1eea237c08
- Discovery modes: Sonic Twin, Era Bridge, Deep Cuts, Rising Artists - Discovery dial (Safe to Adventurous slider) - Block genres/moods exclusion - Thumbs down/dislike on recommendations - My Taste page with Genre DNA breakdown, audio feature meters, listening personality - Updated pricing: Free (5/week), Premium ($6.99/mo), Family ($12.99/mo coming soon) - Weekly rate limiting instead of daily - Alembic migration for new fields
259 lines
10 KiB
Python
259 lines
10 KiB
Python
import json
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from datetime import datetime, timezone, timedelta
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import anthropic
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from sqlalchemy import select, func
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.config import settings
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from app.models.track import Track
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from app.models.playlist import Playlist
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from app.models.recommendation import Recommendation
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from app.models.user import User
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def build_taste_profile(tracks: list[Track]) -> dict:
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"""Analyze tracks to build a taste profile summary."""
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if not tracks:
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return {}
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genres_count: dict[str, int] = {}
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total_energy = 0.0
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total_dance = 0.0
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total_valence = 0.0
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total_tempo = 0.0
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count_features = 0
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for t in tracks:
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if t.genres:
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for g in t.genres:
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genres_count[g] = genres_count.get(g, 0) + 1
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if t.energy is not None:
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total_energy += t.energy
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total_dance += t.danceability or 0
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total_valence += t.valence or 0
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total_tempo += t.tempo or 0
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count_features += 1
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top_genres = sorted(genres_count.items(), key=lambda x: x[1], reverse=True)[:10]
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n = max(count_features, 1)
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return {
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"top_genres": [{"name": g, "count": c} for g, c in top_genres],
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"avg_energy": round(total_energy / n, 3),
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"avg_danceability": round(total_dance / n, 3),
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"avg_valence": round(total_valence / n, 3),
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"avg_tempo": round(total_tempo / n, 1),
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"track_count": len(tracks),
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"sample_artists": list({t.artist for t in tracks[:20]}),
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"sample_tracks": [f"{t.artist} - {t.title}" for t in tracks[:15]],
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}
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async def get_weekly_rec_count(db: AsyncSession, user_id: int) -> int:
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"""Count recommendations generated this week (since Monday) for rate limiting."""
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now = datetime.now(timezone.utc)
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week_start = (now - timedelta(days=now.weekday())).replace(hour=0, minute=0, second=0, microsecond=0)
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result = await db.execute(
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select(func.count(Recommendation.id)).where(
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Recommendation.user_id == user_id,
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Recommendation.created_at >= week_start,
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)
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)
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return result.scalar() or 0
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MODE_PROMPTS = {
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"discover": "Find music they'll love. Mix well-known and underground artists.",
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"sonic_twin": "Find underground or lesser-known artists who sound nearly identical to their favorites. Focus on artists under 100K monthly listeners who share the same sonic qualities — similar vocal style, production approach, tempo, and energy.",
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"era_bridge": "Suggest classic artists from earlier eras who directly inspired their current favorites. Trace musical lineage — if they love Tame Impala, suggest the 70s psych rock that influenced him. Bridge eras.",
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"deep_cuts": "Find B-sides, album tracks, rarities, and lesser-known songs from artists already in their library. Focus on tracks they probably haven't heard even from artists they already know.",
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"rising": "Find artists with under 50K monthly listeners who match their taste. Focus on brand new, up-and-coming artists who haven't broken through yet. Think artists who just released their debut album or EP.",
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}
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def build_adventurousness_prompt(level: int) -> str:
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if level <= 2:
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return "Stick very close to their existing taste. Recommend artists who are very similar to what they already listen to."
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elif level == 3:
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return "Balance familiar and new. Mix artists similar to their taste with some that push boundaries."
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else:
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return "Be adventurous. Recommend artists that are different from their usual taste but share underlying qualities they'd appreciate. Push boundaries."
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async def generate_recommendations(
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db: AsyncSession,
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user: User,
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playlist_id: int | None = None,
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query: str | None = None,
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bandcamp_mode: bool = False,
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mode: str = "discover",
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adventurousness: int = 3,
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exclude: str | None = None,
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) -> tuple[list[Recommendation], int | None]:
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"""Generate AI music recommendations using Claude."""
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# Rate limit check for free users
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remaining = None
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if not user.is_pro:
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used_this_week = await get_weekly_rec_count(db, user.id)
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remaining = max(0, settings.FREE_WEEKLY_RECOMMENDATIONS - used_this_week)
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if remaining <= 0:
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return [], 0
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# Gather context
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taste_context = ""
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existing_tracks = set()
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if playlist_id:
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result = await db.execute(
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select(Playlist).where(Playlist.id == playlist_id, Playlist.user_id == user.id)
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)
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playlist = result.scalar_one_or_none()
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if playlist:
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result = await db.execute(
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select(Track).where(Track.playlist_id == playlist.id)
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)
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tracks = list(result.scalars().all())
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existing_tracks = {f"{t.artist} - {t.title}".lower() for t in tracks}
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profile = build_taste_profile(tracks)
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taste_context = f"Taste profile from playlist '{playlist.name}':\n{json.dumps(profile, indent=2)}"
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else:
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# Gather from all user playlists
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result = await db.execute(
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select(Playlist).where(Playlist.user_id == user.id)
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)
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playlists = list(result.scalars().all())
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all_tracks = []
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for p in playlists:
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result = await db.execute(select(Track).where(Track.playlist_id == p.id))
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all_tracks.extend(result.scalars().all())
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existing_tracks = {f"{t.artist} - {t.title}".lower() for t in all_tracks}
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if all_tracks:
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profile = build_taste_profile(all_tracks)
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taste_context = f"Taste profile from {len(all_tracks)} tracks:\n{json.dumps(profile, indent=2)}"
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# Load disliked artists to exclude
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disliked_result = await db.execute(
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select(Recommendation.artist).where(
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Recommendation.user_id == user.id,
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Recommendation.disliked == True,
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)
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)
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disliked_artists = list({a for a in disliked_result.scalars().all()})
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# Build prompt
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user_request = query or "Find me music I'll love based on my taste profile. Prioritize lesser-known artists and hidden gems."
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if bandcamp_mode:
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focus_instruction = "IMPORTANT: Strongly prioritize independent and underground artists who release music on Bandcamp. Think DIY, indie labels, self-released artists, and the kind of music you'd find crate-digging on Bandcamp. Focus on artists who self-publish or release on small indie labels."
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else:
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focus_instruction = "Focus on discovery - prioritize lesser-known artists, deep cuts, and hidden gems over obvious popular choices."
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# Mode-specific instruction
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mode_instruction = MODE_PROMPTS.get(mode, MODE_PROMPTS["discover"])
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# Adventurousness instruction
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adventurousness_instruction = build_adventurousness_prompt(adventurousness)
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# Exclude genres instruction
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exclude_instruction = ""
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combined_exclude = exclude or ""
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if user.blocked_genres:
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combined_exclude = f"{user.blocked_genres}, {combined_exclude}" if combined_exclude else user.blocked_genres
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if combined_exclude.strip():
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exclude_instruction = f"\nDo NOT recommend anything in these genres/moods: {combined_exclude}"
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# Disliked artists exclusion
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disliked_instruction = ""
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if disliked_artists:
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disliked_instruction = f"\nDo NOT recommend anything by these artists (user disliked them): {', '.join(disliked_artists[:30])}"
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prompt = f"""You are Vynl, an AI music discovery assistant. You help people discover new music they'll love.
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{taste_context}
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User request: {user_request}
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Discovery mode: {mode_instruction}
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{adventurousness_instruction}
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Already in their library (do NOT recommend these):
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{', '.join(list(existing_tracks)[:50]) if existing_tracks else 'None provided'}
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{disliked_instruction}
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{exclude_instruction}
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Respond with exactly {"15" if bandcamp_mode else "5"} music recommendations as a JSON array. Each item should have:
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- "title": song title
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- "artist": artist name
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- "album": album name (if known)
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- "reason": A warm, personal 2-3 sentence explanation of WHY they'll love this track. Reference specific qualities from their taste profile. Be specific about sonic qualities, not generic.
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- "score": confidence score 0.0-1.0
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{focus_instruction}
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Return ONLY the JSON array, no other text."""
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# Call Claude API
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client = anthropic.Anthropic(api_key=settings.ANTHROPIC_API_KEY)
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message = client.messages.create(
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model="claude-haiku-4-5-20251001",
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max_tokens=2000,
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messages=[{"role": "user", "content": prompt}],
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)
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# Parse response
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response_text = message.content[0].text.strip()
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# Handle potential markdown code blocks
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if response_text.startswith("```"):
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response_text = response_text.split("\n", 1)[1]
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response_text = response_text.rsplit("```", 1)[0]
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try:
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recs_data = json.loads(response_text)
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except json.JSONDecodeError:
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return [], remaining
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from app.services.bandcamp import search_bandcamp_verified
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# Save to DB — in bandcamp mode, only keep results verified on Bandcamp
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recommendations = []
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for rec in recs_data:
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if len(recommendations) >= 5:
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break
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bandcamp_url = None
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if bandcamp_mode:
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try:
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match = await search_bandcamp_verified(
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rec.get("artist", ""), rec.get("title", "")
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)
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if match:
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bandcamp_url = match.get("bandcamp_url")
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else:
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# Not verified on Bandcamp — skip
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continue
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except Exception:
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continue
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r = Recommendation(
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user_id=user.id,
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playlist_id=playlist_id,
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title=rec.get("title", "Unknown"),
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artist=rec.get("artist", "Unknown"),
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album=rec.get("album"),
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reason=rec.get("reason", ""),
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score=rec.get("score"),
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query=query,
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bandcamp_url=bandcamp_url,
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)
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db.add(r)
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recommendations.append(r)
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await db.flush()
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if remaining is not None:
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remaining = max(0, remaining - len(recommendations))
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return recommendations, remaining
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