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"],
}

@app.post("/detect-mood")
def detect_mood(input: TextInput):
    vector = encoder.encode([input.text])
    mood = model.predict(vector)[0]
    return {"mood": mood}

import random

@app.post("/recommendations")
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
    }