Initial MVP: full-stack music discovery app

Backend (FastAPI):
- User auth with email/password and Spotify OAuth
- Spotify playlist import with audio feature extraction
- AI recommendation engine using Claude API with taste profiling
- Save/bookmark recommendations
- Rate limiting for free tier (10 recs/day, 1 playlist)
- PostgreSQL models with Alembic migrations
- Redis-ready configuration

Frontend (React 19 + TypeScript + Vite + Tailwind):
- Landing page, auth flows (email + Spotify OAuth)
- Dashboard with stats and quick discover
- Playlist management and import from Spotify
- Discover page with custom query support
- Recommendation cards with explanations and save toggle
- Taste profile visualization
- Responsive layout with mobile navigation
- PWA-ready configuration

Infrastructure:
- Docker Compose with PostgreSQL, Redis, backend, frontend
- Environment-based configuration

backend/app/schemas/recommendation.py

@@ -0,0 +1,39 @@
+from datetime import datetime
+
+from pydantic import BaseModel
+
+
+class RecommendationRequest(BaseModel):
+    playlist_id: int | None = None
+    query: str | None = None  # Manual search/request
+
+
+class RecommendationItem(BaseModel):
+    id: int
+    title: str
+    artist: str
+    album: str | None = None
+    spotify_id: str | None = None
+    preview_url: str | None = None
+    image_url: str | None = None
+    reason: str
+    score: float | None = None
+    saved: bool = False
+    created_at: datetime
+
+    model_config = {"from_attributes": True}
+
+
+class RecommendationResponse(BaseModel):
+    recommendations: list[RecommendationItem]
+    remaining_today: int | None = None  # None = unlimited (pro)
+
+
+class TasteProfile(BaseModel):
+    top_genres: list[dict]
+    avg_energy: float
+    avg_danceability: float
+    avg_valence: float
+    avg_tempo: float
+    era_preferences: list[str]
+    mood_summary: str