import streamlit as st
import pandas as pd
import re
import io
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from transformers import AutoModelForQuestionAnswering

# Streamlit UI
st.set_page_config(page_title="News Classifier & Q&A", layout="wide")
st.markdown("""
    <style>
        body {
            background-color: #f4f4f4;
            color: #333333;
            font-family: 'Arial', sans-serif;
        }
        .stApp {
            background-image: url('https://i.pinimg.com/474x/9c/68/86/9c6886dd642a4869f3fa4578f9fe34ef.jpg');
            background-size: cover;
            background-position: center;
            padding: 20px;
            border-radius: 10px;
            box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1);
        }
        h1 {
            color: #ff4b4b;
            text-align: center;
        }
        .stButton>button {
            background-color: #088da5 !important;
            color: white !important;
            font-size: 18px !important;
            border-radius: 10px !important;
            width: 100%;
            padding: 10px;
        }
        .stDownloadButton>button {
            background-color: #28a745 !important;
            color: white !important;
            font-size: 16px !important;
            border-radius: 10px !important;
        }
    </style>
""", unsafe_allow_html=True)

# Load fine-tuned models
model_name_classification = "TAgroup5/news-classification-model"
model = AutoModelForSequenceClassification.from_pretrained(model_name_classification)
tokenizer = AutoTokenizer.from_pretrained(model_name_classification)

model_name_qa = "distilbert-base-cased-distilled-squad"
model_qa = AutoModelForQuestionAnswering.from_pretrained(model_name_qa)
tokenizer_qa = AutoTokenizer.from_pretrained(model_name_qa)

# Initialize pipelines for both models
text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
qa_pipeline = pipeline("question-answering", model=model_qa, tokenizer=tokenizer_qa)

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

## ====================== News Classification ====================== ##
st.header("📌 Classify News Articles")
st.markdown("Upload a CSV file containing a **'Content'** column to classify news into pre-defined categories.")

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

if uploaded_file is not None:
    df = pd.read_csv(uploaded_file, encoding="utf-8")
    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())

        def preprocess_text(text):
            text = text.lower()
            text = re.sub(r'\s+', ' ', text)
            text = re.sub(r'[^a-z\s]', '', text)
            return text

        df['processed_content'] = df['content'].apply(preprocess_text)
        df['class'] = df['processed_content'].apply(lambda x: text_classification_pipeline(x)[0]['label'] if x.strip() else "Unknown")

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

        output = io.BytesIO()
        df.to_csv(output, index=False, encoding="utf-8-sig")
        st.download_button("📥 Download Classified News", data=output.getvalue(), file_name="output.csv", mime="text/csv")

        #App Component 3: Think!Think!Think! - Introducing a News Filtering Option
        st.write("🔍 **Filter by Category**")
        categories = ['All', 'Business', 'Opinion', 'Political_gossip', 'Sports', 'World_news']

        col1, col2, col3, col4, col5, col6 = st.columns(6)
        selected_category = 'All'
        
        with col1:
            if st.button("All"):
                selected_category = 'All'
        with col2:
            if st.button("📈 Business"):
                selected_category = 'Business'
        with col3:
            if st.button("🗣 Opinion"):
                selected_category = 'Opinion'
        with col4:
            if st.button("🏛 Political Gossip"):
                selected_category = 'Political_gossip'
        with col5:
            if st.button("⚽ Sports"):
                selected_category = 'Sports'
        with col6:
            if st.button("🌎 World News"):
                selected_category = 'World_news'

        if selected_category != 'All':
            filtered_df = df[df['class'] == selected_category]
        else:
            filtered_df = df

        st.write(f"🔎 Showing news articles in category: {selected_category}")
        st.dataframe(filtered_df[['content', 'class']])
        
# Add a separator
st.markdown("---")

## ====================== Q&A ====================== ##
st.header("💬 Ask a Question About the News")
question = st.text_input("❓ Ask a question:")
context = st.text_area("📰 Provide the news article or content:", height=150)

if question and context.strip():
    result = qa_pipeline(question=question, context=context)
    st.success(f"✅ Answer: {result['answer']}")