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Runtime error
| import torch | |
| import streamlit as st | |
| from extractive_summarizer.model_processors import Summarizer | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config | |
| def abstractive_summarizer(text : str, model): | |
| tokenizer = T5Tokenizer.from_pretrained('t5-large') | |
| device = torch.device('cpu') | |
| preprocess_text = text.strip().replace("\n", "") | |
| t5_prepared_text = "summarize: " + preprocess_text | |
| tokenized_text = tokenizer.encode(t5_prepared_text, return_tensors="pt").to(device) | |
| # summmarize | |
| summary_ids = abs_model.generate(tokenized_text, | |
| num_beams=4, | |
| no_repeat_ngram_size=2, | |
| min_length=30, | |
| max_length=100, | |
| early_stopping=True) | |
| abs_summarized_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return abs_summarized_text | |
| def load_ext_model(): | |
| model = Summarizer() | |
| return model | |
| def load_abs_model(): | |
| model = T5ForConditionalGeneration.from_pretrained('t5-large') | |
| return model | |
| if __name__ == "__main__": | |
| # --------------------------------- | |
| # Main Application | |
| # --------------------------------- | |
| st.title("Text Summarizer 📝") | |
| summarize_type = st.sidebar.selectbox("Summarization type", options=["Extractive", "Abstractive"]) | |
| inp_text = st.text_input("Enter the text here") | |
| # view summarized text (expander) | |
| with st.expander("View input text"): | |
| st.write(inp_text) | |
| summarize = st.button("Summarize") | |
| # called on toggle button [summarize] | |
| if summarize: | |
| if summarize_type == "Extractive": | |
| # extractive summarizer | |
| ext_model = load_ext_model() | |
| summarized_text = ext_model(inp_text, num_sentences=5) | |
| elif summarize_type == "Abstractive": | |
| abs_model = load_abs_model() | |
| summarized_text = abstractive_summarizer(inp_text, model=abs_model) | |
| # final summarized output | |
| st.subheader("Summarized text") | |
| st.info(summarized_text) | |