Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the audio classification model
+pipe = pipeline("audio-classification", model="dima806/english_accents_classification")
+
+# Define the inference function with styled, color-coded output
+def classify_accent(audio):
+    try:
+        result = pipe(audio)
+        if not result:
+            return "<p style='color: red; font-weight: bold;'>⚠️ No prediction returned. Please try a different audio file.</p>"
+
+        # Start HTML table with styling
+        table = """
+        <table style="
+            width: 100%; 
+            border-collapse: collapse; 
+            font-family: Arial, sans-serif; 
+            margin-top: 1em;
+        ">
+            <thead>
+                <tr style="border-bottom: 2px solid #4CAF50; background-color: #f2f2f2;">
+                    <th style="text-align:left; padding: 8px; font-size: 1.1em; color: #333;">Accent</th>
+                    <th style="text-align:left; padding: 8px; font-size: 1.1em; color: #333;">Confidence</th>
+                </tr>
+            </thead>
+            <tbody>
+        """
+
+        for i, r in enumerate(result):
+            label = r['label'].capitalize()
+            score = f"{r['score'] * 100:.2f}%"
+            
+            if i == 0:
+                # Highlight top accent with green background and bold text
+                row = f"""
+                <tr style="background-color:#d4edda; font-weight: bold; color: #155724;">
+                    <td style="padding: 8px; border-bottom: 1px solid #c3e6cb;">{label}</td>
+                    <td style="padding: 8px; border-bottom: 1px solid #c3e6cb;">{score}</td>
+                </tr>
+                """
+            else:
+                row = f"""
+                <tr style="color: #333;">
+                    <td style="padding: 8px; border-bottom: 1px solid #ddd;">{label}</td>
+                    <td style="padding: 8px; border-bottom: 1px solid #ddd;">{score}</td>
+                </tr>
+                """
+            table += row
+
+        table += "</tbody></table>"
+
+        top_result = result[0]
+        return f"""
+        <h3 style='color: #2E7D32; font-family: Arial, sans-serif;'>
+            🎤 Predicted Accent: <span style='font-weight:bold'>{top_result['label'].capitalize()}</span>
+        </h3>
+        {table}
+        """
+
+    except Exception as e:
+        error_message = str(e)
+        if "numpy ndarray" in error_message.lower():
+            return "<p style='color: red; font-weight: bold;'>⚠️ Error: Invalid input.<br> Please end the recording then press submit.</p>"
+        else:
+            return f"<p style='color: red; font-weight: bold;'>⚠️ Unexpected Error: {error_message}<br>Please try again with a different audio file.</p>"
+
+# Create and launch the Gradio app
+gr.Interface(
+    fn=classify_accent,
+    inputs=gr.Audio(type="filepath", label="🎙 Record or Upload English Audio"),
+    outputs=gr.HTML(),  # Use HTML to render styled output
+    title="🌍 English Accent Classifier",
+    description=(
+        "Upload or record an English audio sample to detect the speaker's accent.\n\n"
+        "**Supported accents:** American, British, Indian, African, Australian.\n"
+        "Audio Classification Model:\n" 
+        "[dima806/english_accents_classification](https://huggingface.co/dima806/english_accents_classification)\n"
+        "Dataset: https://www.kaggle.com/code/dima806/common-voice-accent-classification\n"
+    ),
+    flagging_mode="never",
+    theme="default"
+).launch(share=True)