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| import gradio as gr | |
| from transformers import pipeline | |
| # Create a zero-shot classification pipeline | |
| classifier = pipeline("zero-shot-classification") | |
| def classify_text(text, additional_labels): | |
| # Default labels | |
| labels = ["Education", "Business", "Sports", "Manufacturing"] | |
| # Add custom labels if provided | |
| if additional_labels: | |
| custom_labels = additional_labels.split(',') | |
| labels.extend(custom_labels) | |
| # Perform classification | |
| result = classifier(text, candidate_labels=labels) | |
| # Formatting the output | |
| output = [] | |
| for label, score in zip(result["labels"], result["scores"]): | |
| output.append(f"Label: {label}, Score: {round(score, 4)}") | |
| return "\n".join(output) | |
| # Create a Gradio interface | |
| interface = gr.Interface( | |
| fn=classify_text, | |
| inputs=["text", "text"], | |
| outputs="text", | |
| title="Text Classification", | |
| description="Enter a text to classify into categories: Education, Business, Sports, Manufacturing. Optionally, add more categories separated by commas." | |
| ) | |
| # Launch the interface | |
| interface.launch() |