Merge branch 'gradio-modal-integration' into 'main'
Browse filesRefactor: Switch to Modal framework for audio processing and storage
See merge request sonne-technology/bsod-tv/waveform-matching-gradio-front-end!1
app.py
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import wave
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import contextlib
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import
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def process_audio(original_audio_path, dubbed_audio_path, email, company_name, tolerance):
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"""
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@@ -10,6 +19,11 @@ def process_audio(original_audio_path, dubbed_audio_path, email, company_name, t
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file upload to presigned URLs, and triggering the processing.
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"""
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# 1. Check the duration of both audio files.
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try:
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with contextlib.closing(wave.open(original_audio_path, 'r')) as f:
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frames = f.getnframes()
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@@ -26,71 +40,29 @@ def process_audio(original_audio_path, dubbed_audio_path, email, company_name, t
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except Exception as e:
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return f"Error reading audio files: {e}"
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# --- ACTION REQUIRED ---
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# Please replace the following placeholder URLs with your actual API endpoints.
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presigned_url_endpoint = "https://your-api.com/get-presigned-urls" # TODO: Change URL
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processing_endpoint = "https://your-api.com/trigger-processing" # TODO: Change URL
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# --------------------------
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# 2.1. Get presigned URLs from your endpoint. # TODO: Change Payload
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payload = {
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"files": [
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{"name": os.path.basename(original_audio_path), "type": "audio/wav"},
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{"name": os.path.basename(dubbed_audio_path), "type": "audio/wav"}
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]
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}
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try:
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print(f"Requesting presigned URLs from: {presigned_url_endpoint}")
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response = requests.post(presigned_url_endpoint, json=payload)
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response.raise_for_status() # Raise an exception for bad status codes
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presigned_data = response.json()
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# IMPORTANT: Adjust the following lines based on the actual JSON response
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# structure of your presigned URL endpoint.
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# This example assumes a response like:
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# {"original_url": "...", "dubbed_url": "..."}
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original_upload_url = presigned_data['original_url']
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dubbed_upload_url = presigned_data['dubbed_url']
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except requests.exceptions.RequestException as e:
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return f"Error getting presigned URLs: {e}"
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except KeyError:
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return "Error: Could not parse the presigned URL response. Please check the JSON structure."
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# 2.2. Upload the audio files to the presigned URLs. # TODO: Check for PUT accuracy
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try:
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with
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upload_response.raise_for_status()
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except requests.exceptions.RequestException as e:
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return f"Error uploading files: {e}"
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# 3. Call the endpoint to trigger the processing.
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processing_payload = { # TODO: Change Payload
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"email": email,
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"company_name": company_name,
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"tolerance": tolerance,
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# The keys here ('original_file', 'dubbed_file') should match what your
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# processing API expects.
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"original_file": original_upload_url,
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"dubbed_file": dubbed_upload_url
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}
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try:
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# Create the Gradio interface for the application.
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if __name__ == "__main__":
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# To run this file locally, you'll need to install gradio and requests:
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# pip install gradio requests
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demo.launch()
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## ENVIRONMENT VARIABLES
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# MODAL_VOLUME
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# MODAL_TOKEN_ID
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# MODAL_ENVIRONMENT
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# MODAL_TOKEN_SECRET
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import os
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import time
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import wave
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import modal
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import contextlib
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import gradio as gr
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def process_audio(original_audio_path, dubbed_audio_path, email, company_name, tolerance):
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"""
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file upload to presigned URLs, and triggering the processing.
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"""
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# 1. Check the duration of both audio files.
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modal_token_id = os.environ['MODAL_TOKEN_ID']
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modal_token_secret = os.environ['MODAL_TOKEN_SECRET']
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modal_environment = os.environ['MODAL_ENVIRONMENT']
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modal_volume = os.environ['MODAL_VOLUME']
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processing_id = str(int(time.time()))
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try:
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with contextlib.closing(wave.open(original_audio_path, 'r')) as f:
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frames = f.getnframes()
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except Exception as e:
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return f"Error reading audio files: {e}"
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# 2. Upload Audio Files to Modal Storage
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try:
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bsodtv_storage = modal.Volume.from_name(modal_volume)
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with bsodtv_storage.batch_upload() as batch:
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batch.put_file(original_audio_path, "/{}/original_audio.wav".format(processing_id))
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batch.put_file(dubbed_audio_path, "/{}/original_audio.wav".format(processing_id))
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bsodtv_storage.commit()
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except:
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return "Error uploading audio files to Cloud Storage."
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# 3. Call modal to trigger processing
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try:
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waveform_matching_function = modal.Function.from_name("Waveform-Matching", "reception_handler")
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waveform_matching_function.spawn(
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processing_id=processing_id,
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original_file="/{}/original_audio.wav".format(processing_id),
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dubbed_file="/{}/original_audio.wav".format(processing_id),
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email=email,
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company_name=company_name,
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tolerance_percentage=tolerance
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)
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except:
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return "Error calling Outpost to trigger processing."
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return "Processing started. Results will be emailed to you shortly."
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# Create the Gradio interface for the application.
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if __name__ == "__main__":
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# To run this file locally, you'll need to install gradio and requests:
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# pip install gradio requests
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demo.launch()
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