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Runtime error
Runtime error
| from fastapi import FastAPI | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from pydantic import BaseModel | |
| import joblib | |
| from sentence_transformers import SentenceTransformer | |
| import requests | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| app = FastAPI() | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], # update to your frontend URL after deployment | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| # loads fine on HF Spaces — no size limit like Render | |
| model = joblib.load("emotion_song_model.pkl") | |
| encoder = SentenceTransformer("all-MiniLM-L6-v2") | |
| LASTFM_API_KEY = os.getenv("LASTFM_API_KEY") | |
| class TextInput(BaseModel): | |
| text: str | |
| mood_to_tags = { | |
| "happy": ["happy", "pop"], | |
| "sad": ["sad", "melancholy"], | |
| "angry": ["metal", "rock"], | |
| "anxious": ["chill", "ambient"], | |
| "calm": ["ambient", "acoustic"], | |
| "romantic": ["love", "romance"], | |
| } | |
| def detect_mood(input: TextInput): | |
| vector = encoder.encode([input.text]) | |
| mood = model.predict(vector)[0] | |
| return {"mood": mood} | |
| import random | |
| def get_recommendations(input: TextInput): | |
| vector = encoder.encode([input.text]) | |
| mood = model.predict(vector)[0] | |
| tags = mood_to_tags[mood] | |
| all_tracks = [] | |
| for tag in tags: | |
| response = requests.get( | |
| "http://ws.audioscrobbler.com/2.0/", | |
| params={ | |
| "method": "tag.getTopTracks", | |
| "tag": tag, | |
| "api_key": LASTFM_API_KEY, | |
| "format": "json", | |
| "limit": 25, | |
| } | |
| ) | |
| data = response.json() | |
| if "tracks" in data and "track" in data["tracks"]: | |
| all_tracks.extend(data["tracks"]["track"]) | |
| # remove duplicates | |
| seen = set() | |
| unique_tracks = [] | |
| for track in all_tracks: | |
| key = ( | |
| track["name"].lower(), | |
| track["artist"]["name"].lower() | |
| ) | |
| if key not in seen: | |
| seen.add(key) | |
| unique_tracks.append(track) | |
| # randomize playlist | |
| random.shuffle(unique_tracks) | |
| # return only 10 songs | |
| unique_tracks = unique_tracks[:10] | |
| songs = [ | |
| { | |
| "name": track["name"], | |
| "artist": track["artist"]["name"], | |
| "url": track["url"], | |
| } | |
| for track in unique_tracks | |
| ] | |
| return { | |
| "mood": mood, | |
| "songs": songs | |
| } | |