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app.py

@@ -0,0 +1,108 @@
+import gradio as gr
+from audio import predict_all  # This is your custom module for predictions
+import re  # Regular expressions for text processing
+
+# Additional CSS for styling the confidence bars and the result layout
+additional_css = """
+/* CSS for the confidence bars */
+.confidence-section {
+    display: flex;
+    align-items: center;
+    margin-top: 10px;
+}
+
+.confidence-label {
+    margin-right: 10px;
+    font-weight: bold;
+}
+.confidence-bar {
+    height: 20px;
+    width: 100%;
+    background-color: #eee;
+    border-radius: 10px;
+    margin: 10px 0;
+}
+
+.confidence-fill {
+    height: 100%;
+    border-radius: 10px;
+    background-color: #4caf50; /* Change color based on confidence level if desired */
+    text-align: center;
+    color: white;
+    line-height: 20px;
+}
+/* Additional CSS for styling the rest of your results */
+"""
+
+# Function to generate custom HTML for the confidence bar
+def custom_confidence_bar(confidence):
+    color = "#4caf50" if confidence > 75 else "#FFC107" if confidence > 50 else "#F44336"
+    return f"""
+<div class="confidence-section">
+    <span class="confidence-label">Model Confidence:</span>
+    <div class="confidence-bar">
+        <div class="confidence-fill" style="width: {confidence}%; background-color: {color};">
+            {confidence}%
+        </div>
+    </div>
+</div>
+    """
+    
+
+# Function to extract score level from message
+def extract_score_level(message):
+    match = re.search(r'Score: (\d+)-(\d+)', message)
+    score_level = f"{match.group(1)} of 10" if match else "N/A"
+    return score_level
+def message_markdown(label, message, task, score_level):
+    md = f'''# {label}
+    **Model Prediction:** {message}
+    
+    **{task} Score:** {score_level}
+    '''
+    return md
+# Function to process the audio file and analyze it
+def analyze_audio(audio_data):
+    # Assuming predict_all returns a tuple of (message, confidence) for accuracy and fluency
+    accuracy, fluency = predict_all(audio_data)
+
+    # Unpack the results
+    accuracy_message, accuracy_confidence = accuracy
+    fluency_message, fluency_confidence = fluency
+
+    # Extract the score level from the message
+    accuracy_score = extract_score_level(accuracy_message)
+    fluency_score = extract_score_level(fluency_message)
+
+    # Remove the score level from the original message
+    accuracy_message = accuracy_message.split(",")[1].strip() if "," in accuracy_message else accuracy_message
+    fluency_message = fluency_message.split(",")[1].strip() if "," in fluency_message else fluency_message
+
+    # Generate the confidence bar HTML
+    accuracy_confidence_html = custom_confidence_bar(accuracy_confidence * 100)
+    fluency_confidence_html = custom_confidence_bar(fluency_confidence * 100)
+    
+    accuracy_markdown = message_markdown('Accuracy of Pronunciation', accuracy_message, 'Pronunciation', accuracy_score)
+    
+    fluency_markdown = message_markdown('Speaker Fluency', fluency_message, 'Fluency', fluency_score)
+    
+    return accuracy_markdown, accuracy_confidence_html, fluency_markdown, fluency_confidence_html
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=analyze_audio,
+    inputs=gr.Audio(label="Upload Audio"),
+    outputs=[
+        gr.Markdown(label="Accuracy Score Level"),
+        gr.HTML(label="Accuracy Confidence"),
+        gr.Markdown(label="Fluency Score Level"),
+        gr.HTML(label="Fluency Confidence"),
+    ],
+    css=additional_css,
+    title="Audio Analysis Tool",
+    description="Upload an audio file to analyze its accuracy and fluency."
+)
+
+# Run the Gradio app
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -1,3 +1,4 @@
 torch
 transformers
-
+gradio
+regex