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, AnalysisSnapshot
from . import schemas
from .ingest import ingest_recently_played
from .services.stats_service import StatsService
from .services.narrative_service import NarrativeService

load_dotenv()

# Create tables
Base.metadata.create_all(bind=engine)

app = FastAPI(title="Music Analyser Backend")

@app.get("/")
def read_root():
    return {"status": "ok", "message": "Music Analyser API is running"}

@app.get("/history", response_model=List[schemas.PlayHistory])
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.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()