app.py

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

class Query(BaseModel):
    text: str


WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
model_name = "JulesBelveze/t5-small-headline-generator"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

   


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 ):

    long_text = q.text
    
    input_ids = tokenizer(
    [WHITESPACE_HANDLER(long_text)],
    return_tensors="pt",
    padding="max_length",
    truncation=True,
    max_length=384
    )["input_ids"]

    output_ids = model.generate(
    input_ids=input_ids,
    max_length=84,
    no_repeat_ngram_size=2,
    num_beams=4
    )[0]

    summary = tokenizer.decode(
    output_ids,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
    )
    return summary    
   
   
   
    return "hello"