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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)
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