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app.py
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import gradio as gr
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import
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import asyncio
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import json
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import
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"""
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Process audio with streaming response via
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"""
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if not audio_path:
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yield "Please upload or record an audio file first."
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return
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try:
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# Read
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with open(audio_path, 'rb') as
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#
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# Initialize response
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#
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if data
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except Exception as e:
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yield f"Error
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# Create Gradio interface
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demo = gr.Interface(
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fn=process_audio_stream,
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inputs=[
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outputs=gr.Textbox(label="Response", interactive=False),
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title="NEXA OmniAudio-2.6B",
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description=f"""
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OmniAudio-2.6B is a compact audio-language model optimized for edge deployment.
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Model Repo: <a href="https://huggingface.co/NexaAIDev/OmniAudio-2.6B">NexaAIDev/OmniAudio-2.6B</a>
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if __name__ == "__main__":
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import gradio as gr
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import requests
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import json
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import os
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API_KEY = os.getenv("API_KEY")
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if not API_KEY:
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raise ValueError("API_KEY environment variable must be set")
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def process_audio_stream(audio_path, max_tokens):
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"""
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Process audio with streaming response via HTTP
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"""
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if not audio_path:
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yield "Please upload or record an audio file first."
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return
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try:
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# Read and prepare audio file
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with open(audio_path, 'rb') as audio_file:
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files = {
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'audio_file': ('audio.wav', audio_file, 'audio/wav')
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}
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data = {
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'prompt': "",
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'max_tokens': max_tokens
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}
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headers = {
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'X-API-Key': API_KEY
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}
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# Make streaming request
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response = requests.post(
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'https://nexa-omni.nexa4ai.com/process-audio/',
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files=files,
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data=data,
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headers=headers,
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stream=True
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)
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if response.status_code != 200:
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yield f"Error: Server returned status code {response.status_code}"
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return
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# Initialize response
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response_text = ""
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token_count = 0
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# Process the streaming response
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for line in response.iter_lines():
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if line:
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line = line.decode('utf-8')
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if line.startswith('data: '):
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try:
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data = json.loads(line[6:]) # Skip 'data: ' prefix
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if data["status"] == "generating":
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if token_count < 3 and data["token"] in [" ", " \n", "\n", "<|im_start|>", "assistant"]:
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token_count += 1
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continue
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response_text += data["token"]
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gr.update(value=response_text)
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yield response_text
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elif data["status"] == "complete":
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break
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elif data["status"] == "error":
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yield f"Error: {data['error']}"
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break
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except json.JSONDecodeError:
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continue
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except Exception as e:
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yield f"Error processing request: {str(e)}"
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# Create Gradio interface with specific queue configurations
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demo = gr.Interface(
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fn=process_audio_stream,
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inputs=[
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outputs=gr.Textbox(label="Response", interactive=False),
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title="NEXA OmniAudio-2.6B",
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description=f"""
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OmniAudio-2.6B is a compact audio-language model optimized for edge deployment.
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Model Repo: <a href="https://huggingface.co/NexaAIDev/OmniAudio-2.6B">NexaAIDev/OmniAudio-2.6B</a>
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)
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if __name__ == "__main__":
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# Configure the queue for better streaming performance
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demo.queue(
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max_size=20,
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).launch(
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server_name="0.0.0.0",
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server_port=7860,
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)
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