import streamlit as st
import os
from datetime import datetime
import pandas as pd
from web_scraper import scrape_article_content
from brand_analyzer import BrandAnalyzer
from search_engines import multi_engine_search, batch_analyze_brands
from db_operations import (
    save_analysis_to_db, get_historical_analyses, get_all_mentions,
    save_co_mentions, get_co_mention_network, create_scheduled_job,
    get_scheduled_jobs
)
import plotly.express as px
import plotly.graph_objects as go
import networkx as nx
from collections import Counter, defaultdict
from scheduler import get_scheduler
from database import init_database
from dotenv import load_dotenv

# Load environment variables and initialize database
load_dotenv()
init_database()

# Page configuration
st.set_page_config(
    page_title="Brand Monitoring Dashboard",
    page_icon="🔍",
    layout="wide"
)

# Initialize scheduler
try:
    scheduler = get_scheduler()
except Exception as e:
    st.warning(f"Scheduler initialization warning: {e}")

# Initialize session state
if 'batch_results' not in st.session_state:
    st.session_state.batch_results = {}
if 'current_page' not in st.session_state:
    st.session_state.current_page = 'Analysis'
if 'selected_analysis_ids' not in st.session_state:
    st.session_state.selected_analysis_ids = []

# Sidebar navigation
st.sidebar.title("🔍 Brand Monitor Pro")
page = st.sidebar.radio(
    "Navigation",
    ["Analysis", "Dashboard", "Co-Mention Network", "Scheduled Monitoring", "History"]
)

def create_csv_export(results: dict) -> str:
    """Create CSV content from batch analysis results"""
    csv_data = []
    
    for brand_name, analysis_results in results.items():
        for result in analysis_results:
            analysis = result.get('analysis', {})
            
            # Add explicit mentions
            for mention in analysis.get('explicit_mentions', []):
                csv_data.append({
                    'Brand': brand_name,
                    'URL': result['url'],
                    'Article Title': result['title'],
                    'Source': result.get('source', 'unknown'),
                    'Mention Type': 'Explicit',
                    'Mention Text': mention.get('mention', ''),
                    'Context': mention.get('context', ''),
                    'Sentiment': mention.get('sentiment', ''),
                    'Explanation': mention.get('explanation', ''),
                    'Timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
                })
            
            # Add indirect mentions
            for mention in analysis.get('indirect_mentions', []):
                csv_data.append({
                    'Brand': brand_name,
                    'URL': result['url'],
                    'Article Title': result['title'],
                    'Source': result.get('source', 'unknown'),
                    'Mention Type': 'Indirect',
                    'Mention Text': mention.get('reference', ''),
                    'Context': mention.get('context', ''),
                    'Sentiment': mention.get('sentiment', ''),
                    'Explanation': mention.get('explanation', ''),
                    'Timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
                })
    
    if csv_data:
        df = pd.DataFrame(csv_data)
        return df.to_csv(index=False)
    else:
        return "No data to export"

def render_analysis_page():
    """Render the main analysis page with batch processing"""
    st.title("🔍 Brand Mention Analysis")
    st.markdown("Analyze multiple brands across different search engines simultaneously")
    
    # Configuration sidebar
    with st.sidebar:
        st.header("🔧 Configuration")
        
        search_query = st.text_input(
            "Search Query",
            placeholder="e.g., AI startups 2024",
            help="Base search query to find relevant articles"
        )
        
        brand_names_input = st.text_area(
            "Brand Names (one per line)",
            placeholder="OpenAI\nAnthropic\nGoogle AI",
            help="Enter brand names to monitor, one per line"
        )
        
        # Search engine selection
        st.subheader("🌐 Search Engines")
        use_google = st.checkbox("Google (SerpAPI)", value=True)
        use_bing = st.checkbox("Bing")
        use_duckduckgo = st.checkbox("DuckDuckGo")
        
        search_engines = []
        if use_google:
            search_engines.append('google')
        if use_bing:
            search_engines.append('bing')
        if use_duckduckgo:
            search_engines.append('duckduckgo')
        
        num_results = st.slider(
            "Results per engine",
            min_value=5,
            max_value=15,
            value=10
        )
        
        custom_prompt = st.text_area(
            "Custom Analysis Prompt (Optional)",
            placeholder="Leave empty for default analysis...",
            height=100
        )
        
        analyze_button = st.button("🚀 Start Batch Analysis", type="primary", use_container_width=True)
        
        # Export section
        if st.session_state.batch_results:
            st.markdown("---")
            st.subheader("📥 Export Results")
            
            csv_content = create_csv_export(st.session_state.batch_results)
            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
            
            st.download_button(
                label="💾 Download CSV Report",
                data=csv_content,
                file_name=f"batch_brand_analysis_{timestamp}.csv",
                mime="text/csv",
                use_container_width=True
            )
    
    # Main content
    if analyze_button:
        if not search_query:
            st.error("⚠️ Please enter a search query")
            return
        
        if not brand_names_input.strip():
            st.error("⚠️ Please enter at least one brand name")
            return
        
        if not search_engines:
            st.error("⚠️ Please select at least one search engine")
            return
        
        # Parse brand names
        brand_names = [b.strip() for b in brand_names_input.strip().split('\n') if b.strip()]
        
        st.info(f"📊 Analyzing {len(brand_names)} brand(s) across {len(search_engines)} search engine(s)")
        
        # Batch analysis
        analyzer = BrandAnalyzer()
        batch_results = batch_analyze_brands(
            search_query,
            brand_names,
            search_engines,
            num_results,
            custom_prompt,
            analyzer,
            scrape_article_content
        )
        
        # Save to database and session state
        st.session_state.batch_results = batch_results
        
        for brand_name, results in batch_results.items():
            if results:
                # Save to database
                for engine in search_engines:
                    save_analysis_to_db(search_query, brand_name, engine, results)
                
                # Track co-mentions if multiple brands
                if len(brand_names) > 1:
                    for result in results:
                        if result.get('analysis', {}).get('explicit_mentions') or result.get('analysis', {}).get('indirect_mentions'):
                            # Find which brands are mentioned in this article
                            mentioned_brands = []
                            for other_brand in brand_names:
                                if other_brand != brand_name:
                                    # Check if other brand is mentioned
                                    article_content = result.get('content', '').lower()
                                    if other_brand.lower() in article_content:
                                        mentioned_brands.append(other_brand)
                            
                            if mentioned_brands:
                                mentioned_brands.append(brand_name)
                                # This will be saved when we have article_id
        
        st.success("✅ Batch analysis complete and saved to database!")
        st.rerun()
    
    # Display results
    if st.session_state.batch_results:
        st.markdown("---")
        st.subheader("📊 Analysis Results")
        
        # Summary metrics
        total_brands = len(st.session_state.batch_results)
        total_articles = sum(len(results) for results in st.session_state.batch_results.values())
        total_mentions = sum(
            sum(r.get('total_mentions', 0) for r in results)
            for results in st.session_state.batch_results.values()
        )
        
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("Brands Analyzed", total_brands)
        with col2:
            st.metric("Total Articles", total_articles)
        with col3:
            st.metric("Total Mentions", total_mentions)
        
        # Display results by brand
        for brand_name, results in st.session_state.batch_results.items():
            with st.expander(f"**{brand_name}** - {len(results)} articles"):
                if not results:
                    st.info("No results found")
                    continue
                
                # Brand-specific metrics
                mentions_count = sum(r.get('total_mentions', 0) for r in results)
                articles_with_mentions = sum(1 for r in results if r.get('total_mentions', 0) > 0)
                
                col1, col2 = st.columns(2)
                with col1:
                    st.metric("Articles with Mentions", articles_with_mentions)
                with col2:
                    st.metric("Total Mentions", mentions_count)
                
                # Show top mentions
                for i, result in enumerate(results[:5]):  # Show top 5
                    analysis = result.get('analysis', {})
                    if analysis.get('explicit_mentions') or analysis.get('indirect_mentions'):
                        st.markdown(f"**📄 {result['title'][:80]}...**")
                        st.caption(f"🔗 {result['url']} | Source: {result.get('source', 'unknown')}")
                        
                        for mention in analysis.get('explicit_mentions', [])[:2]:
                            sentiment_emoji = {"positive": "😊", "negative": "😞", "neutral": "😐"}.get(mention.get('sentiment'), "😐")
                            st.markdown(f"- {sentiment_emoji} *{mention.get('mention', '')}*")
    
    elif not st.session_state.batch_results:
        st.info("👈 Configure your analysis in the sidebar and click 'Start Batch Analysis' to begin")

def render_dashboard():
    """Render the analytics dashboard"""
    st.title("📊 Brand Analytics Dashboard")
    
    # Get historical data
    analyses = get_historical_analyses(limit=100)
    
    if not analyses:
        st.info("No historical data available. Run some analyses first!")
        return
    
    # Filter controls
    st.sidebar.subheader("📊 Dashboard Filters")
    
    # Brand filter
    all_brands = list(set(a.brand_name for a in analyses))
    selected_brands = st.sidebar.multiselect(
        "Filter by Brand",
        all_brands,
        default=all_brands[:5] if len(all_brands) > 5 else all_brands
    )
    
    # Time filter
    time_range = st.sidebar.selectbox(
        "Time Range",
        ["Last 24 hours", "Last 7 days", "Last 30 days", "All time"]
    )
    
    # Filter analyses
    filtered_analyses = [a for a in analyses if a.brand_name in selected_brands]
    
    # Summary metrics
    col1, col2, col3, col4 = st.columns(4)
    
    total_analyses = len(filtered_analyses)
    total_mentions = sum(a.total_mentions for a in filtered_analyses)
    avg_sentiment = sum(a.positive_count for a in filtered_analyses) / max(total_mentions, 1)
    
    with col1:
        st.metric("Total Analyses", total_analyses)
    with col2:
        st.metric("Total Mentions", total_mentions)
    with col3:
        st.metric("Avg Positive %", f"{avg_sentiment*100:.1f}%")
    with col4:
        active_brands = len(set(a.brand_name for a in filtered_analyses))
        st.metric("Active Brands", active_brands)
    
    # Sentiment Distribution Chart
    st.subheader("📈 Sentiment Distribution")
    
    sentiment_data = []
    for analysis in filtered_analyses:
        sentiment_data.append({
            'Positive': analysis.positive_count,
            'Negative': analysis.negative_count,
            'Neutral': analysis.neutral_count
        })
    
    if sentiment_data:
        total_positive = sum(d['Positive'] for d in sentiment_data)
        total_negative = sum(d['Negative'] for d in sentiment_data)
        total_neutral = sum(d['Neutral'] for d in sentiment_data)
        
        col1, col2 = st.columns(2)
        
        with col1:
            # Pie chart
            fig_pie = go.Figure(data=[go.Pie(
                labels=['Positive', 'Negative', 'Neutral'],
                values=[total_positive, total_negative, total_neutral],
                marker=dict(colors=['#00D26A', '#FF5C5C', '#FFD700'])
            )])
            fig_pie.update_layout(title="Overall Sentiment Distribution")
            st.plotly_chart(fig_pie, use_container_width=True)
        
        with col2:
            # Bar chart by brand
            brand_sentiment = defaultdict(lambda: {'positive': 0, 'negative': 0, 'neutral': 0})
            for analysis in filtered_analyses:
                brand_sentiment[analysis.brand_name]['positive'] += analysis.positive_count
                brand_sentiment[analysis.brand_name]['negative'] += analysis.negative_count
                brand_sentiment[analysis.brand_name]['neutral'] += analysis.neutral_count
            
            brands = list(brand_sentiment.keys())
            positive_vals = [brand_sentiment[b]['positive'] for b in brands]
            negative_vals = [brand_sentiment[b]['negative'] for b in brands]
            neutral_vals = [brand_sentiment[b]['neutral'] for b in brands]
            
            fig_bar = go.Figure(data=[
                go.Bar(name='Positive', x=brands, y=positive_vals, marker_color='#00D26A'),
                go.Bar(name='Negative', x=brands, y=negative_vals, marker_color='#FF5C5C'),
                go.Bar(name='Neutral', x=brands, y=neutral_vals, marker_color='#FFD700')
            ])
            fig_bar.update_layout(
                title="Sentiment by Brand",
                barmode='stack',
                xaxis_title="Brand",
                yaxis_title="Mentions"
            )
            st.plotly_chart(fig_bar, use_container_width=True)
    
    # Trend over time
    st.subheader("📅 Mention Trends Over Time")
    
    trend_data = []
    for analysis in filtered_analyses:
        trend_data.append({
            'Date': analysis.created_at.date(),
            'Brand': analysis.brand_name,
            'Mentions': analysis.total_mentions
        })
    
    if trend_data:
        df_trend = pd.DataFrame(trend_data)
        fig_trend = px.line(
            df_trend,
            x='Date',
            y='Mentions',
            color='Brand',
            title="Brand Mentions Over Time"
        )
        st.plotly_chart(fig_trend, use_container_width=True)
    
    # Detailed mentions table with filtering
    st.subheader("🔍 Detailed Mentions")
    
    # Get all mentions for filtered analyses
    all_mentions = []
    for analysis in filtered_analyses:
        mentions = get_all_mentions(analysis_id=analysis.id)
        all_mentions.extend(mentions)
    
    if all_mentions:
        # Sentiment filter
        sentiment_filter = st.multiselect(
            "Filter by Sentiment",
            ["positive", "negative", "neutral"],
            default=["positive", "negative", "neutral"]
        )
        
        # Sort options
        sort_by = st.selectbox(
            "Sort by",
            ["Date (Newest)", "Date (Oldest)", "Confidence (High to Low)", "Confidence (Low to High)"]
        )
        
        # Filter mentions
        filtered_mentions = [m for m in all_mentions if m.sentiment in sentiment_filter]
        
        # Sort mentions
        if sort_by == "Date (Newest)":
            filtered_mentions.sort(key=lambda x: x.created_at, reverse=True)
        elif sort_by == "Date (Oldest)":
            filtered_mentions.sort(key=lambda x: x.created_at)
        elif sort_by == "Confidence (High to Low)":
            filtered_mentions.sort(key=lambda x: x.confidence, reverse=True)
        else:
            filtered_mentions.sort(key=lambda x: x.confidence)
        
        # Display mentions
        for mention in filtered_mentions[:20]:  # Limit to 20
            sentiment_emoji = {"positive": "😊", "negative": "😞", "neutral": "😐"}.get(mention.sentiment, "😐")
            with st.expander(f"{sentiment_emoji} {mention.brand_name} - {mention.mention_type} ({mention.confidence:.0%} confidence)"):
                st.markdown(f"**Mention:** {mention.mention_text}")
                st.markdown(f"**Context:** {mention.context}")
                st.caption(f"**Explanation:** {mention.explanation}")
                st.caption(f"**Date:** {mention.created_at.strftime('%Y-%m-%d %H:%M')}")

def render_co_mention_network():
    """Render co-mention network visualization"""
    st.title("🕸️ Brand Co-Mention Network")
    st.markdown("Visualize which brands are frequently mentioned together in articles")
    
    co_mentions = get_co_mention_network()
    
    if not co_mentions:
        st.info("No co-mention data available. Analyze multiple brands together to see relationships!")
        return
    
    # Build network graph
    G = nx.Graph()
    
    # Add edges with weights
    edge_data = defaultdict(int)
    for cm in co_mentions:
        edge_data[(cm.brand1, cm.brand2)] += cm.co_occurrence_count
    
    for (brand1, brand2), count in edge_data.items():
        G.add_edge(brand1, brand2, weight=count)
    
    # Calculate layout
    pos = nx.spring_layout(G, k=2, iterations=50)
    
    # Create edge trace
    edge_traces = []
    for edge in G.edges():
        x0, y0 = pos[edge[0]]
        x1, y1 = pos[edge[1]]
        weight = G[edge[0]][edge[1]]['weight']
        
        edge_trace = go.Scatter(
            x=[x0, x1, None],
            y=[y0, y1, None],
            mode='lines',
            line=dict(width=weight*2, color='#888'),
            hoverinfo='text',
            text=f"{edge[0]} ↔ {edge[1]}: {weight} co-mentions",
            showlegend=False
        )
        edge_traces.append(edge_trace)
    
    # Create node trace
    node_x = []
    node_y = []
    node_text = []
    node_size = []
    
    for node in G.nodes():
        x, y = pos[node]
        node_x.append(x)
        node_y.append(y)
        
        # Calculate node size based on connections
        connections = G.degree(node)
        node_size.append(30 + connections * 10)
        node_text.append(f"{node}<br>Connections: {connections}")
    
    node_trace = go.Scatter(
        x=node_x,
        y=node_y,
        mode='markers+text',
        text=[node for node in G.nodes()],
        textposition="top center",
        hovertext=node_text,
        hoverinfo='text',
        marker=dict(
            size=node_size,
            color='#1f77b4',
            line=dict(width=2, color='white')
        ),
        showlegend=False
    )
    
    # Create figure
    fig = go.Figure(data=edge_traces + [node_trace])
    
    fig.update_layout(
        title="Brand Co-Mention Network",
        title_font_size=20,
        showlegend=False,
        hovermode='closest',
        margin=dict(b=0, l=0, r=0, t=40),
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        height=600
    )
    
    st.plotly_chart(fig, use_container_width=True)
    
    # Network statistics
    st.subheader("📊 Network Statistics")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("Total Brands", len(G.nodes()))
    with col2:
        st.metric("Total Relationships", len(G.edges()))
    with col3:
        density = nx.density(G)
        st.metric("Network Density", f"{density:.2%}")
    
    # Top co-mentions
    st.subheader("🔝 Top Co-Mentions")
    
    top_pairs = sorted(edge_data.items(), key=lambda x: x[1], reverse=True)[:10]
    
    for (brand1, brand2), count in top_pairs:
        st.write(f"**{brand1}** ↔ **{brand2}**: {count} co-mentions")

def render_scheduled_monitoring():
    """Render scheduled monitoring page"""
    st.title("⏰ Scheduled Brand Monitoring")
    st.markdown("Set up recurring brand analyses")
    
    # Create new schedule
    with st.expander("➕ Create New Schedule", expanded=True):
        col1, col2 = st.columns(2)
        
        with col1:
            schedule_query = st.text_input("Search Query", placeholder="AI technology news")
            schedule_brands = st.text_area(
                "Brand Names (one per line)",
                placeholder="OpenAI\nGoogle\nMicrosoft"
            )
        
        with col2:
            schedule_engines = st.multiselect(
                "Search Engines",
                ["google", "bing", "duckduckgo"],
                default=["google"]
            )
            schedule_frequency = st.selectbox(
                "Frequency",
                ["daily", "weekly", "monthly"]
            )
        
        if st.button("Create Schedule"):
            if schedule_query and schedule_brands:
                brands = [b.strip() for b in schedule_brands.split('\n') if b.strip()]
                job_id = create_scheduled_job(
                    schedule_query,
                    brands,
                    schedule_engines,
                    schedule_frequency
                )
                if job_id:
                    st.success(f"✅ Schedule created successfully! (ID: {job_id})")
                    st.rerun()
            else:
                st.error("Please fill in all fields")
    
    # List existing schedules
    st.subheader("📅 Active Schedules")
    
    jobs = get_scheduled_jobs(active_only=True)
    
    if not jobs:
        st.info("No active schedules. Create one above!")
    else:
        for job in jobs:
            with st.expander(f"🔔 {job.search_query} - {job.schedule_type}"):
                st.write(f"**Brands:** {job.brand_names}")
                st.write(f"**Engines:** {job.search_engines}")
                st.write(f"**Frequency:** {job.schedule_type}")
                if job.last_run:
                    st.write(f"**Last Run:** {job.last_run.strftime('%Y-%m-%d %H:%M')}")
                if job.next_run:
                    st.write(f"**Next Run:** {job.next_run.strftime('%Y-%m-%d %H:%M')}")
                st.caption(f"Created: {job.created_at.strftime('%Y-%m-%d')}")

def render_history():
    """Render analysis history"""
    st.title("📚 Analysis History")
    
    analyses = get_historical_analyses(limit=50)
    
    if not analyses:
        st.info("No historical analyses available")
        return
    
    # Create DataFrame
    history_data = []
    for a in analyses:
        history_data.append({
            'Date': a.created_at.strftime('%Y-%m-%d %H:%M'),
            'Brand': a.brand_name,
            'Query': a.search_query,
            'Engine': a.search_engine,
            'Articles': a.total_articles,
            'Mentions': a.total_mentions,
            'Positive': a.positive_count,
            'Negative': a.negative_count,
            'Neutral': a.neutral_count
        })
    
    df_history = pd.DataFrame(history_data)
    
    # Display with filtering
    brand_filter = st.multiselect(
        "Filter by Brand",
        df_history['Brand'].unique(),
        default=None
    )
    
    if brand_filter:
        df_history = df_history[df_history['Brand'].isin(brand_filter)]
    
    st.dataframe(df_history, use_container_width=True)

# Main routing
if page == "Analysis":
    render_analysis_page()
elif page == "Dashboard":
    render_dashboard()
elif page == "Co-Mention Network":
    render_co_mention_network()
elif page == "Scheduled Monitoring":
    render_scheduled_monitoring()
elif page == "History":
    render_history()