recommendation-api/server.py

from fastapi import FastAPI, Query
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
from learner import setup_learner, custom_accuracy # Note that DotProductBias must be imported to global namespace

app = FastAPI()
model_filename = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'model.pkl')
learn = None

@app.on_event("startup")
async def startup_event():
    global learn
    tasks = [asyncio.ensure_future(setup_learner(model_filename))]  # 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, limit: int = Query(5)):
    return get_recommendations_for_user(learn, user_id, limit)

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