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  1. handlerForAudio.py +46 -0
handlerForAudio.py ADDED
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+ import requests
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+ from typing import Dict, Any
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+ from dotenv import load_dotenv, find_dotenv
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+ import os
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+ import streamlit as st
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+ import json
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+ from textToStoryGeneration import *
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+ import logging
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+
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+ # Configure logging
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+ logging.basicConfig(level=logging.DEBUG)
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+ # Configure logging
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+ logging.basicConfig(level=logging.ERROR)
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+ # Configure logging
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+ logging.basicConfig(level=logging.WARNING)
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+
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+ load_dotenv(find_dotenv())
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+ HUGGINFACE_API = os.getenv("HUGNINGFACEHUB_API_TOKEN")
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+
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+ class CustomHandler:
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+ def __init__(self):
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+ self.model_name = "espnet/kan-bayashi_ljspeech_vits"
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+ self.endpoint = f"https://api-inference.huggingface.co/models/{self.model_name}"
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+
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+ def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
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+ # Prepare the payload with input data
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+ logging.warning(f"------input_data-- {str(data)}")
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+ payload = {"inputs": data}
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+ print("payload----", payload)
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+ # Set headers with API token
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+ headers = {"Authorization": f"Bearer {HUGGINFACE_API}"}
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+
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+ # Send POST request to the Hugging Face model endpoint
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+ response = requests.post(self.endpoint, json=payload, headers=headers)
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+ with open('StoryAudio.mp3', 'wb') as file:
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+ file.write(response.content)
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+ return 'StoryAudio.mp3'
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+ # Check if the request was successful
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+
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+
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+ # Example usage
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+ # if __name__ == "__main__":
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+ # handler = CustomHandler()
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+ # input_data = "Today I have tried with many model but I didnt find the any model which gives us better result and can be deployed on the endpoints. I think we need to Create custom Inference Handler and then it can be deployed on the interfernce end poitn.As I have deployed on model on interfernce endpoint i,e. text-to-story generation. I have also compared the result created with this endpoint and my local server as well that is not same. The endpoint is generating the different stroy."
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+ # result = handler(input_data)
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+ # print(result)dddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddv 4