import os
import requests
import torch

from .vector_db import VectorDB
from .open_ai_connector import OpenAIConnector
from .parameters import *

from fastapi import FastAPI, Header, HTTPException, BackgroundTasks
from fastapi.responses import FileResponse

import logging
import sys




app = FastAPI()

logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s')
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
#logger.addHandler(logging.StreamHandler(sys.stdout))

vector_db = VectorDB(emb_model, db_location, full_actions_list_file_path, num_sub_vectors, batch_size)
open_ai_connector = OpenAIConnector()

@app.get("/find-action")
async def find_action(query: str):
    #logging.basicConfig(filename='myapp.log', level=logging.INFO)
    logger.info('Started')
    #print('start action')
    #data = vector_db.get_embedding_db_as_pandas()
    #print(data)


    prefiltered_names, prefiltered_descriptions = vector_db.retrieve_prefiltered_hits(query, K)

    logger.info('prefiltered list')
    #print(prefiltered_names)

    logger.info('start query openAI')
    response = open_ai_connector.query_open_ai(query, prefiltered_names, prefiltered_descriptions)
    logger.info(response)

    logger.info('Finished')
    return {'success': True, 'query': query, 'response': response}


@app.get("/gpu_check")
async def gpu_check():

    gpu = 'GPU not available'
    if torch.cuda.is_available():
        gpu = 'GPU is available'
        print("GPU is available")
    else:
        print("GPU is not available")

    return {'success': True, 'response': 'hello world 3', 'gpu': gpu}