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import gradio as gr |
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from transformers import pipeline |
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import torch |
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device = 0 if torch.cuda.is_available() else -1 |
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classifier = pipeline("sentiment-analysis", device=device) |
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device) |
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def analyze_sentiment(text): |
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if not text.strip(): |
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return {"Error": 1.0} |
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result = classifier(text)[0] |
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return { |
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f"{result['label']} {'😊' if result['label'] == 'POSITIVE' else '😠'}": result['score'], |
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"Other": 1 - result['score'] |
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} |
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def summarize(text, max_len, min_len): |
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if len(text) < 50: |
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return "Please enter at least 50 characters." |
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result = summarizer(text, max_length=max_len, min_length=min_len) |
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return result[0]['summary_text'] |
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with gr.Blocks(title="NLP Toolkit") as app: |
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gr.Markdown("# 🛠️ NLP Toolkit") |
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with gr.Tabs(): |
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with gr.TabItem("Sentiment"): |
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gr.Interface( |
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fn=analyze_sentiment, |
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inputs=gr.Textbox(lines=3), |
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outputs=gr.Label(), |
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examples=[["I love this!"], ["This is terrible."]] |
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) |
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with gr.TabItem("Summarize"): |
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gr.Interface( |
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fn=summarize, |
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inputs=[ |
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gr.Textbox(lines=6), |
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gr.Slider(50, 200, value=100), |
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gr.Slider(20, 80, value=30) |
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], |
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outputs=gr.Textbox() |
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) |
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app.launch() |
