"""Streamlit frontend for the News Summarization application."""

import streamlit as st
import pandas as pd
import json
import os
import plotly.express as px
import altair as alt
from utils import analyze_company_data  # Import the analysis function directly

# Set page config
st.set_page_config(
    page_title="News Summarization App",
    page_icon="📰",
    layout="wide"
)

# Show loading message
with st.spinner("Initializing the application... Please wait while we load the models."):
    # Initialize components
    try:
        from utils import NewsExtractor, SentimentAnalyzer, TextSummarizer, TextToSpeechConverter
        st.success("Application initialized successfully!")
    except Exception as e:
        st.error(f"Error initializing application: {str(e)}")
        st.info("Please try refreshing the page.")

def process_company(company_name):
    """Process company data directly."""
    try:
        # Call the analysis function directly from utils
        data = analyze_company_data(company_name)
        
        # Generate audio if needed
        if 'summary' in data:
            from gtts import gTTS
            tts = gTTS(text=data['summary'], lang='en')
            audio_path = os.path.join('audio_output', f'{company_name}_summary.mp3')
            os.makedirs('audio_output', exist_ok=True)
            tts.save(audio_path)
            data['audio_path'] = audio_path
            
            return data
    except Exception as e:
        st.error(f"Error processing company: {str(e)}")
        return {"articles": [], "comparative_sentiment_score": {}, "final_sentiment_analysis": "", "audio_path": None}

def main():
    st.title("News Summarization App")
    st.write("Analyze news articles and get sentiment analysis for any company.")

    # User input
    company_name = st.text_input("Enter company name:", "Tesla")

    if st.button("Analyze"):
        with st.spinner("Analyzing news articles..."):
            try:
                # Process company data
                data = analyze_company_data(company_name)
                
                if not data["articles"]:
                    st.error("No articles found for analysis.")
                    return
                
                # Display overall sentiment
                st.subheader("Overall Sentiment Analysis")
                st.write(data["final_sentiment_analysis"])
                
                # Create DataFrame for sentiment scores
                sentiment_df = pd.DataFrame(data["comparative_sentiment_score"])
                
                # Display sentiment distribution by source
                st.subheader("Sentiment Distribution by Source")
                
                # Convert sentiment labels to numeric values for visualization
                sentiment_map = {'positive': 1, 'neutral': 0, 'negative': -1}
                numeric_df = sentiment_df.replace(sentiment_map)
                
                # Calculate sentiment distribution
                sentiment_dist = numeric_df.apply(lambda x: x.value_counts(normalize=True).to_dict())
                
                # Create a new DataFrame for visualization
                viz_data = []
                for source in sentiment_df.columns:
                    dist = sentiment_dist[source]
                    for sentiment, percentage in dist.items():
                        viz_data.append({
                            'Source': source,
                            'Sentiment': sentiment,
                            'Percentage': percentage * 100
                        })
                
                viz_df = pd.DataFrame(viz_data)
                
                # Create stacked bar chart
                fig = px.bar(viz_df, 
                           x='Source', 
                           y='Percentage',
                           color='Sentiment',
                           title='Sentiment Distribution by Source',
                           barmode='stack')
                
                fig.update_layout(
                    yaxis_title='Percentage',
                    xaxis_title='News Source',
                    legend_title='Sentiment'
                )
                
                st.plotly_chart(fig)
                
                # Display fine-grained sentiment analysis
                st.subheader("Fine-grained Sentiment Analysis")
                
                # Create tabs for different fine-grained analyses
                tab1, tab2, tab3 = st.tabs(["Financial Sentiment", "Emotional Sentiment", "ESG Sentiment"])
                
                with tab1:
                    st.write("Financial Market Impact Analysis")
                    financial_data = []
                    for article in data["articles"]:
                        if "financial_sentiment" in article:
                            financial_data.append({
                                "Article": article["title"],
                                "Financial Impact": article["financial_sentiment"]
                            })
                    if financial_data:
                        st.dataframe(pd.DataFrame(financial_data))
                    else:
                        st.info("Financial sentiment analysis not available for these articles.")
                
                with tab2:
                    st.write("Emotional Sentiment Analysis")
                    emotional_data = []
                    for article in data["articles"]:
                        if "emotional_sentiment" in article:
                            emotional_data.append({
                                "Article": article["title"],
                                "Emotional Impact": article["emotional_sentiment"]
                            })
                    if emotional_data:
                        st.dataframe(pd.DataFrame(emotional_data))
                    else:
                        st.info("Emotional sentiment analysis not available for these articles.")
                
                with tab3:
                    st.write("ESG (Environmental, Social, Governance) Analysis")
                    esg_data = []
                    for article in data["articles"]:
                        if "esg_sentiment" in article:
                            esg_data.append({
                                "Article": article["title"],
                                "ESG Impact": article["esg_sentiment"]
                            })
                    if esg_data:
                        st.dataframe(pd.DataFrame(esg_data))
                    else:
                        st.info("ESG sentiment analysis not available for these articles.")
                
                # Display articles with detailed sentiment analysis
                st.subheader("Recent Articles")
                for article in data["articles"]:
                    with st.expander(article["title"]):
                        st.write(f"**Source:** {article['source']}")
                        st.write(f"**Summary:** {article['summary']}")
                        st.write(f"**Overall Sentiment:** {article['sentiment']}")
                        
                        # Display fine-grained sentiment if available
                        if "financial_sentiment" in article:
                            st.write(f"**Financial Impact:** {article['financial_sentiment']}")
                        if "emotional_sentiment" in article:
                            st.write(f"**Emotional Impact:** {article['emotional_sentiment']}")
                        if "esg_sentiment" in article:
                            st.write(f"**ESG Impact:** {article['esg_sentiment']}")
                        
                        st.write(f"**URL:** [{article['url']}]({article['url']})")
                
            except Exception as e:
                st.error(f"Error analyzing company data: {str(e)}")
                print(f"Error: {str(e)}")

if __name__ == "__main__":
    main()