app.py

import streamlit as st
import pandas as pd
from transformers import pipeline
from sentence_transformers import CrossEncoder
from sentence_transformers import SentenceTransformer
import string
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import nltk

# Download NLTK resources (run this once if not already downloaded)
nltk.download('punkt')
nltk.download('punkt_tab')
nltk.download('stopwords')
nltk.download('wordnet')

# Set modern page configuration
st.set_page_config(page_title="News Analyzer", layout="wide")

# Inject custom CSS for sleek dark blue theme with black fonts
st.markdown("""
    <style>
    /* Global Styling */
    body {
        background: #0b132b;
        font-family: 'Arial', sans-serif;
        margin: 0;
        padding: 0;
    }
    /* Header Styling */
    .custom-header {
        background: linear-gradient(to right, #1f4068, #1b1b2f);
        padding: 1.5rem;
        margin-bottom: 1.5rem;
        border-radius: 12px;
        text-align: center;
        font-size: 30px;
        font-weight: bold;
        box-shadow: 0px 4px 15px rgba(0, 217, 255, 0.3);
    }
    /* Buttons */
    .stButton>button {
        background: linear-gradient(45deg, #0072ff, #00c6ff);
        border-radius: 8px;
        padding: 14px 28px;
        font-size: 18px;
        transition: 0.3s ease;
        border: none;
    }
    .stButton>button:hover {
        transform: scale(1.05);
        box-shadow: 0px 4px 10px rgba(0, 255, 255, 0.5);
    }
    /* Text Input */
    .stTextInput>div>div>input {
        background-color: rgba(255, 255, 255, 0.1);
        border-radius: 8px;
        padding: 12px;
        font-size: 18px;
    }
    /* Dataframe Container */
    .dataframe-container {
        background: rgba(255, 255, 255, 0.1);
        padding: 15px;
        border-radius: 12px;
    }
    /* Answer Display Box - Larger */
    .answer-box {
        background: rgba(0, 217, 255, 0.15);
        padding: 35px;
        border-radius: 15px;
        border: 2px solid rgba(0, 217, 255, 0.6);
        font-size: 22px;
        text-align: center;
        margin-bottom: 20px;
        min-height: 150px;
        box-shadow: 0px 2px 12px rgba(0, 217, 255, 0.3);
        display: flex;
        align-items: center;
        justify-content: center;
        transition: all 0.3s ease;
    }
    /* CSV Display Box */
    .csv-box {
        background: rgba(255, 255, 255, 0.1);
        padding: 15px;
        border-radius: 12px;
        margin-top: 20px;
        box-shadow: 0px 2px 12px rgba(0, 217, 255, 0.3);
    }
    </style>
    """, unsafe_allow_html=True)

# Modern Header
st.markdown("<div class='custom-header'> 🧩 AI-Powered News Analyzer</div>", unsafe_allow_html=True)

# Load the Hugging Face models
classifier = pipeline("text-classification", model="Sandini/news-classifier")  # Classification pipeline
qa_pipeline = pipeline("question-answering", model="distilbert/distilbert-base-cased-distilled-squad")  # QA pipeline

# Initialize Cross-Encoder for QA relevance scoring
cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')  # Pre-trained Cross-Encoder model

# Define preprocessing functions for classification
def preprocess_text(text):
    if not isinstance(text, str):
        text = ""
        
    # Step 1: Lowercase the text
    text = text.lower()

    # Step 2: Remove punctuation
    text = text.translate(str.maketrans('', '', string.punctuation))

    # Step 3: Tokenize the text
    tokens = word_tokenize(text)

    # Step 4: Remove stopwords
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens if word not in stop_words]

    # Step 5: Lemmatization
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(word) for word in tokens]

    # Step 6: Join tokens back into a single string
    preprocessed_text = " ".join(tokens)

    return preprocessed_text

# Reverse mapping (numeric label -> category name)
label_mapping = {
    "Business": 0,
    "Opinion": 1,
    "Sports": 2,
    "Political_gossip": 3,
    "World_news": 4
}
reverse_label_mapping = {v: k for k, v in label_mapping.items()}

# Define a function to predict the category for a single text
def predict_category(text):
    prediction = classifier(text)
    predicted_label_id = int(prediction[0]['label'].split('_')[-1])  # Extract numeric label from 'LABEL_X'
    return reverse_label_mapping[predicted_label_id]

# Responsive Layout - Uses full width
col1, col2 = st.columns([1.1, 1])

# Left Section - File Upload & CSV/Excel Display
with col1:
    st.subheader("📂 Upload News Data")
    uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])

    if uploaded_file is not None:
        # Determine the file extension
        file_extension = uploaded_file.name.split('.')[-1]

        if file_extension == 'csv':
            df = pd.read_csv(uploaded_file)
        elif file_extension == 'xlsx':
            df = pd.read_excel(uploaded_file)

        # Preprocess the content column and predict categories
        if 'content' in df.columns:
            df['content'] = df['content'].fillna("").astype(str)
            df['preprocessed_content'] = df['content'].apply(preprocess_text)
            df['class'] = df['preprocessed_content'].apply(predict_category)

            # Drop the preprocessed_content column before displaying or saving
            df_for_display = df.drop(columns=['preprocessed_content'], errors='ignore')
            df_for_download = df.drop(columns=['preprocessed_content'], errors='ignore')

        # Download button
        st.download_button(
            label="⬇️ Download Processed Data",
            data=df_for_download.to_csv(index=False).encode('utf-8'),
            file_name="output.csv",
            mime="text/csv"
        )

        # CSV Preview Box
        st.markdown("<div class='csv-box'><h4>📜 CSV/Excel Preview</h4></div>", unsafe_allow_html=True)
        st.dataframe(df_for_display, use_container_width=True)


# Right Section - Q&A Interface
with col2:
    st.subheader("🤖 AI Assistant")

    # Answer Display Box (Initially Empty)
    answer_placeholder = st.empty()
    answer_placeholder.markdown("<div class='answer-box'></div>", unsafe_allow_html=True)

    # Question Input
    st.markdown("### 🔍 Ask Your Question:")
    user_question = st.text_input("Enter your question here", label_visibility="hidden")  # Hides the label

    # Button & Answer Display
    if st.button("🔮 Get Answer"):
        if user_question.strip() and uploaded_file is not None:
            # Ensure the DataFrame has the required content column
            if 'content' in df.columns:
                context = df['content'].dropna().tolist()  # Use the content column as context

                # Prepare pairs of (question, context)
                pairs = [(user_question, c) for c in context]

                # Score each pair using the Cross-Encoder
                scores = cross_encoder.predict(pairs)

                # Get top matches based on scores
                top_indices = scores.argsort()[-5:][::-1]  # Get indices of top 5 matches
                top_context = "\n".join([context[i] for i in top_indices])

                # Get answer from Hugging Face model using top context
                result = qa_pipeline(question=user_question, context=top_context)
                answer = result['answer']
            else:
                answer = "⚠️ File does not contain a 'content' column!"
        else:
            answer = "⚠️ Please upload a valid file first!"

        answer_placeholder.markdown(f"<div class='answer-box'>{answer}</div>", unsafe_allow_html=True)