from fastai.collab import load_learner
from fastapi import FastAPI, Query
from fastapi.middleware.cors import CORSMiddleware
from custom_models import DotProductBias
import asyncio
import uvicorn
import os

from tracks import get_top_tracks_for_user, get_users_with_track_interactions
from recommender import get_recommendations_for_user

# FastAPI app
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Model filename
model_filename = 'model.pkl'

async def setup_learner():
    learn = load_learner(model_filename)
    learn.dls.device = 'cpu'
    return learn

learn = None
@app.on_event("startup")
async def startup_event():
    """Setup the learner on server start"""
    global learn
    loop = asyncio.get_event_loop()  # get event loop
    tasks = [asyncio.ensure_future(setup_learner())]  # assign some task
    learn = (await asyncio.gather(*tasks))[0]

@app.get("/users")
async def get_users(limit: int = Query(10)):
    return get_users_with_track_interactions(limit=limit)

@app.get('/users/{user_id}')
async def get_user_track_history(user_id: str, limit:int = Query(5)):
    user_history = get_top_tracks_for_user(user_id, limit)
    return {"user_id": user_id, "history": user_history}

@app.get("/recommend/{user_id}")
async def get_recommendations(user_id: str, num_recommendations: int = Query(5)):
    return get_recommendations_for_user(learn, user_id, num_recommendations)

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))