f2b8dadbf8
Track estimated Anthropic API costs per request across all Claude API call sites (recommender, analyze, artist-dive, generate-playlist, crate, rabbit-hole, playlist-fix, timeline, compatibility). Log token usage and estimated cost to the app logger. Aggregate costs in admin stats endpoint and display total/today costs and token usage in the admin dashboard.
226 lines
7.5 KiB
Python
226 lines
7.5 KiB
Python
import json
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import logging
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import anthropic
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api_logger = logging.getLogger("app")
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel
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from sqlalchemy import select
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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.core.database import get_db
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from app.core.security import get_current_user
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from app.models.user import User
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from app.models.playlist import Playlist
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from app.models.track import Track
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from app.services.recommender import build_taste_profile
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router = APIRouter(prefix="/profile", tags=["profile"])
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class CompatibilityRequest(BaseModel):
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friend_email: str
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class CompatibilityResponse(BaseModel):
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friend_name: str
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compatibility_score: int
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shared_genres: list[str]
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unique_to_you: list[str]
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unique_to_them: list[str]
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shared_artists: list[str]
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insight: str
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recommendations: list[dict]
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async def _get_user_tracks(db: AsyncSession, user_id: int) -> list[Track]:
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"""Load all tracks across all playlists for a user."""
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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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return all_tracks
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def _extract_genres(tracks: list[Track]) -> set[str]:
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"""Get the set of genres from a user's tracks."""
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genres = set()
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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.add(g)
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return genres
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def _extract_artists(tracks: list[Track]) -> set[str]:
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"""Get the set of artists from a user's tracks."""
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return {t.artist for t in tracks}
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def _audio_feature_avg(tracks: list[Track], attr: str) -> float:
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"""Calculate the average of an audio feature across tracks."""
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vals = [getattr(t, attr) for t in tracks if getattr(t, attr) is not None]
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return sum(vals) / len(vals) if vals else 0.0
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def _calculate_compatibility(
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my_tracks: list[Track],
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their_tracks: list[Track],
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) -> tuple[int, list[str], list[str], list[str], list[str]]:
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"""Calculate a weighted compatibility score between two users.
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Returns (score, shared_genres, unique_to_you, unique_to_them, shared_artists).
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"""
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my_genres = _extract_genres(my_tracks)
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their_genres = _extract_genres(their_tracks)
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my_artists = _extract_artists(my_tracks)
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their_artists = _extract_artists(their_tracks)
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shared_genres = sorted(my_genres & their_genres)
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unique_to_you = sorted(my_genres - their_genres)
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unique_to_them = sorted(their_genres - my_genres)
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shared_artists = sorted(my_artists & their_artists)
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# Genre overlap (40% weight)
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all_genres = my_genres | their_genres
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genre_score = (len(shared_genres) / len(all_genres) * 100) if all_genres else 0
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# Shared artists (30% weight)
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all_artists = my_artists | their_artists
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artist_score = (len(shared_artists) / len(all_artists) * 100) if all_artists else 0
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# Audio feature similarity (30% weight)
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feature_diffs = []
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for attr in ("energy", "valence", "danceability"):
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my_avg = _audio_feature_avg(my_tracks, attr)
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their_avg = _audio_feature_avg(their_tracks, attr)
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feature_diffs.append(abs(my_avg - their_avg))
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avg_diff = sum(feature_diffs) / len(feature_diffs) if feature_diffs else 0
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feature_score = max(0, (1 - avg_diff) * 100)
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score = int(genre_score * 0.4 + artist_score * 0.3 + feature_score * 0.3)
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score = max(0, min(100, score))
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return score, shared_genres, unique_to_you, unique_to_them, shared_artists
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async def _generate_ai_insight(
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profile1: dict,
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profile2: dict,
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score: int,
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shared_genres: list[str],
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shared_artists: list[str],
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) -> tuple[str, list[dict]]:
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"""Call Claude to generate an insight and shared recommendations."""
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prompt = f"""Two music lovers want to know their taste compatibility.
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User 1 taste profile:
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{json.dumps(profile1, indent=2)}
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User 2 taste profile:
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{json.dumps(profile2, indent=2)}
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Their compatibility score is {score}%.
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Shared genres: {", ".join(shared_genres) if shared_genres else "None"}
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Shared artists: {", ".join(shared_artists) if shared_artists else "None"}
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Respond with JSON:
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{{
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"insight": "A fun 2-3 sentence description of their musical relationship",
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"recommendations": [
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{{"title": "...", "artist": "...", "reason": "Why both would love this"}}
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]
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}}
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Return ONLY the JSON. Include exactly 5 recommendations."""
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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=1024,
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messages=[{"role": "user", "content": prompt}],
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)
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# Track API cost (Haiku: $0.80/M input, $4/M output)
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input_tokens = message.usage.input_tokens
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output_tokens = message.usage.output_tokens
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cost = (input_tokens * 0.80 / 1_000_000) + (output_tokens * 4 / 1_000_000)
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api_logger.info(f"API_COST|model=claude-haiku|input={input_tokens}|output={output_tokens}|cost=${cost:.4f}|user=system|endpoint=compatibility")
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try:
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text = message.content[0].text.strip()
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if text.startswith("```"):
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text = text.split("\n", 1)[1].rsplit("```", 1)[0].strip()
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data = json.loads(text)
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return data.get("insight", ""), data.get("recommendations", [])
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except (json.JSONDecodeError, IndexError, KeyError):
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return "These two listeners have an interesting musical connection!", []
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@router.post("/compatibility", response_model=CompatibilityResponse)
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async def check_compatibility(
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data: CompatibilityRequest,
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user: User = Depends(get_current_user),
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db: AsyncSession = Depends(get_db),
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):
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"""Compare your taste profile with another user."""
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if data.friend_email.lower() == user.email.lower():
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raise HTTPException(status_code=400, detail="You can't compare with yourself!")
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# Look up the friend
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result = await db.execute(
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select(User).where(User.email == data.friend_email.lower())
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)
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friend = result.scalar_one_or_none()
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if not friend:
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raise HTTPException(
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status_code=404,
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detail="No user found with that email. They need to have a Vynl account first!",
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)
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# Load tracks for both users
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my_tracks = await _get_user_tracks(db, user.id)
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their_tracks = await _get_user_tracks(db, friend.id)
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if not my_tracks:
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raise HTTPException(
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status_code=400,
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detail="You need to import some playlists first!",
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)
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if not their_tracks:
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raise HTTPException(
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status_code=400,
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detail="Your friend hasn't imported any playlists yet!",
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)
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# Calculate compatibility
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score, shared_genres, unique_to_you, unique_to_them, shared_artists = (
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_calculate_compatibility(my_tracks, their_tracks)
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)
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# Build taste profiles for AI
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profile1 = build_taste_profile(my_tracks)
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profile2 = build_taste_profile(their_tracks)
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# Generate AI insight and recommendations
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insight, recommendations = await _generate_ai_insight(
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profile1, profile2, score, shared_genres[:10], shared_artists[:10]
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)
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return CompatibilityResponse(
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friend_name=friend.name,
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compatibility_score=score,
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shared_genres=shared_genres[:15],
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unique_to_you=unique_to_you[:10],
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unique_to_them=unique_to_them[:10],
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shared_artists=shared_artists[:15],
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insight=insight,
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recommendations=recommendations,
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)
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