155cbd1bbf
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
40 lines
909 B
Python
40 lines
909 B
Python
from datetime import datetime
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from pydantic import BaseModel
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class RecommendationRequest(BaseModel):
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playlist_id: int | None = None
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query: str | None = None # Manual search/request
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class RecommendationItem(BaseModel):
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id: int
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title: str
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artist: str
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album: str | None = None
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spotify_id: str | None = None
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preview_url: str | None = None
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image_url: str | None = None
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reason: str
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score: float | None = None
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saved: bool = False
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created_at: datetime
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model_config = {"from_attributes": True}
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class RecommendationResponse(BaseModel):
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recommendations: list[RecommendationItem]
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remaining_today: int | None = None # None = unlimited (pro)
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class TasteProfile(BaseModel):
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top_genres: list[dict]
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avg_energy: float
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avg_danceability: float
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avg_valence: float
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avg_tempo: float
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era_preferences: list[str]
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mood_summary: str
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