import logging
import requests
import os
import pandas as pd

from fastapi import FastAPI
from rec_handler import Recommendations

PATH_TO_RECOMENDATIONS = '../../recommendations/'
FILENAME_PERS_RECOMENDATIONS = 'personal_als.parquet'
FILENAME_TOP_POPULAR = 'top_popular.parquet'

logger = logging.getLogger("uvicorn.error")

rec_store = Recommendations()
features_store_url = "http://localhost:8010"
events_store_url = "http://localhost:8020" 


logger.info("Starting")

rec_store.load(
"personal",
PATH_TO_RECOMENDATIONS+FILENAME_PERS_RECOMENDATIONS,
columns=["user_id", "item_id", "score"],
)
rec_store.load(
    "default",
    PATH_TO_RECOMENDATIONS+FILENAME_TOP_POPULAR,
    columns=["item_id", "rank"],
)


# создаём приложение FastAPI
app = FastAPI(title="recommendations")


@app.post("/recommendations_offline")
async def recommendations_offline(user_id: int, k: int = 100):
    """
    Возвращает список рекомендаций длиной k для пользователя user_id
    """

    recs = rec_store.get(user_id=user_id, k=k)
    return {"recs": recs}


@app.post("/recommendations_online")
async def recommendations_online(user_id: int, k: int = 100):
    """
    Возвращает список онлайн-рекомендаций длиной k для пользователя user_id
    """

    headers = {"Content-type": "application/json", "Accept": "text/plain"}

    # получаем последние события пользователя
    params = {"user_id": user_id, "k": 3}
    resp = requests.get(events_store_url + "/get", headers=headers, params=params)
    events = resp.json()
    events = events["events"]

    # получаем список похожих объектов
    if len(events) > 0:
        items = []
        scores = []
        for item_id in events:
            params = {"item_id": item_id, "k": k}
            headers = {"Content-type": "application/json", "Accept": "text/plain"}
            resp = requests.get(features_store_url + "/similar_items", headers=headers, params=params)
            if resp.status_code == 200:
                similar_items = resp.json()
            else:
                similar_items = None
                print(f"status code: {resp.status_code}")
            items += similar_items["item_id_2"]
            scores += similar_items["score"]
        combined = list(zip(items, scores))
        combined = sorted(combined, key=lambda x: x[1], reverse=True)
        combined = [item for item, _ in combined]
        recs = combined[:k]

    else:
        recs = []

    return {"recs": recs} 


@app.post("/recommendations")
async def recommendations(user_id: int, k: int = 100):
    """
    Возвращает список рекомендаций длиной k для пользователя user_id
    """

    recs_offline = await recommendations_offline(user_id, k)
    recs_online = await recommendations_online(user_id, k)

    recs_offline = recs_offline["recs"]
    recs_online = recs_online["recs"]
    recs_blended = []

    min_length = min(len(recs_offline), len(recs_online))
    # чередуем элементы из списков, пока позволяет минимальная длина
    for i in range(min_length):
        recs_blended.append(recs_online[i])
        recs_blended.append(recs_offline[i])

    # добавляем оставшиеся элементы в конец
    recs_blended += recs_online[min_length:]
    recs_blended += recs_offline[min_length:]

    # оставляем только первые k рекомендаций
    recs_blended = recs_blended[:k]

    return {"recs": recs_blended}