Spaces:
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Update app.py
Browse files
app.py
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@@ -824,56 +824,57 @@ def project_extraction(project_description):
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# task_analysis, reasoning_path = generate_reasoning_path(project_description, task_description)
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# steps = store_and_execute_task(task_description, reasoning_path)
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def message_back(task_message, execution_status, doc_url, from_whatsapp):
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# Convert task steps to a simple numbered list
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task_steps_list = "\n".join(
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[f"{i + 1}. {step['action']} - {step.get('output', '')}" for i, step in enumerate(execution_status.to_dict(orient="records"))]
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)
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except Exception as e:
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logger.error(f"Twilio Error: {e}")
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raise HTTPException(status_code=500, detail=f"Error sending WhatsApp message: {str(e)}")
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#
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whisper_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-medium")
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#
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try:
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media_response = requests.get(media_url, auth=HTTPBasicAuth(account_sid, auth_token))
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# Download the media file
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media_response.raise_for_status()
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audio_data = media_response.content
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# In[18]:
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# task_analysis, reasoning_path = generate_reasoning_path(project_description, task_description)
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# steps = store_and_execute_task(task_description, reasoning_path)
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# def message_back(task_message, execution_status, doc_url, from_whatsapp):
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# # Convert task steps to a simple numbered list
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# task_steps_list = "\n".join(
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# [f"{i + 1}. {step['action']} - {step.get('output', '')}" for i, step in enumerate(execution_status.to_dict(orient="records"))]
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# )
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# # Format the body message
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# body_message = (
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# f"*Task Message:*\n{task_message}\n\n"
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# f"*Execution Status:*\n{task_steps_list}\n\n"
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# f"*Doc URL:*\n{doc_url}\n\n"
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# )
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# # Send response back to WhatsApp
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# try:
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# twillo_client.messages.create(
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# from_=twilio_phone_number,
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# to=from_whatsapp,
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# body=body_message
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# )
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# except Exception as e:
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# logger.error(f"Twilio Error: {e}")
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# raise HTTPException(status_code=500, detail=f"Error sending WhatsApp message: {str(e)}")
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# return {"status": "success"}
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# # Initialize the Whisper pipeline
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# whisper_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-medium")
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# # Function to transcribe audio from a media URL
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# def transcribe_audio_from_media_url(media_url):
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# try:
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# media_response = requests.get(media_url, auth=HTTPBasicAuth(account_sid, auth_token))
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# # Download the media file
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# media_response.raise_for_status()
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# audio_data = media_response.content
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# # Save the audio data to a file for processing
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# audio_file_path = "temp_audio_file.mp3"
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# with open(audio_file_path, "wb") as audio_file:
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# audio_file.write(audio_data)
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# # Transcribe the audio using Whisper
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# transcription = whisper_pipeline(audio_file_path, return_timestamps=True)
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# logger.debug(f"Transcription: {transcription['text']}")
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# return transcription["text"]
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# except Exception as e:
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# logger.error(f"An error occurred: {e}")
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# return None
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# In[18]:
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