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Update app.py
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app.py
CHANGED
@@ -52,7 +52,7 @@ model_name_qa = "distilbert-base-cased-distilled-squad"
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model_qa = AutoModelForQuestionAnswering.from_pretrained(model_name_qa)
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tokenizer_qa = AutoTokenizer.from_pretrained(model_name_qa)
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# Initialize pipelines
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text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
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qa_pipeline = pipeline("question-answering", model=model_qa, tokenizer=tokenizer_qa)
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@@ -89,6 +89,7 @@ if uploaded_file is not None:
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df.to_csv(output, index=False, encoding="utf-8-sig")
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st.download_button("π₯ Download Classified News", data=output.getvalue(), file_name="output.csv", mime="text/csv")
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st.write("π **Filter by Category**")
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categories = ['All', 'Business', 'Opinion', 'Political_gossip', 'Sports', 'World_news']
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model_qa = AutoModelForQuestionAnswering.from_pretrained(model_name_qa)
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tokenizer_qa = AutoTokenizer.from_pretrained(model_name_qa)
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# Initialize pipelines for both models
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text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
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qa_pipeline = pipeline("question-answering", model=model_qa, tokenizer=tokenizer_qa)
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df.to_csv(output, index=False, encoding="utf-8-sig")
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st.download_button("π₯ Download Classified News", data=output.getvalue(), file_name="output.csv", mime="text/csv")
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#App Component 3: Think!Think!Think! - Introducing a News Filtering Option
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st.write("π **Filter by Category**")
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categories = ['All', 'Business', 'Opinion', 'Political_gossip', 'Sports', 'World_news']
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