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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 = ["High Priority ticket", "Low Priority ticket", "Medium Priority ticket"] | |
| result = classifier(feedback_text, labels, multi_label=True) | |
| # Get the top label associated with the feedback | |
| top_label = result["labels"][0] | |
| return top_label | |
| # Create Gradio interface | |
| feedback_textbox = gr.Textbox(label="Enter your feedback:") | |
| feedback_output = gr.Label(label="Top Label:") | |
| gr.Interface( | |
| fn=classify_feedback, | |
| inputs=feedback_textbox, | |
| outputs=feedback_output, | |
| title="Feedback Classifier", | |
| description="Enter your feedback and get the priority label for your ticket." | |
| ).launch() |