server.py

from fastai.collab import load_learner
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from custom_models import DotProductBias 
import asyncio
import uvicorn
import pandas as pd
import os

# 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("/recommend/{user_id}")
async def analyze(user_id: str):
    not_listened_songs = ["Revelry, Kings of Leon, 2008", "Gears, Miss May I, 2010", "Sexy Bitch, David Guetta, 2009"]
    input_dataframe = pd.DataFrame({'user_id': ["440abe26940ae9d9268157222a4a3d5735d44ed8"] * len(not_listened_songs), 'entry': not_listened_songs})
    test_dl = learn.dls.test_dl(input_dataframe)
    predictions = learn.get_preds(dl=test_dl)
    print(predictions)
    #pred = learn.predict(file)
    return {"result": predictions[0].numpy().tolist()}

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