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}