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# 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"
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