app.py

import streamlit as st
import pandas as pd
import re
import io
import string
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import nltk

# Download NLTK resources
nltk.download('punkt', download_dir='/root/nltk_data')
nltk.download('stopwords', download_dir='/root/nltk_data')
nltk.download('wordnet', download_dir='/root/nltk_data')

# Initialize lemmatizer and stopwords
lemmatizer = WordNetLemmatizer()
stop_words = set(stopwords.words('english'))

# Load fine-tuned model and tokenizer (adjust the model name)
model_name = "TAgroup5/news-classification-model"  # Replace with the correct model name
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Initialize pipelines
text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Streamlit App
st.title("News Classification and Q&A")

## ====================== Component 1: News Classification ====================== ##
st.header("Classify News Articles")
st.markdown("Upload a CSV file with a 'content' column to classify news into categories.")

uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

if uploaded_file is not None:
    try:
        df = pd.read_csv(uploaded_file, encoding="utf-8")  # Handle encoding issues
    except UnicodeDecodeError:
        df = pd.read_csv(uploaded_file, encoding="ISO-8859-1")

    if 'content' not in df.columns:
        st.error("Error: The uploaded CSV must contain a 'content' column.")
    else:
        st.write("Preview of uploaded data:")
        st.dataframe(df.head())

        # Preprocessing function to clean the text
        def preprocess_text(text):
            text = text.lower()  # Convert to lowercase
            text = re.sub(r'[^a-z\s]', '', text)  # Remove special characters & numbers
            tokens = word_tokenize(text)  # Tokenization
            tokens = [word for word in tokens if word not in stop_words]  # Remove stopwords
            tokens = [lemmatizer.lemmatize(word) for word in tokens]  # Lemmatization
            return " ".join(tokens)

        # Apply preprocessing and classification
        df['processed_content'] = df['content'].apply(preprocess_text)
        
        # Classify each record into one of the five classes
        df['class'] = df['processed_content'].apply(lambda x: text_classification_pipeline(x)[0]['label'] if x.strip() else "Unknown")

        # Show results
        st.write("Classification Results:")
        st.dataframe(df[['content', 'class']])

        # Provide CSV download
        output = io.BytesIO()
        df.to_csv(output, index=False, encoding="utf-8-sig")
        st.download_button(label="Download classified news", data=output.getvalue(), file_name="output.csv", mime="text/csv")

## ====================== Component 2: Q&A ====================== ##
st.header("Ask a Question About the News")
st.markdown("Enter a question and provide a news article to get an answer.")

question = st.text_input("Ask a question:")
context = st.text_area("Provide the news article or content for the Q&A:", height=150)

if question and context.strip():
    qa_model_name = "distilbert-base-uncased-distilled-squad"  # Example of a common Q&A model
    qa_pipeline = pipeline("question-answering", model=qa_model_name, tokenizer=qa_model_name)
    result = qa_pipeline(question=question, context=context)
    
    # Check if the result contains an answer
    if 'answer' in result and result['answer']:
        st.write("Answer:", result['answer'])
    else:
        st.write("No answer found in the provided content.")