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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)
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