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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') | |
| async def startup_event(): | |
| print("on startup") | |
| 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" | |