Fixed and added all the stats_service.py methods

backend/app/main.py

@@ -1,12 +1,15 @@
-from fastapi import FastAPI, Depends
+from fastapi import FastAPI, Depends, HTTPException, BackgroundTasks
 from sqlalchemy.orm import Session
+from datetime import datetime, timedelta
+from typing import List, Optional
+from dotenv import load_dotenv
+
 from .database import engine, Base, get_db
-from .models import PlayHistory as PlayHistoryModel, Track as TrackModel
+from .models import PlayHistory as PlayHistoryModel, Track as TrackModel, AnalysisSnapshot
 from . import schemas
 from .ingest import ingest_recently_played
-import asyncio
-from typing import List
-from dotenv import load_dotenv
+from .services.stats_service import StatsService
+from .services.narrative_service import NarrativeService
 
 load_dotenv()
 
@@ -24,13 +27,68 @@ def get_history(limit: int = 50, db: Session = Depends(get_db)):
     history = db.query(PlayHistoryModel).order_by(PlayHistoryModel.played_at.desc()).limit(limit).all()
     return history
 
-@app.post("/trigger-ingest")
-async def trigger_ingest(db: Session = Depends(get_db)):
-    """Manually trigger the ingestion process (useful for testing)"""
-    await ingest_recently_played(db)
-    return {"status": "Ingestion triggered"}
-
 @app.get("/tracks", response_model=List[schemas.Track])
 def get_tracks(limit: int = 50, db: Session = Depends(get_db)):
     tracks = db.query(TrackModel).limit(limit).all()
     return tracks
+
+@app.post("/trigger-ingest")
+async def trigger_ingest(background_tasks: BackgroundTasks, db: Session = Depends(get_db)):
+    """Triggers Spotify ingestion in the background."""
+    background_tasks.add_task(ingest_recently_played, db)
+    return {"status": "Ingestion started in background"}
+
+@app.post("/trigger-analysis")
+def trigger_analysis(
+    days: int = 30,
+    model_name: str = "gemini-2.5-flash",
+    db: Session = Depends(get_db)
+):
+    """
+    Runs the full analysis pipeline (Stats + LLM) for the last X days.
+    Returns the computed metrics and narrative immediately.
+    """
+    try:
+        end_date = datetime.utcnow()
+        start_date = end_date - timedelta(days=days)
+
+        # 1. Compute Stats
+        stats_service = StatsService(db)
+        stats_json = stats_service.generate_full_report(start_date, end_date)
+
+        if stats_json["volume"]["total_plays"] == 0:
+             raise HTTPException(status_code=404, detail="No plays found in the specified period.")
+
+        # 2. Generate Narrative
+        narrative_service = NarrativeService(model_name=model_name)
+        narrative_json = narrative_service.generate_narrative(stats_json)
+
+        # 3. Save Snapshot
+        snapshot = AnalysisSnapshot(
+            period_start=start_date,
+            period_end=end_date,
+            period_label=f"last_{days}_days",
+            metrics_payload=stats_json,
+            narrative_report=narrative_json,
+            model_used=model_name
+        )
+        db.add(snapshot)
+        db.commit()
+        db.refresh(snapshot)
+
+        return {
+            "status": "success",
+            "snapshot_id": snapshot.id,
+            "period": {"start": start_date, "end": end_date},
+            "metrics": stats_json,
+            "narrative": narrative_json
+        }
+
+    except Exception as e:
+        print(f"Analysis Failed: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/snapshots")
+def get_snapshots(limit: int = 10, db: Session = Depends(get_db)):
+    """Retrieve past analysis snapshots."""
+    return db.query(AnalysisSnapshot).order_by(AnalysisSnapshot.date.desc()).limit(limit).all()