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| import streamlit as st | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| from rouge_score import rouge_scorer | |
| # Load BART model and tokenizer | |
| model_name = "facebook/bart-large-cnn" | |
| model = BartForConditionalGeneration.from_pretrained(model_name) | |
| tokenizer = BartTokenizer.from_pretrained(model_name) | |
| # Load ROUGE scorer | |
| scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) | |
| st.title("Text Summarization") | |
| input_text = st.text_area("Enter an article to summarize:") | |
| if st.button("Generate Summary"): | |
| if input_text.strip() != "": | |
| # Generate summary using the model | |
| inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True) | |
| summary_ids = model.generate(inputs["input_ids"], max_length=350, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True) | |
| generated_summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| # Display generated summary | |
| st.subheader("Generated Summary:") | |
| st.write(generated_summary) |