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| from transformers import pipeline | |
| import gradio as gr | |
| import PyPDF2 | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| def text_summarize(pdf_file): | |
| # Read PDF | |
| text = "" | |
| reader = PyPDF2.PdfReader(pdf_file.name) | |
| for page in reader.pages: | |
| text += page.extract_text() | |
| # summarization logic | |
| article = text[:1500] #(just demo: first 1500 chars) | |
| response = summarizer(article) | |
| return response[0]['summary_text'] | |
| # creating the user interface using gradio | |
| demo = gr.Interface( | |
| fn=text_summarize, | |
| inputs=gr.File(type="filepath", file_types=[".pdf"]), | |
| outputs="text", | |
| title="Text Summarizer", | |
| description="Upload a PDF to summarize" | |
| ) | |
| demo.launch() | |