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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 = ["Procedures", "Maintenance", "Operations", "Health", "Safety"] | |
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] | |
# 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() | |