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| import gradio as gr | |
| from datetime import datetime | |
| import unicodedata | |
| from utils.utils import get_label | |
| def sanitize_feedback(feedback): | |
| """ | |
| Convert emojis or other non-text characters in feedback to a text representation. | |
| This ensures only clean text is saved in the feedback file. | |
| """ | |
| sanitized_feedback = ''.join( | |
| c if unicodedata.category(c).startswith(('L', 'N', 'P', 'Z')) else '' for c in feedback | |
| ) | |
| return sanitized_feedback | |
| # Function to capture feedback and save it to a timestamped file | |
| def capture_feedback(feedback): | |
| # Sanitize feedback to ensure only text is saved | |
| sanitized_feedback = sanitize_feedback(feedback) | |
| # Get the current timestamp | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| # Create a filename with the timestamp | |
| filename = f"{timestamp}.txt" | |
| # Write feedback to the file | |
| with open(filename, "w", encoding="utf-8") as file: | |
| file.write(sanitized_feedback) | |
| return "Thank you for your feedback!" # Return message for the popup | |
| # Main Gradio interface | |
| with gr.Blocks() as demo: | |
| # State to manage interface visibility | |
| feedback_submitted = gr.State(False) | |
| # Title and description of the demo | |
| gr.Markdown("<h1 style='text-align: center;'>Emotions Classification Demo</h1>") | |
| gr.Markdown(""" | |
| <div style='text-align: center;'> | |
| This is a demo page for our classification model. Our model provides a brief description of your image and predicts the most relevant emotions that the image invokes. | |
| </div> | |
| """) | |
| # Organize the input and output sections in a row | |
| with gr.Row(): | |
| # Left side: input section with description | |
| with gr.Column(): | |
| gr.Markdown("<b>Upload your image here to get the Emotion predictions.</b>") | |
| image_input = gr.Image(type="pil", label="Input Image") | |
| # Submit button for image upload | |
| submit_button = gr.Button("Submit") | |
| # Right side: output section with description | |
| with gr.Column(): | |
| gr.Markdown("<b>Predicted outputs</b>") | |
| # Textbox for emotion label | |
| output_text = gr.Textbox(label="Image Description") | |
| # Plot for the bar chart | |
| output_plot = gr.Plot(label="Emotion Probabilities") | |
| # Feedback section directly below the graph | |
| gr.Markdown("<b>How do you feel about our emotion results?</b>") | |
| feedback_choices = [ | |
| "😄 Very Satisfied", | |
| "😊 Satisfied", | |
| "😐 Neutral", | |
| "🙁 Dissatisfied", | |
| "😡 Very Dissatisfied" | |
| ] | |
| feedback = gr.Radio(choices=feedback_choices, label="Your Feedback") | |
| # Button to submit feedback | |
| feedback_button = gr.Button("Submit Feedback") | |
| # Feedback submission action | |
| def submit_feedback(feedback): | |
| capture_feedback(feedback) # Save feedback to file | |
| feedback_submitted.set(True) # Set state to indicate feedback was submitted | |
| return "Thank you for your feedback!" # Return thank-you message | |
| feedback_button.click(fn=submit_feedback, inputs=feedback, outputs=None) | |
| # Thank you message section, initially hidden | |
| thank_you_message = gr.Markdown("Thank you for your feedback!", visible=False) | |
| # Display thank you message when feedback is submitted | |
| feedback_submitted.change( | |
| fn=lambda: ("Thank you for your feedback!", False), # Show thank you message | |
| inputs=feedback_submitted, | |
| outputs=[thank_you_message, feedback_submitted] | |
| ) | |
| # Main interface to show | |
| with gr.Row(visible=True): | |
| gr.Markdown("Thank you for your feedback!", visible=feedback_submitted) | |
| # Function to process the input and output | |
| submit_button.click(fn=get_label, inputs=image_input, outputs=[output_text, output_plot]) | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| demo.launch(share=True) | |