Create app.py

app.py

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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()