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Browse files- app.py +42 -0
- requirements.txt +3 -0
app.py
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import gradio as gr
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import logging
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import sys
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from transformers import pipeline
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# Load the model
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logger.info("Loading bart-large-cnn model...")
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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logger.info("Model loaded successfully!")
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def summarize_text(text, max_length=150, min_length=30):
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if not text or len(text.strip()) < 50:
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return text
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logger.info(f"Summarizing text of length {len(text)}")
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result = summarizer(
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text,
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max_length=max_length,
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min_length=min_length,
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truncation=True
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)
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summary = result[0]["summary_text"]
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return summary
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# Create Gradio interface
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demo = gr.Interface(
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fn=summarize_text,
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inputs=[
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gr.Textbox(lines=10, label="Text to Summarize"),
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gr.Slider(50, 500, value=150, label="Max Length"),
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gr.Slider(10, 200, value=30, label="Min Length")
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],
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outputs=gr.Textbox(label="Summary"),
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title="StudAI ",
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description="Powered by facebook/bart-large-cnn model"
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)
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demo.launch(share=True, server_name="Leo", server_port=7860)
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requirements.txt
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gradio==4.13.0
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transformers==4.35.2
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torch==2.0.1
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