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import streamlit as st |
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from transformers import pipeline |
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try: |
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summarizer = pipeline("summarization", model="syndi-models/titlewave-t5-base") |
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summarizer_loaded = True |
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except ValueError as e: |
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st.error(f"Error loading summarization model: {e}") |
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summarizer_loaded = False |
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model_name = "elozano/bert-base-cased-news-category" |
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try: |
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classifier = pipeline("text-classification", model=model_name, return_all_scores=True) |
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classifier_loaded = True |
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except ValueError as e: |
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st.error(f"Error loading classification model: {e}") |
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classifier_loaded = False |
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st.title("Long Question Summarization and Classification") |
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tab1, tab2 = st.tabs(["Question Summarization", "Question Classification"]) |
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with tab1: |
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st.header("Question Summarization") |
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text_to_summarize = st.text_area("Enter long question to summarize:", "") |
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if st.button("Summarize"): |
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if summarizer_loaded and text_to_summarize: |
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try: |
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summary = summarizer(text_to_summarize, max_length=130, min_length=30, do_sample=False) |
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st.write("Summary:", summary[0]['summary_text']) |
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except Exception as e: |
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st.error(f"Error during summarization: {e}") |
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else: |
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st.warning("Please enter text to summarize and ensure the model is loaded.") |
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with tab2: |
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st.header("Questions Classification") |
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text_to_classify = st.text_area("Enter question to classify:", "") |
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if st.button("Classify"): |
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if classifier_loaded and text_to_classify: |
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try: |
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results = classifier(text_to_classify)[0] |
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max_score = max(results, key=lambda x: x['score']) |
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st.write("Text:", text_to_classify) |
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st.write("Category:", max_score['label']) |
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st.write("Score:", max_score['score']) |
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except Exception as e: |
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st.error(f"Error during classification: {e}") |
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else: |
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st.warning("Please enter text to classify and ensure the model is loaded.") |