import streamlit as st
import pandas as pd
import requests
import json
import base64
import plotly.express as px
import plotly.graph_objects as go
import os
from io import BytesIO
from datetime import datetime

# Set page configuration
st.set_page_config(
    page_title="News Summarization & Analysis",
    page_icon="📰",
    layout="wide",
    initial_sidebar_state="expanded"
)

# API endpoint (Flask backend)
API_URL = "http://0.0.0.0:8000"

def get_company_news(company_name):
    """Fetch news articles for a given company via API"""
    try:
        response = requests.get(f"{API_URL}/news/{company_name}")
        if response.status_code == 200:
            return response.json()
        else:
            st.error(f"Error fetching news: {response.text}")
            return None
    except Exception as e:
        st.error(f"API connection error: {str(e)}")
        return None

def get_analysis(company_name, articles):
    """Get sentiment analysis and comparative analysis via API"""
    try:
        response = requests.post(
            f"{API_URL}/analyze", 
            json={
                "company": company_name,
                "articles": articles
            }
        )
        if response.status_code == 200:
            return response.json()
        else:
            st.error(f"Error analyzing content: {response.text}")
            return None
    except Exception as e:
        st.error(f"API connection error: {str(e)}")
        return None

def get_tts(text, language='hi'):
    """Get TTS audio in the specified language via API"""
    try:
        response = requests.post(
            f"{API_URL}/tts", 
            json={
                "text": text,
                "language": language
            }
        )
        if response.status_code == 200:
            return response.content, language
        else:
            st.error(f"Error generating speech: {response.text}")
            return None, language
    except Exception as e:
        st.error(f"API connection error: {str(e)}")
        return None, language

def create_audio_player(audio_bytes):
    """Create an HTML audio player for the TTS audio"""
    audio_base64 = base64.b64encode(audio_bytes).decode()
    audio_html = f"""
        <audio controls>
            <source src="data:audio/mp3;base64,{audio_base64}" type="audio/mp3">
            Your browser does not support the audio element.
        </audio>
    """
    return audio_html

def display_article_details(articles):
    """Display detailed information about each article in a card layout"""
    st.markdown("""
    <style>
    .article-card {
        background-color: #f9f9f9;
        border-radius: 10px;
        padding: 20px;
        margin-bottom: 20px;
        border-left: 5px solid #4CAF50;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
    }
    .article-negative {
        border-left: 5px solid #F44336;
    }
    .article-neutral {
        border-left: 5px solid #9E9E9E;
    }
    .article-title {
        font-size: 18px;
        font-weight: bold;
        margin-bottom: 10px;
    }
    .article-meta {
        color: #666;
        font-size: 14px;
        margin-bottom: 10px;
    }
    .article-summary {
        margin-bottom: 15px;
    }
    .article-sentiment {
        display: inline-block;
        padding: 5px 10px;
        border-radius: 20px;
        font-size: 14px;
        margin-right: 10px;
    }
    .sentiment-positive {
        background-color: rgba(76, 175, 80, 0.2);
        color: #2E7D32;
    }
    .sentiment-negative {
        background-color: rgba(244, 67, 54, 0.2);
        color: #C62828;
    }
    .sentiment-neutral {
        background-color: rgba(158, 158, 158, 0.2);
        color: #616161;
    }
    .topic-tag {
        display: inline-block;
        background-color: #E0E0E0;
        padding: 3px 10px;
        border-radius: 15px;
        margin-right: 8px;
        margin-bottom: 8px;
        font-size: 12px;
    }
    </style>
    """, unsafe_allow_html=True)
    
    cols = st.columns(1)
    
    for i, article in enumerate(articles):
        sentiment = article['Sentiment']
        sentiment_class = ""
        tag_class = ""
        
        if sentiment == "Positive":
            sentiment_class = "article-positive"
            tag_class = "sentiment-positive"
        elif sentiment == "Negative":
            sentiment_class = "article-negative"
            tag_class = "sentiment-negative"
        else:
            sentiment_class = "article-neutral"
            tag_class = "sentiment-neutral"
        
        article_html = f"""
        <div class="article-card {sentiment_class}">
            <div class="article-title">{article['Title']}</div>
            <div class="article-meta">
                Source: {article.get('Source', 'Unknown')} | 
                Date: {article.get('Date', 'N/A')}
            </div>
            <div class="article-summary">{article['Summary']}</div>
            <div class="article-sentiment {tag_class}">{sentiment}</div>
        """
        
        # Add topics as tags
        if 'Topics' in article and article['Topics']:
            article_html += '<div class="article-topics">'
            for topic in article['Topics']:
                article_html += f'<span class="topic-tag">{topic}</span>'
            article_html += '</div>'
        
        article_html += f"""
            <div style="margin-top: 10px;">
                <a href="{article.get('URL', '#')}" target="_blank" style="text-decoration: none;">
                    <span style="color: #1E88E5;">Read original article →</span>
                </a>
            </div>
        </div>
        """
        
        cols[0].markdown(article_html, unsafe_allow_html=True)

def display_sentiment_distribution(analysis):
    """Display sentiment distribution chart with enhanced styling"""
    if 'Comparative Sentiment Score' in analysis and 'Sentiment Distribution' in analysis['Comparative Sentiment Score']:
        dist = analysis['Comparative Sentiment Score']['Sentiment Distribution']
        data = {
            'Sentiment': list(dist.keys()),
            'Count': list(dist.values())
        }
        df = pd.DataFrame(data)
        
        # Create color map
        color_map = {
            'Positive': '#4CAF50', 
            'Negative': '#F44336', 
            'Neutral': '#9E9E9E'
        }
        
        # Create a card container for the chart
        st.markdown("""
        <div style="background-color:white; padding:20px; border-radius:10px; box-shadow:0 4px 6px rgba(0,0,0,0.1); margin-bottom:20px;">
            <h3 style="margin-bottom:15px; border-bottom:1px solid #eee; padding-bottom:10px;">Sentiment Distribution</h3>
        </div>
        """, unsafe_allow_html=True)
        
        # Create pie chart for sentiment distribution
        labels = list(dist.keys())
        values = list(dist.values())
        colors = [color_map[label] for label in labels]
        
        # Create two columns for different chart types
        col1, col2 = st.columns(2)
        
        with col1:
            # Bar chart
            fig_bar = px.bar(
                df, 
                x='Sentiment', 
                y='Count',
                color='Sentiment',
                color_discrete_map=color_map,
                title="Sentiment Distribution (Bar Chart)"
            )
            fig_bar.update_layout(
                plot_bgcolor='rgba(0,0,0,0)',
                paper_bgcolor='rgba(0,0,0,0)',
                font=dict(size=14),
                margin=dict(l=20, r=20, t=40, b=20),
                height=350
            )
            st.plotly_chart(fig_bar, use_container_width=True)
            
        with col2:
            # Pie chart
            fig_pie = go.Figure(data=[go.Pie(
                labels=labels, 
                values=values,
                marker=dict(colors=colors),
                textinfo='percent+label',
                hole=.4
            )])
            fig_pie.update_layout(
                title_text="Sentiment Distribution (Pie Chart)",
                annotations=[dict(text='Sentiment', x=0.5, y=0.5, font_size=14, showarrow=False)],
                plot_bgcolor='rgba(0,0,0,0)',
                paper_bgcolor='rgba(0,0,0,0)',
                font=dict(size=14),
                margin=dict(l=20, r=20, t=40, b=20),
                height=350
            )
            st.plotly_chart(fig_pie, use_container_width=True)
            
        # Add a summary of the sentiment distribution
        total = sum(values)
        if total > 0:
            percentages = {label: (count/total*100) for label, count in zip(labels, values)}
            
            # Create a summary card
            summary_html = """
            <div style="background-color:#f8f9fa; padding:15px; border-radius:8px; margin-top:10px;">
                <h4 style="margin-bottom:10px;">Summary</h4>
                <p style="font-size:15px; line-height:1.5;">
            """
            
            for label in labels:
                if label in percentages:
                    color = color_map[label]
                    summary_html += f'<span style="color:{color}; font-weight:bold;">{label}</span>: {percentages[label]:.1f}% | '
            
            summary_html = summary_html.rstrip(' | ') + '</p></div>'
            st.markdown(summary_html, unsafe_allow_html=True)

def display_topic_analysis(analysis):
    """Display topic analysis visualization"""
    if 'Comparative Sentiment Score' in analysis and 'Topic Overlap' in analysis['Comparative Sentiment Score']:
        topic_data = analysis['Comparative Sentiment Score']['Topic Overlap']
        
        # Prepare data for visualization
        all_topics = set()
        if 'Common Topics' in topic_data:
            all_topics.update(topic_data['Common Topics'])
        
        for i in range(1, 11):  # Check for unique topics in each article
            key = f"Unique Topics in Article {i}"
            if key in topic_data and topic_data[key]:
                all_topics.update(topic_data[key])
        
        # Count topic occurrences across articles
        topic_counts = {}
        for topic in all_topics:
            count = 0
            if 'Common Topics' in topic_data and topic in topic_data['Common Topics']:
                count += len(analysis['Articles'])  # All articles have common topics
            
            for i in range(1, 11):
                key = f"Unique Topics in Article {i}"
                if key in topic_data and topic in topic_data[key]:
                    count += 1
                    
            topic_counts[topic] = count
            
        # Create DataFrame and visualization
        topic_df = pd.DataFrame({
            'Topic': list(topic_counts.keys()),
            'Occurrence': list(topic_counts.values())
        }).sort_values('Occurrence', ascending=False)
        
        fig = px.bar(
            topic_df,
            x='Topic',
            y='Occurrence',
            title="Topic Distribution Across Articles",
            color='Occurrence',
            color_continuous_scale=px.colors.sequential.Viridis
        )
        st.plotly_chart(fig, use_container_width=True)

def display_comparative_analysis(analysis):
    """Display comparative analysis details"""
    if 'Comparative Sentiment Score' in analysis and 'Coverage Differences' in analysis['Comparative Sentiment Score']:
        differences = analysis['Comparative Sentiment Score']['Coverage Differences']
        
        st.subheader("Comparative Analysis")
        for i, diff in enumerate(differences):
            with st.expander(f"Comparison {i+1}"):
                st.write(f"**Comparison**: {diff['Comparison']}")
                st.write(f"**Impact**: {diff['Impact']}")

# Main app layout with enhanced design and better readability
st.markdown("""
<style>
    .main-header {
        text-align: center;
        padding: 2rem 0;
        background: linear-gradient(to right, #2E7D32, #1565C0);
        color: white;
        border-radius: 10px;
        margin-bottom: 30px;
        box-shadow: 0 4px 12px rgba(0,0,0,0.2);
    }
    .app-title {
        font-size: 36px;
        font-weight: bold;
        text-shadow: 1px 1px 3px rgba(0,0,0,0.3);
        margin-bottom: 15px;
    }
    .app-description {
        font-size: 18px;
        color: white;
        max-width: 800px;
        margin: 0 auto;
        line-height: 1.6;
        text-shadow: 0px 1px 2px rgba(0,0,0,0.2);
    }
    .benefits-container {
        display: flex;
        justify-content: center;
        gap: 20px;
        margin-top: 20px;
        flex-wrap: wrap;
    }
    .benefit-item {
        background-color: rgba(255,255,255,0.25);
        padding: 8px 15px;
        border-radius: 20px;
        font-size: 14px;
        font-weight: 500;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
        text-shadow: 0px 1px 1px rgba(0,0,0,0.1);
    }
</style>

<div class="main-header">
    <div class="app-title">📰 News Summarization & Sentiment Analysis</div>
    <p class="app-description">
        Analyze recent news articles about any company. Get sentiment analysis, topic extraction, 
        and multilingual text-to-speech summaries instantly in 10 different languages.
    </p>
    <div class="benefits-container">
        <div class="benefit-item">✅ Real-time News Analysis</div>
        <div class="benefit-item">📊 Sentiment Visualization</div>
        <div class="benefit-item">🔍 Topic Extraction</div>
        <div class="benefit-item">🎧 Multilingual Text-to-Speech</div>
    </div>
</div>
""", unsafe_allow_html=True)

# Input form with enhanced styling
st.markdown("""
<style>
    .search-container {
        background-color: white;
        padding: 25px;
        border-radius: 10px;
        box-shadow: 0 4px 12px rgba(0,0,0,0.1);
        margin-bottom: 30px;
    }
    .search-title {
        font-size: 20px;
        font-weight: bold;
        margin-bottom: 15px;
        color: #333;
    }
    .search-description {
        color: #666;
        margin-bottom: 20px;
        font-size: 16px;
    }
</style>
<div class="search-container">
    <div class="search-title">🔍 Search for Company News</div>
    <div class="search-description">
        Enter a company name below to analyze its recent news coverage.
        Try companies like Tesla, Apple, Microsoft, Google, or Amazon.
    </div>
</div>
""", unsafe_allow_html=True)

with st.form("search_form"):
    col1, col2 = st.columns([3, 1])
    with col1:
        company_name = st.text_input("Company Name", placeholder="Enter company name (e.g., Tesla)", label_visibility="collapsed")
    with col2:
        submit_button = st.form_submit_button("🔍 Analyze News")
    
    # Add some example buttons below the form
    st.markdown("""
    <style>
        .example-row {
            display: flex;
            gap: 10px;
            margin-top: 10px;
            flex-wrap: wrap;
            justify-content: center;
        }
        .example-chip {
            background-color: #f0f2f6;
            border-radius: 20px;
            padding: 5px 15px;
            font-size: 12px;
            cursor: pointer;
            transition: all 0.2s;
        }
        .example-chip:hover {
            background-color: #4CAF50;
            color: white;
        }
    </style>
    <div style="text-align: center; margin-top: 10px; font-size: 12px; color: #666;">
        Try analyzing news for:
        <div class="example-row">
            <div class="example-chip">Tesla</div>
            <div class="example-chip">Apple</div>
            <div class="example-chip">Microsoft</div>
            <div class="example-chip">Google</div>
            <div class="example-chip">Amazon</div>
        </div>
    </div>
    """, unsafe_allow_html=True)

# Process form submission
if submit_button and company_name:
    with st.spinner(f"Fetching news articles about {company_name}..."):
        articles_data = get_company_news(company_name)
        
    if articles_data and 'articles' in articles_data and len(articles_data['articles']) > 0:
        articles = articles_data['articles']
        
        with st.spinner("Performing sentiment analysis..."):
            analysis_result = get_analysis(company_name, articles)
            
        if analysis_result:
            # Store complete analysis in session state
            st.session_state.analysis = analysis_result
            
            # Display summary and stats
            st.header(f"Analysis Results for {company_name}")
            
            # Create a nice header with company logo or icon
            company_icon = "🏢"  # Default company icon
            if company_name.lower() == "tesla":
                company_icon = "🚗"
            elif company_name.lower() == "apple":
                company_icon = "🍎"
            elif company_name.lower() == "microsoft":
                company_icon = "💻"
            elif company_name.lower() == "amazon":
                company_icon = "📦"
            elif company_name.lower() == "google":
                company_icon = "🔍"
                
            st.markdown(f"""
            <div style="background-color:#f0f2f6; padding:20px; border-radius:10px; margin-bottom:20px;">
                <h1 style="text-align:center; margin-bottom:20px;">{company_icon} {company_name} News Analysis</h1>
                <p style="text-align:center; font-size:16px; color:#666;">
                    Analysis of {len(analysis_result['Articles'])} news articles | Generated on {datetime.now().strftime('%B %d, %Y')}
                </p>
            </div>
            """, unsafe_allow_html=True)
            
            # Display visualization tabs with custom styling
            st.markdown("""
            <style>
            .stTabs [data-baseweb="tab-list"] {
                gap: 8px;
            }
            .stTabs [data-baseweb="tab"] {
                border-radius: 4px 4px 0px 0px;
                padding: 10px 16px;
                background-color: #f0f2f6;
            }
            .stTabs [aria-selected="true"] {
                background-color: #4CAF50 !important;
                color: white !important;
            }
            </style>
            """, unsafe_allow_html=True)
            
            tab1, tab2, tab3, tab4 = st.tabs(["📊 Overview", "😊 Sentiment Analysis", "🔍 Topic Analysis", "📰 Article Details"])
            
            with tab1:
                # Create a card-style container for the summary
                st.markdown("""
                <div style="background-color:white; padding:25px; border-radius:10px; box-shadow:0 4px 6px rgba(0,0,0,0.1); margin-bottom:20px;">
                    <h3 style="margin-bottom:15px; border-bottom:1px solid #eee; padding-bottom:10px;">Executive Summary</h3>
                """, unsafe_allow_html=True)
                
                summary_text = analysis_result.get("Final Sentiment Analysis", "No summary available")
                st.markdown(f"<p style='font-size:16px; line-height:1.6;'>{summary_text}</p>", unsafe_allow_html=True)
                
                st.markdown("</div>", unsafe_allow_html=True)
                
                if "Final Sentiment Analysis" in analysis_result:
                    # Language selection for TTS
                    language_options = {
                        "hi": "Hindi",
                        "en": "English",
                        "es": "Spanish",
                        "fr": "French",
                        "de": "German",
                        "ja": "Japanese",
                        "zh-CN": "Chinese",
                        "ru": "Russian",
                        "ar": "Arabic",
                        "it": "Italian"
                    }
                    
                    selected_language = st.selectbox(
                        "Select Language for Text-to-Speech",
                        options=list(language_options.keys()),
                        format_func=lambda x: language_options[x],
                        index=0  # Default to Hindi
                    )
                    
                    with st.spinner(f"Generating {language_options[selected_language]} text-to-speech..."):
                        audio_bytes, language = get_tts(analysis_result["Final Sentiment Analysis"], selected_language)
                    
                    if audio_bytes:
                        st.markdown(f"""
                        <div style="background-color:white; padding:25px; border-radius:10px; box-shadow:0 4px 6px rgba(0,0,0,0.1);">
                            <h3 style="margin-bottom:15px; border-bottom:1px solid #eee; padding-bottom:10px;">
                                <span style="vertical-align:middle;">🔊</span> {language_options[language]} Text-to-Speech Summary
                            </h3>
                        """, unsafe_allow_html=True)
                        
                        st.markdown(create_audio_player(audio_bytes), unsafe_allow_html=True)
                        st.markdown("</div>", unsafe_allow_html=True)
            
            with tab2:
                st.subheader("Sentiment Distribution")
                display_sentiment_distribution(analysis_result)
                display_comparative_analysis(analysis_result)
                
            with tab3:
                st.subheader("Topic Analysis")
                display_topic_analysis(analysis_result)
                
            with tab4:
                st.subheader("Article Details")
                display_article_details(analysis_result['Articles'])

            # Add video download tab
            tab5 = st.tabs(["📹 Demo Video"])[0]
            with tab5:
                st.subheader("Record and Download Demo")
                if st.button("Record Demo (30s)"):
                    with st.spinner("Recording demo video..."):
                        try:
                            from record_demo import record_demo
                            record_demo()
                            st.success("Recording completed!")
                            
                            # Display download button
                            with open('demo_video.mp4', 'rb') as video_file:
                                video_bytes = video_file.read()
                                st.download_button(
                                    label="Download Demo Video",
                                    data=video_bytes,
                                    file_name="company_analysis_demo.mp4",
                                    mime="video/mp4"
                                )
                        except Exception as e:
                            st.error(f"Error recording video: {str(e)}")
                
            # Display JSON output option
            st.subheader("Raw JSON Output")
            with st.expander("Show JSON"):
                st.json(analysis_result)
                
        else:
            st.error("Failed to perform analysis. Please try again.")
    else:
        st.warning(f"No news articles found for {company_name}. Please try another company name.")

# Footer with enhanced design and improved readability
st.markdown("""
<style>
    .footer {
        background: linear-gradient(to right, #2E7D32, #1565C0);
        padding: 30px 10px;
        color: white;
        border-radius: 10px;
        text-align: center;
        margin-top: 40px;
        box-shadow: 0 4px 12px rgba(0,0,0,0.2);
    }
    .footer-content {
        max-width: 800px;
        margin: 0 auto;
    }
    .footer-title {
        font-size: 22px;
        margin-bottom: 15px;
        font-weight: bold;
        text-shadow: 1px 1px 2px rgba(0,0,0,0.3);
    }
    .footer-text {
        font-size: 15px;
        line-height: 1.6;
        margin-bottom: 15px;
        text-shadow: 0px 1px 1px rgba(0,0,0,0.2);
    }
    .footer-features {
        display: flex;
        justify-content: center;
        gap: 15px;
        margin: 20px 0;
        flex-wrap: wrap;
    }
    .footer-feature {
        background-color: rgba(255,255,255,0.25);
        padding: 8px 15px;
        border-radius: 20px;
        font-size: 14px;
        font-weight: 500;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
        text-shadow: 0px 1px 1px rgba(0,0,0,0.1);
    }
    .footer-copyright {
        margin-top: 15px;
        font-size: 14px;
        opacity: 0.9;
        text-shadow: 0px 1px 1px rgba(0,0,0,0.1);
    }
</style>

<div class="footer">
    <div class="footer-content">
        <div class="footer-title">📰 News Summarization & Analysis Application</div>
        <div class="footer-text">
            A powerful tool for analyzing news content, extracting sentiments, and generating insights.
            Get comprehensive analysis of any company's news coverage within seconds.
        </div>
        <div class="footer-features">
            <div class="footer-feature">⚡ Real-time News Extraction</div>
            <div class="footer-feature">📊 Sentiment Analysis</div>
            <div class="footer-feature">🔍 Topic Analysis</div>
            <div class="footer-feature">🎧 Multilingual Text-to-Speech</div>
        </div>
        <div class="footer-copyright">Created with Streamlit • {datetime.now().year}</div>
    </div>
</div>
""", unsafe_allow_html=True)