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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 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()
# client = InferenceClient(model="SG161222/Realistic_Vision_V1.4")
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.")
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