mirror of
https://github.com/bnair123/MusicAnalyser.git
synced 2026-02-25 11:46:07 +00:00
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
from fastapi import FastAPI, Depends, HTTPException, BackgroundTasks
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from sqlalchemy.orm import Session
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from datetime import datetime, timedelta
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from typing import List, Optional
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from dotenv import load_dotenv
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from .database import engine, Base, get_db
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from .models import PlayHistory as PlayHistoryModel, Track as TrackModel, AnalysisSnapshot
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from . import schemas
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from .ingest import ingest_recently_played
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from .services.stats_service import StatsService
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from .services.narrative_service import NarrativeService
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load_dotenv()
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# Create tables
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Base.metadata.create_all(bind=engine)
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app = FastAPI(title="Music Analyser Backend")
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@app.get("/")
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def read_root():
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return {"status": "ok", "message": "Music Analyser API is running"}
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@app.get("/history", response_model=List[schemas.PlayHistory])
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def get_history(limit: int = 50, db: Session = Depends(get_db)):
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history = db.query(PlayHistoryModel).order_by(PlayHistoryModel.played_at.desc()).limit(limit).all()
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return history
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@app.get("/tracks", response_model=List[schemas.Track])
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def get_tracks(limit: int = 50, db: Session = Depends(get_db)):
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tracks = db.query(TrackModel).limit(limit).all()
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return tracks
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@app.post("/trigger-ingest")
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async def trigger_ingest(background_tasks: BackgroundTasks, db: Session = Depends(get_db)):
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"""Triggers Spotify ingestion in the background."""
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background_tasks.add_task(ingest_recently_played, db)
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return {"status": "Ingestion started in background"}
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@app.post("/trigger-analysis")
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def trigger_analysis(
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days: int = 30,
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model_name: str = "gemini-2.5-flash",
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db: Session = Depends(get_db)
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):
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"""
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Runs the full analysis pipeline (Stats + LLM) for the last X days.
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Returns the computed metrics and narrative immediately.
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"""
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try:
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end_date = datetime.utcnow()
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start_date = end_date - timedelta(days=days)
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# 1. Compute Stats
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stats_service = StatsService(db)
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stats_json = stats_service.generate_full_report(start_date, end_date)
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if stats_json["volume"]["total_plays"] == 0:
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raise HTTPException(status_code=404, detail="No plays found in the specified period.")
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# 2. Generate Narrative
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narrative_service = NarrativeService(model_name=model_name)
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narrative_json = narrative_service.generate_narrative(stats_json)
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# 3. Save Snapshot
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snapshot = AnalysisSnapshot(
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period_start=start_date,
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period_end=end_date,
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period_label=f"last_{days}_days",
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metrics_payload=stats_json,
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narrative_report=narrative_json,
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model_used=model_name
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)
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db.add(snapshot)
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db.commit()
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db.refresh(snapshot)
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return {
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"status": "success",
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"snapshot_id": snapshot.id,
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"period": {"start": start_date, "end": end_date},
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"metrics": stats_json,
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"narrative": narrative_json
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}
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except Exception as e:
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print(f"Analysis Failed: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/snapshots")
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def get_snapshots(limit: int = 10, db: Session = Depends(get_db)):
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"""Retrieve past analysis snapshots."""
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return db.query(AnalysisSnapshot).order_by(AnalysisSnapshot.date.desc()).limit(limit).all() |