# import firebase_admin # from firebase_admin import credentials # from firebase_admin import firestore import io from fastapi import FastAPI, File, UploadFile # from werkzeug.utils import secure_filename # import speech_recognition as sr import subprocess import os import requests import random import pandas as pd # from pydub import AudioSegment from datetime import datetime from datetime import date # import numpy as np # from sklearn.ensemble import RandomForestRegressor # import shutil import json # from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline from pydantic import BaseModel from typing import Annotated # from transformers import BertTokenizerFast, EncoderDecoderModel import torch import re # from transformers import AutoTokenizer, T5ForConditionalGeneration from fastapi import Form # from transformers import AutoModelForSequenceClassification # from transformers import TFAutoModelForSequenceClassification # from transformers import AutoTokenizer, AutoConfig # import numpy as np # from scipy.special import softmax from sentence_transformers import SentenceTransformer # model = SentenceTransformer('flax-sentence-embeddings/all_datasets_v4_MiniLM-L6') # model = SentenceTransformer("sentence-transformers/all-roberta-large-v1") # model =SentenceTransformer("intfloat/multilingual-e5-large") model = SentenceTransformer('intfloat/multilingual-e5-large') class Query(BaseModel): text: str from fastapi import FastAPI, Request, Depends, UploadFile, File from fastapi.exceptions import HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse # now = datetime.now() # UPLOAD_FOLDER = '/files' # ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', # 'jpg', 'jpeg', 'gif', 'ogg', 'mp3', 'wav'} app = FastAPI() app.add_middleware( CORSMiddleware, allow_origins=['*'], allow_credentials=True, allow_methods=['*'], allow_headers=['*'], ) # cred = credentials.Certificate('key.json') # app1 = firebase_admin.initialize_app(cred) # db = firestore.client() # data_frame = pd.read_csv('data.csv') @app.on_event("startup") async def startup_event(): print("on startup") @app.post("/") async def get_answer(q: Query ): text = q.text # text_e = model.encode(text) input_texts = [text] embeddings = model.encode(input_texts) text_e = embeddings[0] dict={ } c=0 text_e= text_e.tolist() for num in text_e: dict[c]= num c= c+1 return dict return "hello"