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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") | |
| # Keywords associated with the "Value" label | |
| value_keywords = [ | |
| "cheap", "expensive", "worth", "waste", "value for money", "overpriced", "bargain", | |
| "affordable", "pricey", "costly", "economical", "deal", "rip-off", "budget-friendly", | |
| "high-priced", "low-priced", "discounted", "premium", "luxurious", "inexpensive", | |
| "priced right", "steal", "splurge", "bang for the buck", "investment", "saver", | |
| "money's worth", "exorbitant", "reasonable", "unreasonable", "priced well", | |
| "cost-effective", "overvalued", "undervalued", "fair price", "high cost", "low cost", | |
| "good deal", "bad deal", "profitable", "loss", "savings", "spendy", "wallet-friendly", | |
| "financially smart", "economic", "lavish", "modestly priced", "upscale", "downscale" | |
| ] | |
| # Function to check for value-related keywords in feedback | |
| def contains_value_keywords(feedback_text): | |
| for keyword in value_keywords: | |
| if keyword in feedback_text.lower(): | |
| return True | |
| return False | |
| # 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) | |
| # Check for value-related keywords and adjust scores if necessary | |
| if contains_value_keywords(feedback_text): | |
| # Find the index of the "Value" label | |
| try: | |
| value_index = result["labels"].index("Value") | |
| # Promote the score of the "Value" label | |
| result["scores"][value_index] += 0.2 # Adjust the promotion strength as needed | |
| # Ensure the score does not exceed 1 | |
| result["scores"][value_index] = min(result["scores"][value_index], 1.0) | |
| except ValueError: | |
| pass # "Value" label not in the top results | |
| # Get the top two labels associated with the feedback, after possible adjustment | |
| top_labels_scores = sorted(zip(result["labels"], result["scores"]), key=lambda x: x[1], reverse=True)[:2] | |
| top_labels, scores = zip(*top_labels_scores) | |
| # 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() | |