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Merge branch 'main' of gitea.treytartt.com:admin/Spanish

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Trey t
2026-04-19 15:23:01 -05:00
parent c73762ab9f 63dfc5e41a
commit df96a9e540
34 changed files with 4516 additions and 61 deletions
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13
.gitignore vendored
View File

@@ -40,3 +40,16 @@ scrape/
*.webm
*.mp4
*.mkv
# Third-party textbook sources (not redistributable)
*.pdf
*.epub
epub_extract/
# Textbook extraction artifacts — regenerate locally via run_pipeline.sh.
# Scripts are committed; their generated outputs are not.
Conjuga/Scripts/textbook/*.json
Conjuga/Scripts/textbook/review.html
# App-bundle copies of the textbook content
Conjuga/Conjuga/textbook_data.json
Conjuga/Conjuga/textbook_vocab.json

289
Conjuga/Conjuga.xcodeproj/project.pbxproj
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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 +661,25 @@
548B46ED3C40F5F28A5ADCC6 /* XCLocalSwiftPackageReference "SharedModels" */,
);
preferredProjectObjectVersion = 77;
productRefGroup = F605D24E5EA11065FD18AF7E /* Products */;
projectDirPath = "";
projectRoot = "";
targets = (
96127FACA68AE541F5C0F8BC /* Conjuga */,
F73909B4044081DB8F6272AF /* ConjugaWidgetExtension */,
C6CC399BFD5A2574CB9956B4 /* ConjugaUITests */,
);
};
/* End PBXProject section */
/* Begin PBXResourcesBuildPhase section */
425DC31DA6EF2C4C7A873DAA /* Resources */ = {
isa = PBXResourcesBuildPhase;
buildActionMask = 2147483647;
files = (
);
runOnlyForDeploymentPostprocessing = 0;
};
B74A8384221C70A670B902D8 /* Resources */ = {
isa = PBXResourcesBuildPhase;
buildActionMask = 2147483647;
@@ -585,6 +687,8 @@
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 +706,10 @@
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 +761,7 @@
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 +777,8 @@
C8AF0931F7FD458C80B6EC0D /* ChatLibraryView.swift in Sources */,
6CCC8D51F5524688A4BC5AF8 /* ChatView.swift in Sources */,
8510085D78E248D885181E80 /* FeatureReferenceView.swift in Sources */,
);
943728CD3E65FE6CCADB05EE /* StemChangeConjugationView.swift in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
217A29BCEDD9D44B6DD85AF6 /* Sources */ = {
@@ -686,9 +795,25 @@
);
runOnlyForDeploymentPostprocessing = 0;
};
66589E8F78971725CA2066ED /* Sources */ = {
isa = PBXSourcesBuildPhase;
buildActionMask = 2147483647;
files = (
96A3E5FA8EC63123D97365E1 /* TextbookFlowUITests.swift in Sources */,
F7E459C46F25A8A45D7E0DFB /* AllChaptersScreenshotTests.swift in Sources */,
1B0B3B2C771AD72E25B3493C /* StemChangeToggleTests.swift in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
/* 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 +962,24 @@
};
name = Release;
};
A923186E44A25A8086B27A34 /* Release */ = {
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;
VALIDATE_PRODUCT = YES;
};
name = Release;
};
B9223DC55BB69E9AB81B59AE /* Debug */ = {
isa = XCBuildConfiguration;
buildSettings = {
@@ -902,6 +1045,23 @@
};
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 +1092,15 @@
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 */

10
Conjuga/Conjuga.xcodeproj/xcshareddata/xcschemes/Conjuga.xcscheme
View File

@@ -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>

4
Conjuga/Conjuga/ConjugaApp.swift
View File

@@ -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
View 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)
}
}

178
Conjuga/Conjuga/Services/DataLoader.swift
View File

@@ -6,6 +6,9 @@ actor DataLoader {
static let courseDataVersion = 7
static let courseDataKey = "courseDataVersion"
static let textbookDataVersion = 8
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 +139,35 @@ actor DataLoader {
// Seed course data (uses the same mainContext so @Query sees it)
seedCourseData(context: context)
// Seed textbook data
seedTextbookData(context: context)
UserDefaults.standard.set(textbookDataVersion, forKey: textbookDataKey)
}
/// Re-seed textbook data if the version has changed.
static func refreshTextbookDataIfNeeded(container: ModelContainer) async {
let shared = UserDefaults.standard
if shared.integer(forKey: textbookDataKey) >= textbookDataVersion { return }
print("Textbook data version outdated — re-seeding...")
let context = ModelContext(container)
// Only wipe textbook chapters and our textbook-scoped CourseDecks
// (not the LanGo decks, which live in the same tables).
try? context.delete(model: TextbookChapter.self)
let textbookCourseName = "Complete Spanish Step-by-Step"
let deckDescriptor = FetchDescriptor<CourseDeck>(
predicate: #Predicate<CourseDeck> { $0.courseName == textbookCourseName }
)
if let decks = try? context.fetch(deckDescriptor) {
for deck in decks { context.delete(deck) }
}
try? context.save()
seedTextbookData(context: context)
shared.set(textbookDataVersion, forKey: textbookDataKey)
print("Textbook data re-seeded to version \(textbookDataVersion)")
}
/// Re-seed course data if the version has changed (e.g. examples were added).
@@ -170,6 +205,10 @@ 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 +375,143 @@ actor DataLoader {
context.insert(reviewCard)
return reviewCard
}
// MARK: - Textbook seeding
private static func seedTextbookData(context: ModelContext) {
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
}
guard let json = try? JSONSerialization.jsonObject(with: data) as? [String: Any] else {
print("[DataLoader] ERROR: Could not parse textbook_data.json")
return
}
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
}
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 }
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
}
try? context.save()
// 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")
}
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")
}
}

1
Conjuga/Conjuga/Services/StartupCoordinator.swift
View File

@@ -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.

53
Conjuga/Conjuga/Views/Course/CourseView.swift
View File

@@ -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 {

56
Conjuga/Conjuga/Views/Course/DeckStudyView.swift
View File

@@ -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
View 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
View 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)
}

185
Conjuga/Conjuga/Views/Course/TextbookChapterView.swift Normal file
View File

@@ -0,0 +1,185 @@
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 lines = block.ocrLines ?? []
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 (\(lines.count) items)")
.font(.subheadline.weight(.medium))
.foregroundStyle(.primary)
Spacer()
}
.contentShape(Rectangle())
}
.buttonStyle(.plain)
if expanded {
VStack(alignment: .leading, spacing: 2) {
ForEach(Array(lines.enumerated()), id: \.offset) { _, line in
Text(line)
.font(.callout.monospaced())
.foregroundStyle(.secondary)
}
}
.padding(.leading, 14)
}
}
.padding(10)
.frame(maxWidth: .infinity, alignment: .leading)
.background(Color.orange.opacity(0.08), in: RoundedRectangle(cornerRadius: 10))
}
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
View 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: "")
}
}

45
Conjuga/Conjuga/Views/Course/VocabFlashcardView.swift
View File

@@ -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 {

95
Conjuga/ConjugaUITests/AllChaptersScreenshotTests.swift Normal file
View 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
View 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)
}
}

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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)
}
}

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#!/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()

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#!/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()

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#!/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()

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#!/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()

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Conjuga/Scripts/textbook/extract_pdf_text.py Normal file
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#!/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()

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Conjuga/Scripts/textbook/fix_vocab.py Normal file
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#!/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 obvious mis-pairs: both sides same language OR language mismatch
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")
# A good card has front=es, back=en. Anything else when the card is
# flagged is almost always a column-pairing error.
if front_lang != "es" or back_lang != "en":
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()

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#!/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()

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#!/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"
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 {}
print(f"Mapped {len(pdf_pages)} PDF pages to book page numbers")
# 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
derived = build_vocab_cards_for_block(
{"src": src},
{"lines": merged_lines, "confidence": merged_conf},
ch, current_section_title, bi
)
all_vocab_cards.extend(derived)
out_blocks.append({
"kind": "vocab_table",
"sourceImage": src,
"ocrLines": merged_lines,
"ocrConfidence": merged_conf,
"cardCount": len(derived),
"source": "pdf-repaired" if repairs > 0 else ("epub" if epub_lines else "pdf"),
"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()

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#!/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)
}

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Conjuga/Scripts/textbook/ocr_pdf.swift Normal file
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#!/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)
}

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Conjuga/Scripts/textbook/repair_quarantined.swift Normal file
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#!/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
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#!/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
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#!/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
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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]
}
}

86
Conjuga/SharedModels/Sources/SharedModels/TextbookChapter.swift Normal file
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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]
}

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Conjuga/SharedModels/Sources/SharedModels/TextbookExerciseAttempt.swift Normal file
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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
}
}

80
Conjuga/SharedModels/Tests/SharedModelsTests/AnswerGraderTests.swift Normal file
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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)
}
}