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| import gradio as gr | |
| from transformers import pipeline | |
| # Load the model | |
| model_name = "knowledgator/comprehend_it-base" | |
| classifier = pipeline("zero-shot-classification", model=model_name, device="cpu") | |
| # Function to classify feedback | |
| def classify_feedback(feedback_text): | |
| # Classify feedback using the loaded model | |
| labels = ["Value", "Facilities", "Experience", "Functionality", "Quality"] | |
| result = classifier(feedback_text, labels, multi_label=True) | |
| # Get the top two labels associated with the feedback | |
| top_labels = result["labels"][:2] | |
| scores = result["scores"][:2] | |
| # Check if the accuracy of the top label is less than 30% | |
| if scores[0] < 0.5: | |
| return "Please provide another relevant feedback." | |
| # Generate HTML content for displaying the scores as meters/progress bars | |
| html_content = "" | |
| for i in range(len(top_labels)): | |
| score_percentage = scores[i] * 100 # Convert score to percentage | |
| html_content += f"<div><b>{top_labels[i]}:</b> {scores[i]:.2f} <div style='background-color: #e0e0e0; border-radius: 10px;'><div style='height: 24px; width: {score_percentage}%; background-color: #76b900; border-radius: 10px;'></div></div></div>" | |
| return html_content | |
| # Create Gradio interface | |
| feedback_textbox = gr.Textbox(label="Enter your feedback:") | |
| feedback_output = gr.HTML(label="Top 2 Labels with Scores:") | |
| gr.Interface( | |
| fn=classify_feedback, | |
| inputs=feedback_textbox, | |
| outputs=feedback_output, | |
| title="Feedback Classifier", | |
| description="Enter your feedback and get the top 2 associated labels with scores." | |
| ).launch() | |