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/models.py

@@ -0,0 +1,39 @@
+from sqlalchemy import Column, Integer, String, DateTime, JSON, ForeignKey, Boolean
+from sqlalchemy.orm import relationship
+from datetime import datetime
+from .database import Base
+
+class Track(Base):
+    __tablename__ = "tracks"
+
+    id = Column(String, primary_key=True, index=True) # Spotify ID
+    name = Column(String)
+    artist = Column(String)
+    album = Column(String)
+    duration_ms = Column(Integer)
+    popularity = Column(Integer, nullable=True)
+
+    # Store raw full JSON response for future-proofing analysis
+    raw_data = Column(JSON, nullable=True)
+
+    # AI Analysis fields
+    lyrics_summary = Column(String, nullable=True)
+    genre_tags = Column(String, nullable=True) # JSON list stored as string or just raw JSON
+
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+    plays = relationship("PlayHistory", back_populates="track")
+
+
+class PlayHistory(Base):
+    __tablename__ = "play_history"
+
+    id = Column(Integer, primary_key=True, index=True)
+    track_id = Column(String, ForeignKey("tracks.id"))
+    played_at = Column(DateTime, index=True) # The timestamp from Spotify
+
+    # Context (album, playlist, etc.)
+    context_uri = Column(String, nullable=True)
+
+    track = relationship("Track", back_populates="plays")