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import gradio as gr |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", model="tasksource/ModernBERT-base-nli") |
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def zeroShotClassification(text_input, candidate_labels): |
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labels = [label.strip() for label in candidate_labels.split(',')] |
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prediction = classifier(text_input, labels) |
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results = {label: score for label, score in zip(prediction['labels'], prediction['scores'])} |
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return results, '' |
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demo = gr.Interface( |
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fn=zeroShotClassification, |
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inputs=[ |
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gr.Textbox( |
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label="✍️ Input Text", |
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placeholder="Enter the text you want to classify...", |
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lines=3, |
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elem_classes=["example-text"] |
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), |
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gr.Textbox( |
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label="🏷️ Category Labels", |
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placeholder="Enter comma-separated categories (e.g., happy, sad, excited, confused)", |
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lines=2, |
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elem_classes=["example-text"] |
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) |
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], |
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outputs=[ |
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gr.Label(label="📊 Classification Results"), |
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gr.Markdown(label="📈 Detailed Analysis", elem_classes=["markdown-text"]) |
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], |
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title="🤖 Zero-Shot Text Classification with ModernBERT", |
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description=""" |
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Classify any text into categories of your choice using advanced AI! |
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**How to use:** |
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1. Enter your text in the first box |
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2. Add comma-separated category labels in the second box |
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3. Click submit to see how your text matches each category |
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Try the examples below or create your own classifications! |
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""", |
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examples=[ |
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["One day I will see the world", "travel, adventure, dreams, future"], |
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["The movie had amazing special effects but a weak plot", "entertainment, technology, criticism, story"], |
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["This new phone has an amazing camera and great battery life", "technology, photography, consumer, review"], |
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["Mix flour, sugar, and eggs until well combined", "cooking, baking, instructions, food"], |
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["Scientists discovered a new species of butterfly in the Amazon", "science, nature, discovery, environment"], |
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["The team scored in the final minute to win the championship", "sports, victory, competition, excitement"], |
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["The painting uses vibrant colors to express deep emotions", "art, emotion, creativity, analysis"] |
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], |
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cache_examples=False, |
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css=""" |
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footer {display:none !important} |
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.output-markdown{display:none !important} |
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.gradio-container { |
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font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important; |
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max-width: 1200px !important; |
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} |
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.gr-button-primary { |
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background: linear-gradient(90deg, #11142D, #253885) !important; |
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border: none !important; |
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color: white !important; |
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border-radius: 12px !important; |
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transition: all 0.3s ease !important; |
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} |
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.gr-button-primary:hover { |
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transform: translateY(-2px) !important; |
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box-shadow: 0 4px 12px rgba(17, 20, 45, 0.3) !important; |
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background: linear-gradient(90deg, #253885, #4285F4) !important; |
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} |
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.gr-input, .gr-textarea { |
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border-radius: 8px !important; |
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border: 2px solid #E2E8F0 !important; |
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padding: 12px !important; |
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font-size: 16px !important; |
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} |
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.gr-input:focus, .gr-textarea:focus { |
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border-color: #253885 !important; |
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box-shadow: 0 0 0 3px rgba(37, 56, 133, 0.2) !important; |
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} |
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.gr-panel { |
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border-radius: 16px !important; |
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box-shadow: 0 4px 15px -1px rgba(0, 0, 0, 0.1) !important; |
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background: white !important; |
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} |
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.gr-box { |
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border-radius: 12px !important; |
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background: white !important; |
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} |
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.markdown-text { |
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font-size: 16px !important; |
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line-height: 1.6 !important; |
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} |
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.example-text { |
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font-family: 'Inter', sans-serif !important; |
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color: #11142D !important; |
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} |
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""" |
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) |
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if __name__ == "__main__": |
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demo.launch() |
