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Update app.py
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app.py
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
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import gradio as gr
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# Load model
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tokenizer = AutoTokenizer.from_pretrained('/
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model = AutoModelForSeq2SeqLM.from_pretrained('/
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tokenizer.model_max_length = 1024
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# Config
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gen_config = GenerationConfig(
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max_length=150,
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min_length=40,
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length_penalty=2.0,
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num_beams=4,
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early_stopping=True
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)
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# Summarization function
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def summarize(blog_post):
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input = tokenizer(blog_post, max_length=1024, truncation=True, return_tensors='pt')
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summary_ids = model.generate(input['input_ids'], generation_config=gen_config)
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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# Gradio UI
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iface = gr.Interface(
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fn=summarize,
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inputs=gr.Textbox(lines=15, label="Enter Text to Summarize"),
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outputs=gr.Textbox(label="Summary"),
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title="Text Summarizer",
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description="Enter a long paragraph or blog post to get a summarized version."
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)
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iface.launch(share=True)
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
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import gradio as gr
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# Load model
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tokenizer = AutoTokenizer.from_pretrained('AyushSoni14/text-summarizer-model')
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model = AutoModelForSeq2SeqLM.from_pretrained('AyushSoni14/text-summarizer-model')
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tokenizer.model_max_length = 1024
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# Config
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gen_config = GenerationConfig(
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max_length=150,
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min_length=40,
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length_penalty=2.0,
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num_beams=4,
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early_stopping=True
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)
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# Summarization function
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def summarize(blog_post):
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input = tokenizer(blog_post, max_length=1024, truncation=True, return_tensors='pt')
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summary_ids = model.generate(input['input_ids'], generation_config=gen_config)
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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# Gradio UI
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iface = gr.Interface(
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fn=summarize,
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inputs=gr.Textbox(lines=15, label="Enter Text to Summarize"),
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outputs=gr.Textbox(label="Summary"),
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title="Text Summarizer",
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description="Enter a long paragraph or blog post to get a summarized version."
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)
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iface.launch(share=True)
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