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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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# import gradio as gr
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# gr.Interface.load("models/Abijith/Text-summarizer-t5-small").launch()
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import os
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import gradio as gr
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from transformers import AutoTokenizer, T5ForConditionalGeneration
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# Load model directly
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tokenizer = AutoTokenizer.from_pretrained("Abijith/my_first_t5_billsum_model")
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model = T5ForConditionalGeneration.from_pretrained("Abijith/my_first_t5_billsum_model")
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def summarize_text(input_text):
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summar_input = 'summarize: '+input_text
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input_tokens = tokenizer(summar_input, return_tensors='pt').input_ids
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outputs = model.generate(input_tokens)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Interface for the Gradio app
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iface = gr.Interface(
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fn=summarize_text,
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inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
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outputs=gr.outputs.Textbox(label="Summary"),
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title="Text Summarizer",
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description="Enter a paragraph, and the app will provide a summary.",
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)
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# Launch the Gradio app
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iface.launch()
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