Delete async_ai_voice.py

async_ai_voice.py

@@ -1,60 +0,0 @@
-import aiohttp  # Import aiohttp for making asynchronous HTTP requests
-import os        # Import os to access environment variables
-import json      # Import json to handle JSON data
-from typing import Dict, Any  # Import type hints for better code clarity
-
-# Define the API endpoint URL for the Hugging Face Whisper model
-API_URL = "https://api-inference.huggingface.co/models/distil-whisper/distil-large-v3"
-
-# Set up the headers with the authorization token retrieved from environment variables
-headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
-
-async def send_to_voice_AI(audio_data: bytes) -> Dict[str, Any]:
-    """
-    Asynchronously sends audio data to the Hugging Face Whisper model for transcription.
-
-    Args:
-        audio_data (bytes): The raw audio data to be transcribed.
-
-    Returns:
-        Dict[str, Any]: A dictionary containing the transcription result or error message.
-    """
-    print("Sending to voice AI")  # Log the action for debugging purposes
-
-    # Create an asynchronous HTTP session using aiohttp
-    async with aiohttp.ClientSession() as session:
-        try:
-            # Asynchronously send a POST request to the API_URL with headers and audio data
-            async with session.post(API_URL, headers=headers, data=audio_data) as response:
-                # Await the response to ensure the request completes before proceeding
-                if response.status == 200:
-                    # If the response is successful, await and return the JSON content
-                    return await response.json()
-                else:
-                    # If the response status is not 200, await the text of the response
-                    error_text = await response.text()
-                    # Parse the error text into a JSON object
-                    error_json = json.loads(error_text)
-                    # Return a dictionary with an error message and estimated loading time
-                    return {
-                        "text": f"error: {error_json.get('error', 'Unknown error')}\n"
-                                f"Estimated time to load: {error_json.get('estimated_time', 'unknown')} seconds"
-                    }
-        except aiohttp.ClientError as e:
-            # Handle any client-related errors that occur during the request
-            return {"text": f"Network error: {str(e)}"}
-        except json.JSONDecodeError:
-            # Handle errors that occur if the response cannot be parsed as JSON
-            return {"text": "Error: Unable to parse API response"}
-
-# Example usage:
-# The following example demonstrates how to use the send_to_voice_AI function asynchronously.
-# It reads an audio file, sends it to the voice AI, and prints the output.
-
-# import asyncio  # Import asyncio to run the asynchronous function
-# async def example():
-#     with open("sample1.flac", "rb") as f:  # Open the audio file in binary read mode
-#         audio_data = f.read()  # Read the audio data from the file
-#     output = await send_to_voice_AI(audio_data)  # Await the asynchronous transcription function
-#     print(output)  # Print the transcription result
-# asyncio.run(example())  # Run the asynchronous example function