Initial commit — PlantGuideScraper project
This commit is contained in:
Trey T
190
backend/app/api/stats.py
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190
backend/app/api/stats.py
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import json
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy.orm import Session
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from sqlalchemy import func, case
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from app.database import get_db
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from app.models import Species, Image, Job
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from app.models.cached_stats import CachedStats
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from app.schemas.stats import StatsResponse, SourceStats, LicenseStats, SpeciesStats, JobStats
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router = APIRouter()
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@router.get("", response_model=StatsResponse)
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def get_stats(db: Session = Depends(get_db)):
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"""Get dashboard statistics from cache (updated every 60s by Celery)."""
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# Try to get cached stats
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cached = db.query(CachedStats).filter(CachedStats.key == "dashboard_stats").first()
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if cached:
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data = json.loads(cached.value)
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return StatsResponse(
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total_species=data["total_species"],
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total_images=data["total_images"],
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images_downloaded=data["images_downloaded"],
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images_pending=data["images_pending"],
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images_rejected=data["images_rejected"],
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disk_usage_mb=data["disk_usage_mb"],
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sources=[SourceStats(**s) for s in data["sources"]],
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licenses=[LicenseStats(**l) for l in data["licenses"]],
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jobs=JobStats(**data["jobs"]),
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top_species=[SpeciesStats(**s) for s in data["top_species"]],
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under_represented=[SpeciesStats(**s) for s in data["under_represented"]],
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)
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# No cache yet - return empty stats (Celery will populate soon)
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# This only happens on first startup before Celery runs
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return StatsResponse(
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total_species=0,
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total_images=0,
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images_downloaded=0,
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images_pending=0,
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images_rejected=0,
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disk_usage_mb=0.0,
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sources=[],
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licenses=[],
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jobs=JobStats(running=0, pending=0, completed=0, failed=0),
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top_species=[],
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under_represented=[],
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)
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@router.post("/refresh")
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def refresh_stats_now(db: Session = Depends(get_db)):
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"""Manually trigger a stats refresh."""
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from app.workers.stats_tasks import refresh_stats
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refresh_stats.delay()
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return {"status": "refresh_queued"}
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@router.get("/sources")
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def get_source_stats(db: Session = Depends(get_db)):
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"""Get per-source breakdown."""
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stats = db.query(
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Image.source,
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func.count(Image.id).label("total"),
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func.sum(case((Image.status == "downloaded", 1), else_=0)).label("downloaded"),
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func.sum(case((Image.status == "pending", 1), else_=0)).label("pending"),
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func.sum(case((Image.status == "rejected", 1), else_=0)).label("rejected"),
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).group_by(Image.source).all()
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return [
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{
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"source": s.source,
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"total": s.total,
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"downloaded": s.downloaded or 0,
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"pending": s.pending or 0,
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"rejected": s.rejected or 0,
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}
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for s in stats
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]
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@router.get("/species")
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def get_species_stats(
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min_count: int = 0,
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max_count: int = None,
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db: Session = Depends(get_db),
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):
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"""Get per-species image counts."""
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query = db.query(
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Species.id,
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Species.scientific_name,
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Species.common_name,
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Species.genus,
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func.count(Image.id).label("image_count")
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).outerjoin(Image, (Image.species_id == Species.id) & (Image.status == "downloaded")
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).group_by(Species.id)
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if min_count > 0:
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query = query.having(func.count(Image.id) >= min_count)
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if max_count is not None:
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query = query.having(func.count(Image.id) <= max_count)
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stats = query.order_by(func.count(Image.id).desc()).all()
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return [
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{
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"id": s.id,
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"scientific_name": s.scientific_name,
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"common_name": s.common_name,
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"genus": s.genus,
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"image_count": s.image_count,
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}
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for s in stats
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]
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@router.get("/distribution")
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def get_image_distribution(db: Session = Depends(get_db)):
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"""Get distribution of images per species for ML training assessment.
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Returns counts of species at various image thresholds to help
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determine dataset quality for training image classifiers.
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"""
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from sqlalchemy import text
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# Get image counts per species using optimized raw SQL
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distribution_sql = text("""
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WITH species_counts AS (
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SELECT
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s.id,
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COUNT(i.id) as cnt
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FROM species s
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LEFT JOIN images i ON i.species_id = s.id AND i.status = 'downloaded'
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GROUP BY s.id
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)
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SELECT
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COUNT(*) as total_species,
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SUM(CASE WHEN cnt = 0 THEN 1 ELSE 0 END) as with_0,
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SUM(CASE WHEN cnt >= 1 AND cnt < 10 THEN 1 ELSE 0 END) as with_1_9,
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SUM(CASE WHEN cnt >= 10 AND cnt < 25 THEN 1 ELSE 0 END) as with_10_24,
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SUM(CASE WHEN cnt >= 25 AND cnt < 50 THEN 1 ELSE 0 END) as with_25_49,
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SUM(CASE WHEN cnt >= 50 AND cnt < 100 THEN 1 ELSE 0 END) as with_50_99,
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SUM(CASE WHEN cnt >= 100 AND cnt < 200 THEN 1 ELSE 0 END) as with_100_199,
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SUM(CASE WHEN cnt >= 200 THEN 1 ELSE 0 END) as with_200_plus,
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SUM(CASE WHEN cnt >= 10 THEN 1 ELSE 0 END) as trainable_10,
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SUM(CASE WHEN cnt >= 25 THEN 1 ELSE 0 END) as trainable_25,
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SUM(CASE WHEN cnt >= 50 THEN 1 ELSE 0 END) as trainable_50,
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SUM(CASE WHEN cnt >= 100 THEN 1 ELSE 0 END) as trainable_100,
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AVG(cnt) as avg_images,
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MAX(cnt) as max_images,
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MIN(cnt) as min_images,
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SUM(cnt) as total_images
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FROM species_counts
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""")
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result = db.execute(distribution_sql).fetchone()
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return {
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"total_species": result[0] or 0,
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"distribution": {
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"0_images": result[1] or 0,
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"1_to_9": result[2] or 0,
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"10_to_24": result[3] or 0,
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"25_to_49": result[4] or 0,
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"50_to_99": result[5] or 0,
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"100_to_199": result[6] or 0,
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"200_plus": result[7] or 0,
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},
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"trainable_species": {
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"min_10_images": result[8] or 0,
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"min_25_images": result[9] or 0,
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"min_50_images": result[10] or 0,
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"min_100_images": result[11] or 0,
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},
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"summary": {
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"avg_images_per_species": round(result[12] or 0, 1),
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"max_images": result[13] or 0,
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"min_images": result[14] or 0,
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"total_downloaded_images": result[15] or 0,
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},
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"recommendations": {
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"for_basic_model": f"{result[8] or 0} species with 10+ images",
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"for_good_model": f"{result[10] or 0} species with 50+ images",
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"for_excellent_model": f"{result[11] or 0} species with 100+ images",
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}
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}
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