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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 threading | |
| import random | |
| import string | |
| import time | |
| from fastapi import Form | |
| class Query(BaseModel): | |
| text: str | |
| class Query2(BaseModel): | |
| text: str | |
| host:str | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization') | |
| # model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device) | |
| summarizer = pipeline( | |
| "summarization", | |
| "pszemraj/long-t5-tglobal-base-16384-book-summary", | |
| device=0 if torch.cuda.is_available() else -1, | |
| ) | |
| def generate_summary(text): | |
| result = summarizer(text,max_length=10000) | |
| return result[0]["summary_text"] | |
| # cut off at BERT max length 512 | |
| # inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt") | |
| # input_ids = inputs.input_ids.to(device) | |
| # attention_mask = inputs.attention_mask.to(device) | |
| # output = model.generate(input_ids, attention_mask=attention_mask) | |
| # return tokenizer.decode(output[0], skip_special_tokens=True) | |
| 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 ): | |
| long_text = q.text | |
| r= generate_summary(long_text) | |
| return r | |
| return "hello" | |
| async def get_answer(q: Query2 ): | |
| N = 20 | |
| res = ''.join(random.choices(string.ascii_uppercase + | |
| string.digits, k=N)) | |
| res= res+ str(time.time()) | |
| id= res | |
| text = q.text | |
| host= q.host | |
| t = threading.Thread(target=do_ML, args=(id,text,host)) | |
| t.start() | |
| return JSONResponse({"id":id}) | |
| return "hello" | |
| import requests | |
| def do_ML(id:str,long_text:str,host:str): | |
| try: | |
| r= generate_summary(long_text) | |
| data={"id":id,"result":r} | |
| x=requests.post(host,data= data) | |
| print(x.text) | |
| except: | |
| print("Error occured id= "+id) | |