > 0 ? Double(totalConfidence) / Double(count) : 0.0
|
||||
|
||||
// Try to detect book page number: a short numeric line in the first
|
||||
// 3 or last 3 entries (typical page-number placement).
|
||||
var bookPage: Int? = nil
|
||||
let candidates = Array(lines.prefix(3)) + Array(lines.suffix(3))
|
||||
for c in candidates {
|
||||
let trimmed = c.trimmingCharacters(in: .whitespaces)
|
||||
if let n = Int(trimmed), n >= 1 && n <= 1000 {
|
||||
bookPage = n
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
results[String(i)] = PageResult(lines: lines, confidence: avg, bookPage: bookPage)
|
||||
} catch {
|
||||
print("\(i): \(error)")
|
||||
}
|
||||
|
||||
if (i + 1) % 25 == 0 || (i + 1) == pageCount {
|
||||
let elapsed = Date().timeIntervalSince(startTime)
|
||||
let rate = Double(i + 1) / max(elapsed, 0.001)
|
||||
let remaining = Double(pageCount - (i + 1)) / max(rate, 0.001)
|
||||
print(String(format: "%d/%d %.1f pg/s eta %.0fs", i + 1, pageCount, rate, remaining))
|
||||
}
|
||||
}
|
||||
|
||||
let encoder = JSONEncoder()
|
||||
encoder.outputFormatting = [.sortedKeys]
|
||||
do {
|
||||
let data = try encoder.encode(results)
|
||||
try data.write(to: outputURL)
|
||||
print("Wrote \(results.count) pages to \(outputURL.path)")
|
||||
} catch {
|
||||
print("Error writing output: \(error)")
|
||||
exit(1)
|
||||
}
|
||||
177
Conjuga/Scripts/textbook/repair_quarantined.swift
Normal file
177
Conjuga/Scripts/textbook/repair_quarantined.swift
Normal file
@@ -0,0 +1,177 @@
|
||||
#!/usr/bin/env swift
|
||||
// Re-OCR the images referenced in quarantined_cards.json using Vision with
|
||||
// bounding-box info, then pair lines by column position (left = Spanish,
|
||||
// right = English) instead of by document read order.
|
||||
//
|
||||
// Output: repaired_cards.json — {"byImage": {"f0142-02.jpg": [{"es":..., "en":...}, ...]}}
|
||||
|
||||
import Foundation
|
||||
import Vision
|
||||
import AppKit
|
||||
|
||||
guard CommandLine.arguments.count >= 4 else {
|
||||
print("Usage: swift repair_quarantined.swift <quarantined.json> <epub_oebps_dir> <output.json>")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
let quarantinedURL = URL(fileURLWithPath: CommandLine.arguments[1])
|
||||
let imageDir = URL(fileURLWithPath: CommandLine.arguments[2])
|
||||
let outputURL = URL(fileURLWithPath: CommandLine.arguments[3])
|
||||
|
||||
guard let data = try? Data(contentsOf: quarantinedURL),
|
||||
let json = try? JSONSerialization.jsonObject(with: data) as? [String: Any],
|
||||
let cards = json["cards"] as? [[String: Any]] else {
|
||||
print("Could not load \(quarantinedURL.path)")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
var uniqueImages = Set<String>()
|
||||
for card in cards {
|
||||
if let src = card["sourceImage"] as? String { uniqueImages.insert(src) }
|
||||
}
|
||||
print("Unique images to re-OCR: \(uniqueImages.count)")
|
||||
|
||||
struct RecognizedLine {
|
||||
let text: String
|
||||
let cx: CGFloat // center X (normalized 0..1)
|
||||
let cy: CGFloat // center Y (normalized 0..1 from top)
|
||||
let confidence: Float
|
||||
}
|
||||
|
||||
struct Pair: Encodable {
|
||||
var es: String
|
||||
var en: String
|
||||
var confidence: Double
|
||||
}
|
||||
|
||||
struct ImageResult: Encodable {
|
||||
var pairs: [Pair]
|
||||
var lineCount: Int
|
||||
var strategy: String
|
||||
}
|
||||
|
||||
func classify(_ s: String) -> String {
|
||||
// "es" if has accents or starts with ES article; "en" if starts with EN article; else "?"
|
||||
let lower = s.lowercased()
|
||||
let accentChars: Set<Character> = ["á", "é", "í", "ó", "ú", "ñ", "ü", "¿", "¡"]
|
||||
if lower.contains(where: { accentChars.contains($0) }) { return "es" }
|
||||
let first = lower.split(separator: " ").first.map(String.init) ?? ""
|
||||
let esArticles: Set<String> = ["el", "la", "los", "las", "un", "una", "unos", "unas"]
|
||||
let enStarters: Set<String> = ["the", "a", "an", "to", "my", "his", "her", "our", "their"]
|
||||
if esArticles.contains(first) { return "es" }
|
||||
if enStarters.contains(first) { return "en" }
|
||||
return "?"
|
||||
}
|
||||
|
||||
func recognizeLines(cgImage: CGImage) -> [RecognizedLine] {
|
||||
let handler = VNImageRequestHandler(cgImage: cgImage, options: [:])
|
||||
let request = VNRecognizeTextRequest()
|
||||
request.recognitionLevel = .accurate
|
||||
request.recognitionLanguages = ["es-ES", "es", "en-US"]
|
||||
request.usesLanguageCorrection = true
|
||||
if #available(macOS 13.0, *) {
|
||||
request.automaticallyDetectsLanguage = true
|
||||
}
|
||||
do { try handler.perform([request]) } catch { return [] }
|
||||
var out: [RecognizedLine] = []
|
||||
for obs in request.results ?? [] {
|
||||
guard let top = obs.topCandidates(1).first else { continue }
|
||||
let s = top.string.trimmingCharacters(in: .whitespaces)
|
||||
if s.isEmpty { continue }
|
||||
// Vision's boundingBox is normalized with origin at lower-left
|
||||
let bb = obs.boundingBox
|
||||
let cx = bb.origin.x + bb.width / 2
|
||||
let cyTop = 1.0 - (bb.origin.y + bb.height / 2) // flip to top-origin
|
||||
out.append(RecognizedLine(text: s, cx: cx, cy: cyTop, confidence: top.confidence))
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
/// Pair lines by column position: left column = Spanish, right column = English.
|
||||
/// Groups lines into rows by Y proximity, then within each row pairs left-right.
|
||||
func pairByPosition(_ lines: [RecognizedLine]) -> ([Pair], String) {
|
||||
guard !lines.isEmpty else { return ([], "empty") }
|
||||
|
||||
// Cluster by Y into rows. Use adaptive row height: median line gap * 0.6
|
||||
let sortedByY = lines.sorted { $0.cy < $1.cy }
|
||||
var rows: [[RecognizedLine]] = []
|
||||
var current: [RecognizedLine] = []
|
||||
let rowTol: CGFloat = 0.015 // 1.5% of page height
|
||||
for l in sortedByY {
|
||||
if let last = current.last, abs(l.cy - last.cy) > rowTol {
|
||||
rows.append(current)
|
||||
current = [l]
|
||||
} else {
|
||||
current.append(l)
|
||||
}
|
||||
}
|
||||
if !current.isEmpty { rows.append(current) }
|
||||
|
||||
var pairs: [Pair] = []
|
||||
var strategy = "row-pair"
|
||||
for row in rows {
|
||||
guard row.count >= 2 else { continue }
|
||||
// Sort row by X, split at midpoint; left = Spanish, right = English
|
||||
let sortedX = row.sorted { $0.cx < $1.cx }
|
||||
// Find gap: pick the biggest x-gap in the row to split
|
||||
var maxGap: CGFloat = 0
|
||||
var splitIdx = 1
|
||||
for i in 1..<sortedX.count {
|
||||
let gap = sortedX[i].cx - sortedX[i - 1].cx
|
||||
if gap > maxGap {
|
||||
maxGap = gap
|
||||
splitIdx = i
|
||||
}
|
||||
}
|
||||
let leftLines = Array(sortedX[0..<splitIdx])
|
||||
let rightLines = Array(sortedX[splitIdx..<sortedX.count])
|
||||
let leftText = leftLines.map(\.text).joined(separator: " ").trimmingCharacters(in: .whitespaces)
|
||||
let rightText = rightLines.map(\.text).joined(separator: " ").trimmingCharacters(in: .whitespaces)
|
||||
if leftText.isEmpty || rightText.isEmpty { continue }
|
||||
// Verify language orientation — swap if we got it backwards
|
||||
var es = leftText
|
||||
var en = rightText
|
||||
let lc = classify(es)
|
||||
let rc = classify(en)
|
||||
if lc == "en" && rc == "es" {
|
||||
es = rightText
|
||||
en = leftText
|
||||
}
|
||||
let avgConf = (leftLines + rightLines).reduce(Float(0)) { $0 + $1.confidence } / Float(leftLines.count + rightLines.count)
|
||||
pairs.append(Pair(es: es, en: en, confidence: Double(avgConf)))
|
||||
}
|
||||
|
||||
if pairs.isEmpty { strategy = "no-rows" }
|
||||
return (pairs, strategy)
|
||||
}
|
||||
|
||||
var results: [String: ImageResult] = [:]
|
||||
|
||||
for name in uniqueImages.sorted() {
|
||||
let url = imageDir.appendingPathComponent(name)
|
||||
guard let img = NSImage(contentsOf: url),
|
||||
let tiff = img.tiffRepresentation,
|
||||
let rep = NSBitmapImageRep(data: tiff),
|
||||
let cg = rep.cgImage else {
|
||||
print("\(name): could not load")
|
||||
continue
|
||||
}
|
||||
let lines = recognizeLines(cgImage: cg)
|
||||
let (pairs, strategy) = pairByPosition(lines)
|
||||
results[name] = ImageResult(pairs: pairs, lineCount: lines.count, strategy: strategy)
|
||||
print("\(name): \(lines.count) lines -> \(pairs.count) pairs via \(strategy)")
|
||||
}
|
||||
|
||||
struct Output: Encodable {
|
||||
var byImage: [String: ImageResult]
|
||||
var totalPairs: Int
|
||||
}
|
||||
let output = Output(
|
||||
byImage: results,
|
||||
totalPairs: results.values.reduce(0) { $0 + $1.pairs.count }
|
||||
)
|
||||
|
||||
let enc = JSONEncoder()
|
||||
enc.outputFormatting = [.prettyPrinted, .sortedKeys]
|
||||
try enc.encode(output).write(to: outputURL)
|
||||
print("Wrote \(output.totalPairs) repaired pairs to \(outputURL.path)")
|
||||
54
Conjuga/Scripts/textbook/run_pipeline.sh
Executable file
54
Conjuga/Scripts/textbook/run_pipeline.sh
Executable file
@@ -0,0 +1,54 @@
|
||||
#!/usr/bin/env bash
|
||||
# End-to-end textbook extraction pipeline.
|
||||
#
|
||||
# Requires: Python 3 + lxml/beautifulsoup4/pypdf installed.
|
||||
# macOS for Vision + NSSpellChecker (Swift).
|
||||
#
|
||||
# Inputs: EPUB extracted to epub_extract/OEBPS/ and the PDF at project root.
|
||||
# Outputs: book.json, vocab_cards.json, manual_review.json, quarantined_cards.json
|
||||
|
||||
set -e
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)"
|
||||
cd "$ROOT"
|
||||
|
||||
echo "=== Phase 1a: parse XHTML chapters ==="
|
||||
python3 "$SCRIPT_DIR/extract_chapters.py"
|
||||
|
||||
echo "=== Phase 1b: parse answer key ==="
|
||||
python3 "$SCRIPT_DIR/extract_answers.py"
|
||||
|
||||
if [ ! -f "$SCRIPT_DIR/ocr.json" ]; then
|
||||
echo "=== Phase 1c: OCR EPUB images (first-time only) ==="
|
||||
swift "$SCRIPT_DIR/ocr_images.swift" "$ROOT/epub_extract/OEBPS" "$SCRIPT_DIR/ocr.json"
|
||||
else
|
||||
echo "=== Phase 1c: EPUB OCR already cached ==="
|
||||
fi
|
||||
|
||||
PDF_FILE="$(ls "$ROOT"/Complete\ Spanish\ Step-By-Step*.pdf 2>/dev/null | head -1 || true)"
|
||||
if [ -n "$PDF_FILE" ] && [ ! -f "$SCRIPT_DIR/pdf_ocr.json" ]; then
|
||||
echo "=== Phase 1d: OCR PDF pages (first-time only) ==="
|
||||
swift "$SCRIPT_DIR/ocr_pdf.swift" "$PDF_FILE" "$SCRIPT_DIR/pdf_ocr.json" 240
|
||||
fi
|
||||
|
||||
echo "=== Phase 1e: merge into book.json ==="
|
||||
python3 "$SCRIPT_DIR/merge_pdf_into_book.py"
|
||||
|
||||
echo "=== Phase 2: spell-check validation ==="
|
||||
swift "$SCRIPT_DIR/validate_vocab.swift" "$SCRIPT_DIR/vocab_cards.json" "$SCRIPT_DIR/vocab_validation.json"
|
||||
|
||||
echo "=== Phase 3: auto-fix + quarantine pass 1 ==="
|
||||
python3 "$SCRIPT_DIR/fix_vocab.py"
|
||||
|
||||
echo "=== Phase 3: auto-fix + quarantine pass 2 (convergence) ==="
|
||||
swift "$SCRIPT_DIR/validate_vocab.swift" "$SCRIPT_DIR/vocab_cards.json" "$SCRIPT_DIR/vocab_validation.json"
|
||||
python3 "$SCRIPT_DIR/fix_vocab.py"
|
||||
|
||||
echo ""
|
||||
echo "=== Copy to app bundle ==="
|
||||
cp "$SCRIPT_DIR/book.json" "$ROOT/Conjuga/Conjuga/textbook_data.json"
|
||||
cp "$SCRIPT_DIR/vocab_cards.json" "$ROOT/Conjuga/Conjuga/textbook_vocab.json"
|
||||
ls -lh "$ROOT/Conjuga/Conjuga/textbook_"*.json
|
||||
echo ""
|
||||
echo "Done. Bump textbookDataVersion in DataLoader.swift to trigger re-seed."
|
||||
156
Conjuga/Scripts/textbook/validate_vocab.swift
Normal file
156
Conjuga/Scripts/textbook/validate_vocab.swift
Normal file
@@ -0,0 +1,156 @@
|
||||
#!/usr/bin/env swift
|
||||
// Validate every Spanish/English word in vocab_cards.json using NSSpellChecker.
|
||||
// For each flagged word, produce up to 3 candidate corrections.
|
||||
//
|
||||
// Usage: swift validate_vocab.swift <vocab_cards.json> <output_report.json>
|
||||
|
||||
import Foundation
|
||||
import AppKit
|
||||
|
||||
guard CommandLine.arguments.count >= 3 else {
|
||||
print("Usage: swift validate_vocab.swift <vocab_cards.json> <output_report.json>")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
let inputURL = URL(fileURLWithPath: CommandLine.arguments[1])
|
||||
let outputURL = URL(fileURLWithPath: CommandLine.arguments[2])
|
||||
|
||||
guard let data = try? Data(contentsOf: inputURL),
|
||||
let json = try? JSONSerialization.jsonObject(with: data) as? [String: Any],
|
||||
let chapters = json["chapters"] as? [[String: Any]] else {
|
||||
print("Could not load \(inputURL.path)")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
let checker = NSSpellChecker.shared
|
||||
|
||||
// Tokenize — only letter runs (Unicode aware for Spanish accents)
|
||||
func tokens(_ s: String) -> [String] {
|
||||
let letters = CharacterSet.letters
|
||||
return s.unicodeScalars
|
||||
.split { !letters.contains($0) }
|
||||
.map { String(String.UnicodeScalarView($0)) }
|
||||
.filter { !$0.isEmpty }
|
||||
}
|
||||
|
||||
// Minimal stopword set — names, proper nouns, numeric tokens already filtered
|
||||
let stopES: Set<String> = [
|
||||
"el", "la", "los", "las", "un", "una", "unos", "unas", "del", "al", "de",
|
||||
"a", "en", "y", "o", "que", "no", "se", "con", "por", "para", "lo", "le",
|
||||
"su", "mi", "tu", "yo", "te", "me", "es", "son", "está", "están",
|
||||
]
|
||||
let stopEN: Set<String> = [
|
||||
"the", "a", "an", "to", "of", "in", "and", "or", "is", "are", "was", "were",
|
||||
"be", "been", "my", "his", "her", "our", "their", "your",
|
||||
]
|
||||
|
||||
func checkWord(_ w: String, lang: String, stop: Set<String>) -> [String]? {
|
||||
// Return nil if word is OK, else list of candidate corrections.
|
||||
if w.count < 2 { return nil }
|
||||
if stop.contains(w.lowercased()) { return nil }
|
||||
if w.rangeOfCharacter(from: .decimalDigits) != nil { return nil }
|
||||
|
||||
let range = checker.checkSpelling(
|
||||
of: w,
|
||||
startingAt: 0,
|
||||
language: lang,
|
||||
wrap: false,
|
||||
inSpellDocumentWithTag: 0,
|
||||
wordCount: nil
|
||||
)
|
||||
// Range of `(0, 0)` means no misspelling; otherwise we have a misspelling.
|
||||
if range.location == NSNotFound || range.length == 0 { return nil }
|
||||
|
||||
let guesses = checker.guesses(
|
||||
forWordRange: NSRange(location: 0, length: (w as NSString).length),
|
||||
in: w,
|
||||
language: lang,
|
||||
inSpellDocumentWithTag: 0
|
||||
) ?? []
|
||||
return Array(guesses.prefix(3))
|
||||
}
|
||||
|
||||
struct Flag: Encodable {
|
||||
var chapter: Int
|
||||
var front: String
|
||||
var back: String
|
||||
var badFront: [BadWord]
|
||||
var badBack: [BadWord]
|
||||
var sourceImage: String
|
||||
}
|
||||
struct BadWord: Encodable {
|
||||
var word: String
|
||||
var suggestions: [String]
|
||||
var side: String // "es" or "en"
|
||||
}
|
||||
|
||||
var flags: [Flag] = []
|
||||
var totalCards = 0
|
||||
var totalBadES = 0
|
||||
var totalBadEN = 0
|
||||
|
||||
for ch in chapters {
|
||||
guard let chNum = ch["chapter"] as? Int,
|
||||
let cards = ch["cards"] as? [[String: Any]] else { continue }
|
||||
for card in cards {
|
||||
totalCards += 1
|
||||
let front = (card["front"] as? String) ?? ""
|
||||
let back = (card["back"] as? String) ?? ""
|
||||
let img = (card["sourceImage"] as? String) ?? ""
|
||||
|
||||
var badFront: [BadWord] = []
|
||||
for w in tokens(front) {
|
||||
if let sugg = checkWord(w, lang: "es", stop: stopES) {
|
||||
badFront.append(BadWord(word: w, suggestions: sugg, side: "es"))
|
||||
totalBadES += 1
|
||||
}
|
||||
}
|
||||
var badBack: [BadWord] = []
|
||||
for w in tokens(back) {
|
||||
if let sugg = checkWord(w, lang: "en", stop: stopEN) {
|
||||
badBack.append(BadWord(word: w, suggestions: sugg, side: "en"))
|
||||
totalBadEN += 1
|
||||
}
|
||||
}
|
||||
if !badFront.isEmpty || !badBack.isEmpty {
|
||||
flags.append(Flag(
|
||||
chapter: chNum,
|
||||
front: front,
|
||||
back: back,
|
||||
badFront: badFront,
|
||||
badBack: badBack,
|
||||
sourceImage: img
|
||||
))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct Report: Encodable {
|
||||
var totalCards: Int
|
||||
var flaggedCards: Int
|
||||
var flaggedSpanishWords: Int
|
||||
var flaggedEnglishWords: Int
|
||||
var flags: [Flag]
|
||||
}
|
||||
let report = Report(
|
||||
totalCards: totalCards,
|
||||
flaggedCards: flags.count,
|
||||
flaggedSpanishWords: totalBadES,
|
||||
flaggedEnglishWords: totalBadEN,
|
||||
flags: flags
|
||||
)
|
||||
|
||||
let encoder = JSONEncoder()
|
||||
encoder.outputFormatting = [.prettyPrinted, .sortedKeys]
|
||||
do {
|
||||
let data = try encoder.encode(report)
|
||||
try data.write(to: outputURL)
|
||||
print("Cards: \(totalCards)")
|
||||
print("Flagged cards: \(flags.count) (\(Double(flags.count)/Double(totalCards)*100.0 as Double)%)")
|
||||
print("Flagged ES words: \(totalBadES)")
|
||||
print("Flagged EN words: \(totalBadEN)")
|
||||
print("Wrote \(outputURL.path)")
|
||||
} catch {
|
||||
print("Error writing output: \(error)")
|
||||
exit(1)
|
||||
}
|
||||
68
Conjuga/SharedModels/Sources/SharedModels/AnswerGrader.swift
Normal file
68
Conjuga/SharedModels/Sources/SharedModels/AnswerGrader.swift
Normal file
@@ -0,0 +1,68 @@
|
||||
import Foundation
|
||||
|
||||
/// On-device deterministic answer grader with partial-credit support.
|
||||
/// No network calls, no API keys. Handles accent stripping and single-char typos.
|
||||
public enum AnswerGrader {
|
||||
|
||||
/// Evaluate `userText` against the canonical answer (plus alternates).
|
||||
/// Returns `.correct` for exact/normalized match, `.close` for accent-strip
|
||||
/// match or Levenshtein distance 1, `.wrong` otherwise.
|
||||
public static func grade(userText: String, canonical: String, alternates: [String] = []) -> TextbookGrade {
|
||||
let candidates = [canonical] + alternates
|
||||
let normalizedUser = normalize(userText)
|
||||
if normalizedUser.isEmpty { return .wrong }
|
||||
|
||||
for c in candidates {
|
||||
if normalize(c) == normalizedUser { return .correct }
|
||||
}
|
||||
for c in candidates {
|
||||
if stripAccents(normalize(c)) == stripAccents(normalizedUser) {
|
||||
return .close
|
||||
}
|
||||
}
|
||||
for c in candidates {
|
||||
if levenshtein(normalizedUser, normalize(c)) <= 1 {
|
||||
return .close
|
||||
}
|
||||
}
|
||||
return .wrong
|
||||
}
|
||||
|
||||
/// Lowercase, collapse whitespace, strip leading/trailing punctuation.
|
||||
public static func normalize(_ s: String) -> String {
|
||||
let lowered = s.lowercased(with: Locale(identifier: "es"))
|
||||
let collapsed = lowered.replacingOccurrences(of: "\\s+", with: " ", options: .regularExpression)
|
||||
let trimmed = collapsed.trimmingCharacters(in: .whitespacesAndNewlines)
|
||||
let punct = CharacterSet(charactersIn: ".,;:!?¿¡\"'()[]{}—–-")
|
||||
return trimmed.trimmingCharacters(in: punct)
|
||||
}
|
||||
|
||||
/// Remove combining diacritics (á→a, ñ→n, ü→u).
|
||||
public static func stripAccents(_ s: String) -> String {
|
||||
s.folding(options: .diacriticInsensitive, locale: Locale(identifier: "en"))
|
||||
}
|
||||
|
||||
/// Standard Levenshtein edit distance.
|
||||
public static func levenshtein(_ a: String, _ b: String) -> Int {
|
||||
if a == b { return 0 }
|
||||
if a.isEmpty { return b.count }
|
||||
if b.isEmpty { return a.count }
|
||||
let aa = Array(a)
|
||||
let bb = Array(b)
|
||||
var prev = Array(0...bb.count)
|
||||
var curr = Array(repeating: 0, count: bb.count + 1)
|
||||
for i in 1...aa.count {
|
||||
curr[0] = i
|
||||
for j in 1...bb.count {
|
||||
let cost = aa[i - 1] == bb[j - 1] ? 0 : 1
|
||||
curr[j] = min(
|
||||
prev[j] + 1,
|
||||
curr[j - 1] + 1,
|
||||
prev[j - 1] + cost
|
||||
)
|
||||
}
|
||||
swap(&prev, &curr)
|
||||
}
|
||||
return prev[bb.count]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,86 @@
|
||||
import Foundation
|
||||
import SwiftData
|
||||
|
||||
/// One chapter of the textbook. Ordered content blocks are stored as JSON in `bodyJSON`
|
||||
/// (encoded [TextbookBlock]) since SwiftData @Model doesn't support heterogeneous arrays.
|
||||
@Model
|
||||
public final class TextbookChapter {
|
||||
@Attribute(.unique) public var id: String = ""
|
||||
public var number: Int = 0
|
||||
public var title: String = ""
|
||||
public var part: Int = 0 // 0 = no part assignment
|
||||
public var courseName: String = ""
|
||||
public var bodyJSON: Data = Data()
|
||||
public var exerciseCount: Int = 0
|
||||
public var vocabTableCount: Int = 0
|
||||
|
||||
public init(
|
||||
id: String,
|
||||
number: Int,
|
||||
title: String,
|
||||
part: Int,
|
||||
courseName: String,
|
||||
bodyJSON: Data,
|
||||
exerciseCount: Int,
|
||||
vocabTableCount: Int
|
||||
) {
|
||||
self.id = id
|
||||
self.number = number
|
||||
self.title = title
|
||||
self.part = part
|
||||
self.courseName = courseName
|
||||
self.bodyJSON = bodyJSON
|
||||
self.exerciseCount = exerciseCount
|
||||
self.vocabTableCount = vocabTableCount
|
||||
}
|
||||
|
||||
public func blocks() -> [TextbookBlock] {
|
||||
(try? JSONDecoder().decode([TextbookBlock].self, from: bodyJSON)) ?? []
|
||||
}
|
||||
}
|
||||
|
||||
/// One content block within a chapter. Polymorphic via `kind`.
|
||||
public struct TextbookBlock: Codable, Identifiable, Sendable {
|
||||
public enum Kind: String, Codable, Sendable {
|
||||
case heading
|
||||
case paragraph
|
||||
case keyVocabHeader = "key_vocab_header"
|
||||
case vocabTable = "vocab_table"
|
||||
case exercise
|
||||
}
|
||||
|
||||
public var id: String { "\(kind.rawValue):\(index)" }
|
||||
public var index: Int
|
||||
public var kind: Kind
|
||||
|
||||
// heading
|
||||
public var level: Int?
|
||||
// heading / paragraph
|
||||
public var text: String?
|
||||
|
||||
// vocab_table
|
||||
public var sourceImage: String?
|
||||
public var ocrLines: [String]?
|
||||
public var ocrConfidence: Double?
|
||||
public var cards: [TextbookVocabPair]?
|
||||
|
||||
// exercise
|
||||
public var exerciseId: String?
|
||||
public var instruction: String?
|
||||
public var extra: [String]?
|
||||
public var prompts: [String]?
|
||||
public var answerItems: [TextbookAnswerItem]?
|
||||
public var freeform: Bool?
|
||||
}
|
||||
|
||||
public struct TextbookVocabPair: Codable, Sendable {
|
||||
public var front: String
|
||||
public var back: String
|
||||
}
|
||||
|
||||
public struct TextbookAnswerItem: Codable, Sendable {
|
||||
public var label: String? // A/B/C subpart label or nil
|
||||
public var number: Int
|
||||
public var answer: String
|
||||
public var alternates: [String]
|
||||
}
|
||||
@@ -0,0 +1,83 @@
|
||||
import Foundation
|
||||
import SwiftData
|
||||
|
||||
/// Per-prompt grading state recorded after the user submits an exercise.
|
||||
public enum TextbookGrade: Int, Codable, Sendable {
|
||||
case wrong = 0
|
||||
case close = 1
|
||||
case correct = 2
|
||||
}
|
||||
|
||||
/// User's attempt for one exercise. Stored in the cloud container so progress
|
||||
/// syncs across devices.
|
||||
@Model
|
||||
public final class TextbookExerciseAttempt {
|
||||
/// Deterministic id: "<courseName>|<exerciseId>". CloudKit-synced models can't
|
||||
/// use @Attribute(.unique); code that writes attempts must fetch-or-create.
|
||||
public var id: String = ""
|
||||
public var courseName: String = ""
|
||||
public var chapterNumber: Int = 0
|
||||
public var exerciseId: String = ""
|
||||
|
||||
/// JSON-encoded per-prompt state array.
|
||||
/// Each entry: { "number": Int, "userText": String, "grade": Int }
|
||||
public var stateJSON: Data = Data()
|
||||
|
||||
public var lastAttemptAt: Date = Date()
|
||||
public var correctCount: Int = 0
|
||||
public var closeCount: Int = 0
|
||||
public var wrongCount: Int = 0
|
||||
public var totalCount: Int = 0
|
||||
|
||||
public init(
|
||||
id: String,
|
||||
courseName: String,
|
||||
chapterNumber: Int,
|
||||
exerciseId: String,
|
||||
stateJSON: Data = Data(),
|
||||
lastAttemptAt: Date = Date(),
|
||||
correctCount: Int = 0,
|
||||
closeCount: Int = 0,
|
||||
wrongCount: Int = 0,
|
||||
totalCount: Int = 0
|
||||
) {
|
||||
self.id = id
|
||||
self.courseName = courseName
|
||||
self.chapterNumber = chapterNumber
|
||||
self.exerciseId = exerciseId
|
||||
self.stateJSON = stateJSON
|
||||
self.lastAttemptAt = lastAttemptAt
|
||||
self.correctCount = correctCount
|
||||
self.closeCount = closeCount
|
||||
self.wrongCount = wrongCount
|
||||
self.totalCount = totalCount
|
||||
}
|
||||
|
||||
public func promptStates() -> [TextbookPromptState] {
|
||||
(try? JSONDecoder().decode([TextbookPromptState].self, from: stateJSON)) ?? []
|
||||
}
|
||||
|
||||
public func setPromptStates(_ states: [TextbookPromptState]) {
|
||||
stateJSON = (try? JSONEncoder().encode(states)) ?? Data()
|
||||
correctCount = states.filter { $0.grade == .correct }.count
|
||||
closeCount = states.filter { $0.grade == .close }.count
|
||||
wrongCount = states.filter { $0.grade == .wrong }.count
|
||||
totalCount = states.count
|
||||
}
|
||||
|
||||
public static func attemptId(courseName: String, exerciseId: String) -> String {
|
||||
"\(courseName)|\(exerciseId)"
|
||||
}
|
||||
}
|
||||
|
||||
public struct TextbookPromptState: Codable, Sendable {
|
||||
public var number: Int
|
||||
public var userText: String
|
||||
public var grade: TextbookGrade
|
||||
|
||||
public init(number: Int, userText: String, grade: TextbookGrade) {
|
||||
self.number = number
|
||||
self.userText = userText
|
||||
self.grade = grade
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,80 @@
|
||||
import Testing
|
||||
@testable import SharedModels
|
||||
|
||||
@Suite("AnswerGrader")
|
||||
struct AnswerGraderTests {
|
||||
|
||||
@Test("exact match is correct")
|
||||
func exact() {
|
||||
#expect(AnswerGrader.grade(userText: "tengo", canonical: "tengo") == .correct)
|
||||
#expect(AnswerGrader.grade(userText: "Tengo", canonical: "tengo") == .correct)
|
||||
#expect(AnswerGrader.grade(userText: " tengo ", canonical: "tengo") == .correct)
|
||||
}
|
||||
|
||||
@Test("missing accent is close")
|
||||
func missingAccent() {
|
||||
#expect(AnswerGrader.grade(userText: "esta", canonical: "está") == .close)
|
||||
#expect(AnswerGrader.grade(userText: "nino", canonical: "niño") == .close)
|
||||
#expect(AnswerGrader.grade(userText: "asi", canonical: "así") == .close)
|
||||
}
|
||||
|
||||
@Test("single-char typo is close")
|
||||
func singleCharTypo() {
|
||||
// deletion
|
||||
#expect(AnswerGrader.grade(userText: "tngo", canonical: "tengo") == .close)
|
||||
// insertion
|
||||
#expect(AnswerGrader.grade(userText: "tengoo", canonical: "tengo") == .close)
|
||||
// substitution
|
||||
#expect(AnswerGrader.grade(userText: "tengu", canonical: "tengo") == .close)
|
||||
}
|
||||
|
||||
@Test("two-char typo is wrong")
|
||||
func twoCharTypo() {
|
||||
#expect(AnswerGrader.grade(userText: "tngu", canonical: "tengo") == .wrong)
|
||||
}
|
||||
|
||||
@Test("empty is wrong")
|
||||
func empty() {
|
||||
#expect(AnswerGrader.grade(userText: "", canonical: "tengo") == .wrong)
|
||||
#expect(AnswerGrader.grade(userText: " ", canonical: "tengo") == .wrong)
|
||||
}
|
||||
|
||||
@Test("alternates accepted")
|
||||
func alternates() {
|
||||
#expect(AnswerGrader.grade(userText: "flaca", canonical: "delgada", alternates: ["flaca"]) == .correct)
|
||||
#expect(AnswerGrader.grade(userText: "flacca", canonical: "delgada", alternates: ["flaca"]) == .close)
|
||||
}
|
||||
|
||||
@Test("punctuation stripped")
|
||||
func punctuation() {
|
||||
#expect(AnswerGrader.grade(userText: "el libro.", canonical: "el libro") == .correct)
|
||||
#expect(AnswerGrader.grade(userText: "¿dónde?", canonical: "dónde") == .correct)
|
||||
}
|
||||
|
||||
@Test("very different text is wrong")
|
||||
func wrong() {
|
||||
#expect(AnswerGrader.grade(userText: "hola", canonical: "tengo") == .wrong)
|
||||
#expect(AnswerGrader.grade(userText: "casa", canonical: "perro") == .wrong)
|
||||
}
|
||||
|
||||
@Test("normalize produces expected output")
|
||||
func normalize() {
|
||||
#expect(AnswerGrader.normalize(" Hola ") == "hola")
|
||||
#expect(AnswerGrader.normalize("ABC!") == "abc")
|
||||
}
|
||||
|
||||
@Test("stripAccents handles common Spanish diacritics")
|
||||
func stripAccents() {
|
||||
#expect(AnswerGrader.stripAccents("niño") == "nino")
|
||||
#expect(AnswerGrader.stripAccents("está") == "esta")
|
||||
#expect(AnswerGrader.stripAccents("güero") == "guero")
|
||||
}
|
||||
|
||||
@Test("levenshtein computes edit distance")
|
||||
func levenshtein() {
|
||||
#expect(AnswerGrader.levenshtein("kitten", "sitting") == 3)
|
||||
#expect(AnswerGrader.levenshtein("flaw", "lawn") == 2)
|
||||
#expect(AnswerGrader.levenshtein("abc", "abc") == 0)
|
||||
#expect(AnswerGrader.levenshtein("", "abc") == 3)
|
||||
}
|
||||
}
|
||||
57
README.md
57
README.md
@@ -1,32 +1,57 @@
|
||||
# Conjuga
|
||||
|
||||
A Spanish verb conjugation learning app for iOS. Practice all 20 tenses with spaced repetition, handwriting recognition, and vocabulary courses.
|
||||
A Spanish-learning iOS app that combines verb conjugation practice, a full textbook reader, an AI conversation partner, offline dictionary lookups, grammar exercises, and more. Runs entirely on-device where possible (Foundation Models, Speech framework, Vision OCR).
|
||||
|
||||
## Features
|
||||
|
||||
- **Conjugation Practice** — Flashcards, typing, multiple choice, handwriting, sentence building, and full table modes
|
||||
- **Spaced Repetition** — SM-2 algorithm tracks what you know and surfaces what you don't
|
||||
- **1,750 Verbs** — From basic to expert, with irregular form highlighting
|
||||
- **20 Tenses** — Every indicative, subjunctive, conditional, and imperative tense
|
||||
- **Grammar Guide** — 20 tense guides with conjugation tables + 20 grammar topic notes (Ser vs Estar, Por vs Para, etc.)
|
||||
- **Vocabulary Courses** — Weekly decks with example sentences
|
||||
- **Progress Tracking** — Streaks, daily goals, accuracy stats, and achievement badges
|
||||
- **CloudKit Sync** — Review progress syncs across devices
|
||||
- **Widgets** — Daily progress, word of the day, and weekly stats
|
||||
- **Text-to-Speech** — Hear any verb pronounced in Spanish
|
||||
### Verb practice
|
||||
- **Six conjugation modes** — flashcards, typing, multiple choice, handwriting (Apple Pencil / finger), sentence builder, and full-table (conjugate all persons at once)
|
||||
- **Focus modes** — weak verbs (SM-2 spaced repetition), irregularity drills (spelling / stem / unique), common tenses
|
||||
- **1,750 verbs** across 5 levels (Basic → Expert) with 209 K pre-conjugated forms and 23 K irregular-span annotations
|
||||
- **20 tenses** — every indicative, subjunctive, conditional, and imperative tense, each with character-level irregular highlighting
|
||||
- **Irregularity filter** — search the verb list by Any Irregular / Spelling Change / Stem Change / Unique Irregular, combinable with level filter
|
||||
- **Text-to-speech** on any form
|
||||
|
||||
### Content & study
|
||||
- **Textbook reader** — 30 chapters of *Complete Spanish Step-by-Step* with 251 interactive exercises (keyboard + Apple Pencil), 931 OCR'd vocab tables rendered as Spanish→English grids (~3 100 paired cards extracted via bounding-box OCR)
|
||||
- **Course decks** — weekly vocab decks with example sentences, week tests, and cumulative checkpoint exams
|
||||
- **Stem-change toggle** on Week 4 flashcard decks (E-IE, E-I, O-UE, U-UE) showing inline present-tense conjugations
|
||||
- **Grammar guide** — 20 tense guides with usage rules and examples + 20+ grammar topic notes (ser/estar, por/para, preterite/imperfect, etc.), each with 100+ practice exercises
|
||||
- **Grammar exercises** — interactive quizzes for 5 core topics (ser/estar, por/para, preterite/imperfect, subjunctive, personal *a*)
|
||||
|
||||
### AI & speech
|
||||
- **Conversational practice** — on-device AI chat partner (Apple Foundation Models) with 10 scenario types; chat bubbles have tappable words that open dictionary / on-demand AI lookup
|
||||
- **AI short stories** — generated stories with tappable words and comprehension quizzes
|
||||
- **Listening practice** — listen-and-type + pronunciation scoring via the Speech framework
|
||||
- **Pronunciation check** — word-by-word match scoring
|
||||
|
||||
### Vocabulary & tools
|
||||
- **Offline dictionary** — reverse index of 175 K verb forms + 200 common words, cached to disk for instant lookups
|
||||
- **Vocab SRS review** — spaced repetition over course vocabulary with Again / Hard / Good / Easy rating
|
||||
- **Cloze practice** — fill-in-the-blank sentences with distractor generation
|
||||
- **Lyrics practice** — search, translate, and read Spanish song lyrics
|
||||
|
||||
### Tracking & sync
|
||||
- **Progress** — streaks, daily goals, accuracy stats, achievement badges, study-time tracking per day
|
||||
- **CloudKit sync** — review progress, test results, saved stories, conversations, and textbook attempts sync across devices
|
||||
- **Widgets** — combined dashboard + word-of-the-day, refreshed daily and on backgrounding
|
||||
|
||||
## Architecture
|
||||
|
||||
- **SwiftUI** + **SwiftData** with a dual-store configuration:
|
||||
- **Local store** — Reference data (verbs, forms, guides) seeded from JSON on first launch
|
||||
- **Cloud store** — User data (review cards, progress, streaks) synced via CloudKit
|
||||
- **SharedModels** package shared between the app and widget extension
|
||||
- **Local store** (App Group `group.com.conjuga.app`) — reference data: verbs, forms, irregular spans, tense guides, course decks, vocab cards, textbook chapters. Seeded from bundled JSON on first launch. Self-healing re-seeds trigger on version bumps *or* if rows are missing on disk.
|
||||
- **Cloud store** (CloudKit `iCloud.com.conjuga.app`, private database) — user data: review cards, course reviews, user progress, test results, daily logs, saved songs, stories, conversations, textbook exercise attempts.
|
||||
- **SharedModels** Swift Package shared between the app and widget extension. Widget schema must include every local-store entity or SwiftData destructively migrates the shared store.
|
||||
- **Foundation Models** for on-device AI generation (`@Generable` structs for typed output).
|
||||
- **Vision** framework for OCR of textbook pages and vocabulary images.
|
||||
- **Speech** framework for recognition and pronunciation scoring.
|
||||
- **Textbook extraction pipeline** (`Conjuga/Scripts/textbook/`) — XHTML and answer-key parsers, macOS Vision image OCR + PDF page OCR, bounding-box vocab pair extractor, NSSpellChecker-based validator, and language-aware auto-fixer.
|
||||
|
||||
## Requirements
|
||||
|
||||
- iOS 18+
|
||||
- iOS 18+ (iOS 26 for Foundation Models features)
|
||||
- Xcode 16+
|
||||
|
||||
## Building
|
||||
|
||||
Open `Conjuga/Conjuga.xcodeproj` in Xcode and run on a simulator or device. Data seeds automatically on first launch.
|
||||
Open `Conjuga/Conjuga.xcodeproj` in Xcode and run on a simulator or device. Reference data seeds automatically on first launch. To regenerate textbook content, run `Conjuga/Scripts/textbook/run_pipeline.sh` locally — the generated `textbook_data.json` / `textbook_vocab.json` are committed so fresh clones build without the pipeline.
|
||||
|
||||
137
app_features.md
137
app_features.md
@@ -1,6 +1,85 @@
|
||||
# Spanish Conjugation Apps — Feature Comparison
|
||||
|
||||
Side-by-side feature analysis of **ConjuGato** and **Conjuu ES**, based on App Store screenshots and extracted app data.
|
||||
Side-by-side feature analysis of **Conjuga** (this project), **ConjuGato**, and **Conjuu ES**, based on App Store screenshots, extracted app data, and this repository's source.
|
||||
|
||||
---
|
||||
|
||||
## Conjuga (this project)
|
||||
|
||||
**Platform:** iOS 18+ (iOS 26 required for Foundation Models features)
|
||||
**Monetization:** —
|
||||
**Tech stack:** SwiftUI + SwiftData (dual local / CloudKit stores), SharedModels Swift Package, Foundation Models, Vision, Speech, WidgetKit
|
||||
|
||||
### Practice Modes
|
||||
|
||||
- **Six core conjugation modes** — flashcards, typing, multiple choice, handwriting (Apple Pencil + finger), sentence builder, full table (all persons at once)
|
||||
- **Focus modes** — weak verbs (SM-2 SRS), irregularity drills (spelling / stem / unique, selectable), common tenses
|
||||
- **Quick answer review** with per-form irregular-span highlighting
|
||||
- **Vocab SRS Review** — spaced repetition over course vocabulary with Again / Hard / Good / Easy rating
|
||||
- **Cloze practice** — fill-in-the-blank with distractor generation from vocab pool
|
||||
- **Listening practice** — listen-and-type + pronunciation scoring via Speech framework, word-by-word match
|
||||
- **Lyrics practice** — search Spanish songs, translate line by line
|
||||
- **Conversational practice** — on-device AI chat partner (Apple Foundation Models) with 10 scenarios, tappable words that open dictionary or on-demand AI lookup
|
||||
- **AI short stories** — generated stories with tappable words + comprehension quiz
|
||||
|
||||
### Verb Reference
|
||||
|
||||
- **1,750 verbs**, 5 levels (Basic / Elementary / Intermediate / Advanced / Expert), 209 K pre-conjugated forms
|
||||
- **20 tenses** with character-level irregular-span highlighting (spelling / stem / unique)
|
||||
- **Irregularity filter** — All / Any Irregular / Spelling Change / Stem Change / Unique Irregular — combinable with level
|
||||
- **Per-verb detail** with per-form English translations and tense-grouped table
|
||||
- **Text-to-speech** on any conjugated form
|
||||
|
||||
### Grammar & Content
|
||||
|
||||
- **20 tense guides** with usage rules and examples
|
||||
- **20+ grammar topic notes** (ser/estar, por/para, preterite/imperfect, subjunctive, personal *a*, suffixes, irregular yo forms, stem-changers, etc.) each with 100+ practice exercises
|
||||
- **Grammar exercises** — interactive quizzes for 5 core topics
|
||||
- **Course decks** — weekly vocabulary with example sentences, week tests, cumulative checkpoint exams
|
||||
- **Stem-change toggle** on Week 4 decks (E-IE, E-I, O-UE, U-UE) with inline present-tense conjugations
|
||||
- **Textbook reader** — 30 chapters of *Complete Spanish Step-by-Step* with 251 interactive exercises (keyboard + Apple Pencil), 931 OCR'd vocab tables rendered as paired Spanish→English grids (~3 100 cards)
|
||||
- **Textbook extraction pipeline** — XHTML + answer-key parsers, macOS Vision image OCR, PDF page OCR, bounding-box vocab pair extractor, NSSpellChecker validator, language-aware auto-fixer
|
||||
|
||||
### Offline Dictionary
|
||||
|
||||
- **Reverse index of 175 K verb forms + 200 common words**, cached to disk
|
||||
- **Tappable-word lookup** in chat bubbles and stories; falls back to Foundation Models `@Generable ChatWordInfo` when a word isn't in the dictionary
|
||||
|
||||
### Progress & Sync
|
||||
|
||||
- **SM-2 spaced repetition** for verb review
|
||||
- **Streaks, daily goals, accuracy stats, achievement badges**
|
||||
- **Study-time tracking** per day (foreground time)
|
||||
- **CloudKit private-database sync** — review cards, user progress, test results, daily logs, saved songs, stories, conversations, textbook exercise attempts
|
||||
- **Background app refresh** for widget data
|
||||
|
||||
### Widgets
|
||||
|
||||
- **Combined dashboard** (word of day + stats)
|
||||
- **Word-of-day** standalone
|
||||
- Both refresh daily and on app backgrounding
|
||||
|
||||
### Settings & Filters
|
||||
|
||||
- **Selectable verb level** and **enabled tenses**
|
||||
- **Include vosotros** toggle
|
||||
- **Auto-fill verb stem** toggle for Full Table practice
|
||||
- **Feature reference** page in Settings documenting every feature and which settings affect it
|
||||
|
||||
### Data (in repo)
|
||||
|
||||
| Asset | Count |
|
||||
|-------|-------|
|
||||
| Verbs | 1,750 |
|
||||
| Verb forms (pre-conjugated) | 209,014 |
|
||||
| Irregular span annotations | 23,795 |
|
||||
| Tenses | 20 |
|
||||
| Tense guides | 20 |
|
||||
| Grammar notes | 20+ (each with 100+ exercises) |
|
||||
| Textbook chapters | 30 |
|
||||
| Textbook exercises | 251 |
|
||||
| Textbook vocab pairs | ~3,118 |
|
||||
| Offline dictionary forms | 175,425 |
|
||||
|
||||
---
|
||||
|
||||
@@ -184,30 +263,44 @@ Side-by-side feature analysis of **ConjuGato** and **Conjuu ES**, based on App S
|
||||
|
||||
## Feature Comparison
|
||||
|
||||
| Feature | ConjuGato | Conjuu ES |
|
||||
|---------|-----------|-----------|
|
||||
| **Verb count** | 1,750 | 621 |
|
||||
| **Tenses** | 27 | 20 |
|
||||
| **Practice style** | Flashcard (tap to reveal) | Typing (fill in forms) |
|
||||
| **Input method** | Self-rate (no typing) | Type conjugation |
|
||||
| **Daily goals** | Numeric counter (25/100) | 4 presets (Casual→Committed) |
|
||||
| **Spaced repetition** | Yes (star + heart ratings) | Session-based review |
|
||||
| **Rhyming mnemonics** | Yes (dedicated tab) | Not visible |
|
||||
| **Grammar breakdown** | Color-coded span highlighting | Ending table + usage rules |
|
||||
| **Irregularity detail** | 3-level (spelling/stem/unique) with character spans | 4-label (ordinary/irregular/regular/orto) |
|
||||
| **Tense explanations** | In-app guides with mnemonics | Modal popups with usage categories |
|
||||
| **Example sentences** | Per-tense with audio | Per-tense in guide popups |
|
||||
| **Audio** | Tap any conjugation + auto-pronounce | Not prominent |
|
||||
| **Difficulty levels** | Filter by irregularity type | 8 graded word lists |
|
||||
| **Custom lists** | No | Yes (My Lists + Pinboard) |
|
||||
| **Verb filters** | Ending type + irregularity + tense | Tense checkboxes + level |
|
||||
| **Translations** | 8 languages | 4 languages |
|
||||
| **Platform** | iOS (Catalyst) | macOS + iOS |
|
||||
| **Monetization** | One-time purchase | Free tier + paid |
|
||||
| **vosotros toggle** | Yes | Not visible |
|
||||
| Feature | Conjuga | ConjuGato | Conjuu ES |
|
||||
|---------|---------|-----------|-----------|
|
||||
| **Verb count** | 1,750 | 1,750 | 621 |
|
||||
| **Tenses** | 20 | 27 | 20 |
|
||||
| **Practice style** | Flashcards, typing, MC, handwriting, sentence builder, full table | Flashcard (tap to reveal) | Typing (fill in forms) |
|
||||
| **Input method** | All four (type / tap / write / speak) | Self-rate (no typing) | Type conjugation |
|
||||
| **Daily goals** | Numeric counter + streaks + study-time | Numeric counter (25/100) | 4 presets (Casual→Committed) |
|
||||
| **Spaced repetition** | SM-2 (verb forms + vocab cards) | Yes (star + heart ratings) | Session-based review |
|
||||
| **Grammar breakdown** | Character-level irregular span highlighting | Color-coded span highlighting | Ending table + usage rules |
|
||||
| **Irregularity detail** | 3-level (spelling/stem/unique) + per-verb badges + filter | 3-level with character spans | 4-label (ordinary/irregular/regular/orto) |
|
||||
| **Tense explanations** | 20 guides + 20 grammar notes with 100+ exercises each | In-app guides with mnemonics | Modal popups with usage categories |
|
||||
| **Example sentences** | Per-tense + per-card + per-story + in textbook | Per-tense with audio | Per-tense in guide popups |
|
||||
| **Audio** | TTS on any form + pronunciation scoring | Tap any conjugation + auto-pronounce | Not prominent |
|
||||
| **Difficulty levels** | 5 levels + irregularity filter (combinable) | Filter by irregularity type | 8 graded word lists |
|
||||
| **Custom lists** | — | No | Yes (My Lists + Pinboard) |
|
||||
| **Verb filters** | Level + irregularity category + tense selection + search | Ending type + irregularity + tense | Tense checkboxes + level |
|
||||
| **AI features** | On-device chat, stories, word lookups (Foundation Models) | — | — |
|
||||
| **Textbook** | 30 chapters, 251 exercises, ~3,100 vocab pairs (OCR pipeline) | — | — |
|
||||
| **Listening / speech** | Listen-and-type + pronunciation scoring | — | — |
|
||||
| **Offline dictionary** | 175 K forms | — | — |
|
||||
| **Lyrics** | Search + translate | — | — |
|
||||
| **CloudKit sync** | Yes (private database) | — | — |
|
||||
| **Widgets** | Combined + word-of-day | — | — |
|
||||
| **Platform** | iOS 18+ (iOS 26 for AI) | iOS (Catalyst) | macOS + iOS |
|
||||
| **Monetization** | — | One-time purchase | Free tier + paid |
|
||||
| **vosotros toggle** | Yes | Yes | Not visible |
|
||||
|
||||
## Unique Strengths
|
||||
|
||||
### Conjuga excels at:
|
||||
- **Breadth of modes** — six conjugation practice styles plus cloze, listening, lyrics, chat, stories, textbook, and vocab review in one app
|
||||
- **AI integration** — on-device Foundation Models power the chat partner, story generator, and dictionary fallback lookup with no cloud round-trips
|
||||
- **Textbook reader** — full *Complete Spanish Step-by-Step* textbook with 251 interactive exercises and 3,100 OCR-paired vocab cards, built via an in-repo extraction pipeline
|
||||
- **Combinable filters** — Level × Irregularity × search on the verb list, with per-row category badges
|
||||
- **Multi-input practice** — type, tap, Apple Pencil handwriting, and voice (pronunciation scoring)
|
||||
- **Offline dictionary** — 175 K verb-form reverse index makes word lookups instant and network-free
|
||||
- **CloudKit sync** — progress, tests, saved content, and conversations travel between devices
|
||||
|
||||
### ConjuGato excels at:
|
||||
- **Breadth** — nearly 3× the verbs
|
||||
- **Irregularity teaching** — character-level color-coded highlighting showing exactly *why* each form is irregular
|
||||
|
||||
Reference in New Issue
Block a user