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.
142 lines
4.2 KiB
Python
142 lines
4.2 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.playlist import Playlist
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from app.models.track import Track
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from app.models.user import User
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from app.services.recommender import build_taste_profile
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router = APIRouter(prefix="/playlists", tags=["playlists"])
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class PlaylistFixRequest(BaseModel):
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count: int = 5
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class OutlierTrack(BaseModel):
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track_number: int
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artist: str
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title: str
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reason: str
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class ReplacementTrack(BaseModel):
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title: str
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artist: str
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album: str | None = None
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reason: str
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class PlaylistFixResponse(BaseModel):
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playlist_vibe: str
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outliers: list[OutlierTrack]
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replacements: list[ReplacementTrack]
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@router.post("/{playlist_id}/fix", response_model=PlaylistFixResponse)
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async def fix_playlist(
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playlist_id: int,
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data: PlaylistFixRequest = PlaylistFixRequest(),
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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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# Load playlist
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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 not playlist:
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raise HTTPException(status_code=404, detail="Playlist not found")
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# Load tracks
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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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if not tracks:
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raise HTTPException(status_code=400, detail="Playlist has no tracks")
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# Build taste profile
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taste_profile = build_taste_profile(tracks)
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# Build numbered track list
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track_list = "\n".join(
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f"{i + 1}. {t.artist} - {t.title}" for i, t in enumerate(tracks)
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)
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count = min(max(data.count, 1), 10)
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prompt = f"""You are Vynl, a music playlist curator. Analyze this playlist and identify tracks that don't fit the overall vibe.
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Playlist: {playlist.name}
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Taste profile: {json.dumps(taste_profile, indent=2)}
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Tracks:
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{track_list}
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Analyze the playlist and respond with a JSON object:
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{{
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"playlist_vibe": "A 1-2 sentence description of the overall playlist vibe/mood",
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"outliers": [
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{{
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"track_number": 1,
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"artist": "...",
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"title": "...",
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"reason": "Why this track doesn't fit the playlist vibe"
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}}
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],
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"replacements": [
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{{
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"title": "...",
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"artist": "...",
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"album": "...",
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"reason": "Why this fits better"
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}}
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]
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}}
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Identify up to {count} outlier tracks. For each outlier, suggest a replacement that fits the playlist vibe better. Focus on maintaining sonic cohesion — same energy, tempo range, and mood.
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Return ONLY the JSON object."""
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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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# 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={user.id}|endpoint=playlist_fix")
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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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fix_data = json.loads(response_text)
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except json.JSONDecodeError:
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raise HTTPException(status_code=500, detail="Failed to parse AI response")
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return PlaylistFixResponse(
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playlist_vibe=fix_data.get("playlist_vibe", ""),
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outliers=[OutlierTrack(**o) for o in fix_data.get("outliers", [])],
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replacements=[ReplacementTrack(**r) for r in fix_data.get("replacements", [])],
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)
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