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

# Load fine-tuned models and tokenizers for both functions
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
from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer

model_name = "distilbert-base-cased-distilled-squad"  # Example model
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)

text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
qa_pipeline = pipeline("question-answering", model=model)

# Streamlit App 
st.set_page_config(page_title="News Classification & Q&A", page_icon="📰", layout="wide")
st.markdown(
    """
    <style>
        body {background-color: #f4f4f4;}
        .title {text-align: center; font-size: 36px; font-weight: bold; color: #ff4b4b;}
        .subheader {font-size: 24px; color: #333; margin-bottom: 20px; text-align: right;}
        .stTextInput>div>div>input {border-radius: 10px;}
        .stTextArea>div>div>textarea {border-radius: 10px;}
        .stButton>button {border-radius: 10px; background-color: #ff4b4b; color: white; font-weight: bold;}
    </style>
    """,
    unsafe_allow_html=True,
)

st.markdown('<h1 class="title">📰 News Classification & Q&A App</h1>', unsafe_allow_html=True)

col1, col2 = st.columns([2, 1])
with col2:
    # ====================== Component 1: News Classification ====================== #
    st.markdown('<h2 class="subheader">📌 Classify News Articles</h2>', unsafe_allow_html=True)
    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.success("✅ File successfully uploaded!")
            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'\s+', ' ', text)  # Remove extra spaces
                text = re.sub(r'[^a-z\s]', '', text)  # Remove special characters & numbers
                return text

            # Apply preprocessing and classification
            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")
            
            # Show results
            st.markdown("### 🔹 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="classified_news.csv", mime="text/csv")

    # ====================== Component 2: Q&A ====================== #
    st.markdown('<h2 class="subheader">❓ Ask a Question About the News</h2>', unsafe_allow_html=True)
    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:", height=150)

    if question and context.strip():
        model_name_qa = "distilbert-base-uncased-distilled-squad"
        qa_pipeline = pipeline("question-answering", model=model_name_qa, tokenizer=model_name_qa)
        result = qa_pipeline(question=question, context=context)
        
        # Display Answer
        if 'answer' in result and result['answer']:
            st.markdown(f"### ✅ Answer: {result['answer']}")
        else:
            st.markdown("### ❌ No answer found in the provided content.")