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Update app.py
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app.py
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import streamlit as st
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import pandas as pd
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import io
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# Load
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model_name = "TAgroup5/news-
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Initialize
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text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
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# Initialize the question answering pipeline
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qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
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# Streamlit App
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st.title("News Classification and Q&A")
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st.header("Classify News Articles")
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st.markdown("""
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Upload a CSV file containing news articles, and the model will classify each article
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into one of the following categories: Business, Opinion, Political Gossip, Sports, or World News.
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""")
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uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
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if uploaded_file is not None:
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if 'content' not in df.columns:
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st.error("The uploaded CSV
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else:
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st.write("Preview of
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st.dataframe(df.head())
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#
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def preprocess_text(text):
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return text
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# Apply preprocessing and classification
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df['processed_content'] = df['content'].apply(preprocess_text)
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df['class'] = df['processed_content'].apply(lambda x: text_classification_pipeline(x)[0]['label'])
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# Show
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st.write("Classification Results:")
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st.dataframe(df[['content', 'class']])
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# Provide
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output = io.
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df.to_csv(output, index=False)
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st.download_button(label="Download classified news", data=output.getvalue(), file_name="output.csv", mime="text/csv")
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# Component 2: Q&A Pipeline
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st.header("Ask a Question About the News")
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st.markdown("""
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Type in a question, and the model will extract an answer from the provided news content.
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""")
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question = st.text_input("Ask a question:")
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if question:
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if context:
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# Perform the question-answering task
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result = qa_pipeline(question=question, context=context)
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st.write("Answer:", result['answer'])
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import streamlit as st
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import pandas as pd
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import re
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import io
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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# Load fine-tuned model and tokenizer
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model_name = "TAgroup5/daily-mirror-news-classifier"
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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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, tokenizer=tokenizer)
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# Streamlit App
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st.title("News Classification and Q&A")
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## ====================== Component 1: News Classification ====================== ##
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st.header("Classify News Articles")
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st.markdown("Upload a CSV file with a 'content' column to classify news into categories.")
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uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
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if uploaded_file is not None:
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try:
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df = pd.read_csv(uploaded_file, encoding="utf-8") # Handle encoding issues
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except UnicodeDecodeError:
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df = pd.read_csv(uploaded_file, encoding="ISO-8859-1")
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if 'content' not in df.columns:
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st.error("Error: The uploaded CSV must contain a 'content' column.")
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else:
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st.write("Preview of uploaded data:")
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st.dataframe(df.head())
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# Preprocessing function
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def preprocess_text(text):
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text = text.lower() # Ensure consistent casing
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text = re.sub(r'\s+', ' ', text) # Remove extra spaces
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text = re.sub(r'[^a-zA-Z0-9\s]', '', text) # Remove special characters
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return text
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# Apply preprocessing and classification
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df['processed_content'] = df['content'].apply(preprocess_text)
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df['class'] = df['processed_content'].apply(lambda x: text_classification_pipeline(x)[0]['label'] if x.strip() else "Unknown")
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# Show results
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st.write("Classification Results:")
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st.dataframe(df[['content', 'class']])
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# Provide CSV download
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output = io.BytesIO()
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df.to_csv(output, index=False, encoding="utf-8-sig")
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st.download_button(label="Download classified news", data=output.getvalue(), file_name="output.csv", mime="text/csv")
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## ====================== Component 2: Q&A ====================== ##
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st.header("Ask a Question About the News")
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st.markdown("Enter a question and provide a news article to get an answer.")
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question = st.text_input("Ask a question:")
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context = st.text_area("Provide the news article or content for the Q&A:", height=150)
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if question and context.strip():
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result = qa_pipeline(question=question, context=context)
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st.write("Answer:", result['answer'])
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