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app.py
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import gradio as gr
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from transformers import pipeline
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import os
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# Load the model
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pipe = pipeline("audio-classification", model="dima806/english_accents_classification")
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def classify_accent(audio):
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try:
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result = pipe(audio)
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if not result:
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return "<p style='color: red; font-weight: bold;'>⚠️ No prediction returned. Please try a different audio file.</p>"
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table = """
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<table style='width: 100%; border-collapse: collapse; font-family: Arial, sans-serif; margin-top: 1em;'>
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<thead>
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<tr style='border-bottom: 2px solid #4CAF50; background-color: #f2f2f2;'>
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<th style='text-align:left; padding: 8px; font-size: 1.1em; color: #333;'>Accent</th>
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<th style='text-align:left; padding: 8px; font-size: 1.1em; color: #333;'>Confidence</th>
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</tr>
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</thead>
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<tbody>
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"""
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for i, r in enumerate(result):
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label = r['label'].capitalize()
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score = f"{r['score'] * 100:.2f}%"
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if i == 0:
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row = f"""
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<tr style='background-color:#d4edda; font-weight: bold; color: #155724;'>
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<td style='padding: 8px; border-bottom: 1px solid #c3e6cb;'>{label}</td>
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<td style='padding: 8px; border-bottom: 1px solid #c3e6cb;'>{score}</td>
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</tr>
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"""
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else:
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row = f"""
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<tr style='color: #333;'>
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<td style='padding: 8px; border-bottom: 1px solid #ddd;'>{label}</td>
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<td style='padding: 8px; border-bottom: 1px solid #ddd;'>{score}</td>
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</tr>
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"""
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table += row
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table += "</tbody></table>"
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top_result = result[0]
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return f"""
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<h3 style='color: #2E7D32; font-family: Arial, sans-serif;'>
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🎤 Predicted Accent: <span style='font-weight:bold'>{top_result['label'].capitalize()}</span>
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</h3>
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{table}
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"""
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except Exception as e:
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return f"<p style='color: red; font-weight: bold;'>⚠️ Error: {str(e)}<br>Please upload a valid English audio file (e.g., .wav, .mp3).</p>"
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# Determine if it's safe to enable the submit button
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def enable_submit(audio_path):
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if audio_path is None:
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return gr.update(interactive=False)
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# If file name contains "microphone", assume it’s from mic input and delay submission
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if "microphone" in os.path.basename(audio_path).lower():
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return gr.update(interactive=True) # Enable only when recording finishes
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return gr.update(interactive=True) # File upload: enable immediately
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# Build UI
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with gr.Blocks(theme="default") as demo:
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gr.Markdown("## 🌍 English Accent Classifier\nRecord or upload English audio to detect accent.")
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audio_input = gr.Audio(type="filepath", label="🎙 Record or Upload English Audio")
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submit_button = gr.Button("Submit", interactive=False)
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result_output = gr.HTML()
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# Reactively enable submit only after audio is uploaded or recording stops
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audio_input.change(enable_submit, inputs=audio_input, outputs=submit_button)
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submit_button.click(classify_accent, inputs=audio_input, outputs=result_output)
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demo.launch(share=True)
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