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Update app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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def
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"""
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#
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gr.
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demo.launch()
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import gradio as gr
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from transformers import pipeline
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# Model options available for summarization
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model_names = ["Falconsai/text_summarization"]
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def summarize_article(article, model_name, max_length, temperature, top_k, top_p):
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""" Summarize the provided article text using the specified model and hyperparameters. """
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summarizer = pipeline("summarization", model=model_name)
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summary = summarizer(
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article,
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max_length=int(round(max_length)),
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min_length=30,
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do_sample=True,
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temperature=temperature,
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top_k=int(round(top_k)),
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top_p=top_p
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)
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return summary[0]['summary_text']
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# Gradio interface setup
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iface = gr.Interface(
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fn=summarize_article,
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inputs=[
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gr.Textbox(lines=10, placeholder="Enter the article text here..."),
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gr.Dropdown(choices=model_names, label="Select Model"),
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gr.Slider(minimum=10, maximum=200, step=10, label="Max Length of Summary"),
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gr.Slider(minimum=0.1, maximum=2, step=0.1, label="Temperature for Sampling"),
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gr.Slider(minimum=1, maximum=100, step=1, label="Top-k"),
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gr.Slider(minimum=0.1, maximum=1, step=0.1, label="Top-p")
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],
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outputs="text",
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title="Text Summarization",
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description="Adjust the parameters below to summarize the article."
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iface.launch(debug=True, share=True)
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