feat: Initial backend setup for Music Analyser

- Created FastAPI backend structure.
- Implemented Spotify Recently Played ingestion logic.
- Set up SQLite database with SQLAlchemy models.
- Added AI Service using Google Gemini.
- Created helper scripts for auth and background worker.
- Added Dockerfile and GitHub Actions workflow.

backend/app/main.py

@@ -0,0 +1,36 @@
+from fastapi import FastAPI, Depends
+from sqlalchemy.orm import Session
+from .database import engine, Base, get_db
+from .models import PlayHistory as PlayHistoryModel, Track as TrackModel
+from . import schemas
+from .ingest import ingest_recently_played
+import asyncio
+from typing import List
+from dotenv import load_dotenv
+
+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.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