Delete gradio_app.py

gradio_app.py

@@ -1,108 +0,0 @@
-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()