app.py

import streamlit as st
import pandas as pd
import re
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from transformers import pipeline
from PIL import Image
import matplotlib.pyplot as plt
from wordcloud import WordCloud

# Download required NLTK data
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('omw-1.4')

# Load Models
news_classifier = pipeline("text-classification", model="Oneli/News_Classification")
qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")

# Label Mapping
label_mapping = {
    "LABEL_0": "Business",
    "LABEL_1": "Opinion",
    "LABEL_2": "Political Gossip",
    "LABEL_3": "Sports",
    "LABEL_4": "World News"
}

# Store classified article for QA
context_storage = {"context": "", "bulk_context": "", "num_articles": 0}

# Text Cleaning Functions
def clean_text(text):
    text = text.lower()
    text = re.sub(f"[{string.punctuation}]", "", text)  # Remove punctuation
    text = re.sub(r"[^a-zA-Z0-9\s]", "", text)  # Remove special characters
    words = text.split()  # Tokenization without Punkt
    words = [word for word in words if word not in stopwords.words("english")]  # Remove stopwords
    lemmatizer = WordNetLemmatizer()
    words = [lemmatizer.lemmatize(word) for word in words]  # Lemmatize tokens
    return " ".join(words)

# Define the functions
def classify_text(text):
    cleaned_text = clean_text(text)
    result = news_classifier(cleaned_text)[0]
    category = label_mapping.get(result['label'], "Unknown")
    confidence = round(result['score'] * 100, 2)

    # Store context for QA
    context_storage["context"] = cleaned_text

    return category, f"Confidence: {confidence}%"

def classify_csv(file):
    try:
        df = pd.read_csv(file, encoding="utf-8")
        text_column = df.columns[0]  # Assume first column is the text column
        
        df[text_column] = df[text_column].astype(str).apply(clean_text)  # Clean text column
        df["Decoded Prediction"] = df[text_column].apply(lambda x: label_mapping.get(news_classifier(x)[0]['label'], "Unknown"))
        df["Confidence"] = df[text_column].apply(lambda x: round(news_classifier(x)[0]['score'] * 100, 2))

        # Store all text as a single context for QA
        context_storage["bulk_context"] = " ".join(df[text_column].dropna().astype(str).tolist())
        context_storage["num_articles"] = len(df)

        output_file = "output.csv"
        df.to_csv(output_file, index=False)
        return df, output_file
    except Exception as e:
        return None, f"Error: {str(e)}"

def chatbot_response(history, user_input, text_input=None, file_input=None):
    user_input = user_input.lower()
    context = ""
    
    if text_input:
        context += text_input
    
    if file_input:
        df, _ = classify_csv(file_input)
        context += context_storage["bulk_context"]
    
    if context:
        with st.spinner("Finding answer..."):
            result = qa_pipeline(question=user_input, context=context)
            answer = result["answer"]
            history.append([user_input, answer])
    
    return history, answer

# Function to generate word cloud from the 'content' column (from output CSV)
def generate_word_cloud_from_output(df):
    # Assuming 'content' column is the first column after processing
    content_text = " ".join(df["content"].dropna().astype(str).tolist())
    wordcloud = WordCloud(width=800, height=400, background_color="white").generate(content_text)
    return wordcloud

# Function to generate bar graph for decoded predictions
def generate_bar_graph(df):
    prediction_counts = df["Decoded Prediction"].value_counts()
    fig, ax = plt.subplots(figsize=(10, 6))
    prediction_counts.plot(kind='bar', ax=ax, color='skyblue')
    ax.set_title('Frequency of Decoded Predictions', fontsize=16)
    ax.set_xlabel('Category', fontsize=12)
    ax.set_ylabel('Frequency', fontsize=12)
    st.pyplot(fig)

# Streamlit App Layout
st.set_page_config(page_title="News Classifier", page_icon="📰")
cover_image = Image.open("cover.png")  # Ensure this image exists
st.image(cover_image, caption="News Classifier 📢", use_container_width=True)

# Section for Single Article Classification
st.subheader("📰 Single Article Classification")
text_input = st.text_area("Enter News Text", placeholder="Type or paste news content here...")
if st.button("🔍 Classify"):
    if text_input:
        category, confidence = classify_text(text_input)
        st.write(f"Predicted Category: {category}")
        st.write(f"Confidence Level: {confidence}")
        
        # Generate word cloud for the cleaned text input
        wordcloud = generate_word_cloud_from_output(pd.DataFrame({"content": [text_input]}))  # Create a DataFrame for single input
        st.image(wordcloud.to_array(), caption="Word Cloud for Text Input", use_container_width=True)
    else:
        st.warning("Please enter some text to classify.")

# Section for Bulk CSV Classification
st.subheader("📂 Bulk Classification (CSV)")
file_input = st.file_uploader("Upload CSV File", type="csv")
if file_input:
    df, output_file = classify_csv(file_input)
    if df is not None:
        st.dataframe(df)
        st.download_button(
            label="Download Processed CSV",
            data=open(output_file, 'rb').read(),
            file_name=output_file,
            mime="text/csv"
        )
        
        # Generate word cloud for the 'content' column of the processed CSV data
        wordcloud = generate_word_cloud_from_output(df)
        st.image(wordcloud.to_array(), caption="Word Cloud for CSV Content", use_container_width=True)
        
        # Generate bar graph for decoded predictions frequency
        generate_bar_graph(df)
    else:
        st.error(f"Error processing file: {output_file}")

# Section for Chatbot Interaction
st.subheader("💬 AI Chat Assistant")
history = []
user_input = st.text_input("Ask about news classification or topics", placeholder="Type a message...")
source_toggle = st.radio("Select Context Source", ["Single Article", "Bulk Classification"])
if st.button("✉ Send"):
    history, bot_response = chatbot_response(history, user_input, text_input=text_input if source_toggle == "Single Article" else None, file_input=file_input if source_toggle == "Bulk Classification" else None)
    st.write("Chatbot Response:")
    for q, a in history:
        st.write(f"Q: {q}")
        st.write(f"A: {a}")