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Create test.py
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test.py
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# import gradio as gr
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# from transformers import pipeline
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# import os
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# import pandas as pd
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# # Load the text summarization pipeline
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# summarizer = pipeline("summarization", model="astro21/bart-cls")
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# chunk_counter = 0
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# def summarize_text(input_text):
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# global chunk_counter
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# chunk_counter = 0
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# max_chunk_size = 1024
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# chunks = [input_text[i:i + max_chunk_size] for i in range(0, len(input_text), max_chunk_size)]
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# summarized_chunks = []
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# chunk_lengths = []
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# summarized_chunks_only = []
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# for chunk in chunks:
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# chunk_counter += 1
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# summarized_chunk = summarizer(chunk, max_length=128, min_length=64, do_sample=False)[0]['summary_text']
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# summarized_chunks.append(f"Chunk {chunk_counter}:\n{summarized_chunk}")
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# summarized_chunks_only.append(summarized_chunk)
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# chunk_lengths.append(len(chunk))
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# summarized_text = "\n".join(summarized_chunks)
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# summarized_text_only = "\n".join(summarized_chunks_only)
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# # Save the merged summary to a file
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# with open("summarized.txt", "w") as output_file:
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# output_file.write(summarized_text_only)
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# chunk_df = pd.DataFrame({'Chunk Number': range(1, chunk_counter + 1), 'Chunk Length': chunk_lengths})
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# return summarized_text, chunk_df, "summarized.txt"
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# def read_file(file):
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# print(file[0].name)
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# with open(file[0].name, 'r') as file_:
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# content = file_.read()
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# return content
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# def summarize_text_file(file):
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# if file is not None:
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# content = read_file(file)
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# return summarize_text(content)
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# input_type = gr.inputs.File("text")
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# # Name the outputs using the label parameter and provide a download option
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# demo = gr.Interface(fn=summarize_text_file, inputs=input_type,
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# outputs=[gr.Textbox(label="Summarized Text"),
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# gr.Dataframe(label="Chunk Information", type="pandas"),
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# gr.File(label="Download Summarized Text", type="file", live=False)],
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# title = "Text Summarization",
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# description = "Summarize text using BART",
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# theme = "huggingface",
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# allow_flagging="never",
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# live=True)
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# demo.launch(share=True)
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