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 random
import string
import time
from huggingface_hub import InferenceClient

from fastapi import Form

class Query(BaseModel):
    text: str
    code: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)







from fastapi import FastAPI, Request, Depends, UploadFile, File
from fastapi.exceptions import HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse


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")
    # requests.get("https://audiospace-1-u9912847.deta.app/sendcode")

audio_space="https://audiospace-1-u9912847.deta.app/uphoto"

# @app.post("/code")
# async def get_code(request: Request):
#     data = await request.form()
#     code = data.get("code")
#     global audio_space
#     print("code ="+code)
#     audio_space= audio_space+code

import threading
@app.post("/")
async def get_answer(q: Query ):

    text = q.text
    code= q.code
    
    
    N = 20
    res = ''.join(random.choices(string.ascii_uppercase +
                             string.digits, k=N))



    res= res+ str(time.time())

    filename= res

    t = threading.Thread(target=do_ML, args=(filename,text,code))  
    t.start()

    return JSONResponse({"id": filename})

    return "hello"


import requests
import io
import torch
import io
from PIL import Image
import json


client = InferenceClient()


    
def do_ML(filename:str,text:str,code:str):

    global client

    imagei = client.text_to_image(text)

    byte_array = io.BytesIO()
    imagei.save(byte_array, format='JPEG')
    image_bytes = byte_array.getvalue()

    
    files = {'file': image_bytes}

    global audio_space
    url = audio_space+code

    data = {"filename": filename}
    response = requests.post(url, files=files,data= data)

    print(response.text)

    if response.status_code == 200:
        print("File uploaded successfully.")
    # Handle the response as needed
    else:
        print("File upload failed.")