import streamlit as st
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import plotly.express as px
import plotly.graph_objects as go
from sklearn.ensemble import IsolationForest
from sklearn.linear_model import LinearRegression
import random
import calendar

# Set random seed for reproducibility
np.random.seed(42)

def generate_device_data(num_days=90, device_type="home"):
    """Generate synthetic energy consumption data for devices with enhanced patterns"""
    dates = pd.date_range(end=datetime.now(), periods=num_days*24, freq='h')
    
    if device_type == "home":
        devices = {
            'HVAC': {'base': 8, 'var': 4, 'peak_hours': [14, 15, 16, 17], 'weekend_factor': 1.2},
            'Refrigerator': {'base': 2, 'var': 0.5, 'peak_hours': [12, 13, 14], 'weekend_factor': 1.0},
            'Washing Machine': {'base': 1, 'var': 0.8, 'peak_hours': [10, 19, 20], 'weekend_factor': 1.5},
            'Lighting': {'base': 1.5, 'var': 0.3, 'peak_hours': [18, 19, 20, 21], 'weekend_factor': 1.1},
            'Television': {'base': 0.5, 'var': 0.2, 'peak_hours': [20, 21, 22], 'weekend_factor': 1.3}
        }
    else:
        devices = {
            'HVAC System': {'base': 20, 'var': 8, 'peak_hours': [14, 15, 16, 17], 'weekend_factor': 0.6},
            'Server Room': {'base': 15, 'var': 3, 'peak_hours': [12, 13, 14], 'weekend_factor': 0.9},
            'Office Equipment': {'base': 10, 'var': 4, 'peak_hours': [9, 10, 11, 14, 15], 'weekend_factor': 0.4},
            'Lighting': {'base': 8, 'var': 2, 'peak_hours': [9, 10, 11, 14, 15], 'weekend_factor': 0.5},
            'Kitchen Appliances': {'base': 5, 'var': 2, 'peak_hours': [12, 13], 'weekend_factor': 0.3}
        }

    data = []
    
    for date in dates:
        hour = date.hour
        is_weekend = date.weekday() >= 5
        
        for device, params in devices.items():
            # Add seasonal variation
            seasonal_factor = 1 + 0.3 * np.sin(2 * np.pi * date.dayofyear / 365)
            
            # Add peak hour variation
            peak_factor = 1.5 if hour in params['peak_hours'] else 1
            
            # Add weekend variation
            weekend_factor = params['weekend_factor'] if is_weekend else 1
            
            # Base consumption with random variation
            consumption = (params['base'] * seasonal_factor * peak_factor * weekend_factor + 
                         np.random.normal(0, params['var']))
            
            # Add some anomalies (3% chance)
            if np.random.random() < 0.03:
                consumption *= np.random.choice([1.5, 2.0, 0.5])
            
            data.append({
                'Date': date,
                'Device': device,
                'Consumption': max(0, consumption),
                'Hour': hour,
                'Weekday': date.strftime('%A'),
                'Weekend': is_weekend
            })
    
    return pd.DataFrame(data)

def detect_anomalies(df):
    """Enhanced anomaly detection using Isolation Forest with multiple features"""
    iso_forest = IsolationForest(contamination=0.03, random_state=42)
    by_device = df.groupby('Device')
    
    anomalies = []
    for device, group in by_device:
        # Use multiple features for anomaly detection
        features = group[['Consumption', 'Hour']].copy()
        features['Weekend'] = group['Weekend'].astype(int)
        
        predictions = iso_forest.fit_predict(features)
        anomaly_indices = predictions == -1
        
        anomaly_data = group[anomaly_indices]
        
        for _, row in anomaly_data.iterrows():
            anomalies.append({
                'Device': device,
                'Date': row['Date'],
                'Consumption': row['Consumption'],
                'Hour': row['Hour'],
                'Weekday': row['Weekday']
            })
    
    return pd.DataFrame(anomalies)

def generate_insights(df):
    """Generate detailed insights from the energy consumption data"""
    insights = []
    
    # Peak usage analysis
    peak_hours = df.groupby(['Device', 'Hour'])['Consumption'].mean().reset_index()
    for device in df['Device'].unique():
        device_peaks = peak_hours[peak_hours['Device'] == device].nlargest(3, 'Consumption')
        insights.append({
            'Type': 'Peak Hours',
            'Device': device,
            'Description': f"Peak usage hours: {', '.join(map(str, device_peaks['Hour']))}",
            'Impact': 'High'
        })
    
    # Weekend vs Weekday analysis
    weekend_comparison = df.groupby(['Device', 'Weekend'])['Consumption'].mean().unstack()
    for device in weekend_comparison.index:
        diff_pct = ((weekend_comparison.loc[device, True] - weekend_comparison.loc[device, False]) / 
                    weekend_comparison.loc[device, False] * 100)
        insights.append({
            'Type': 'Weekend Pattern',
            'Device': device,
            'Description': f"{'Higher' if diff_pct > 0 else 'Lower'} weekend usage by {abs(diff_pct):.1f}%",
            'Impact': 'Medium' if abs(diff_pct) < 20 else 'High'
        })
    
    return pd.DataFrame(insights)

def predict_consumption(df, days_ahead=30):
    """Predict future consumption using linear regression with multiple features"""
    predictions = []
    
    for device in df['Device'].unique():
        device_data = df[df['Device'] == device].copy()
        
        # Create features for prediction
        device_data['Day_of_Week'] = device_data['Date'].dt.dayofweek
        device_data['Month'] = device_data['Date'].dt.month
        device_data['Day_of_Year'] = device_data['Date'].dt.dayofyear
        
        X = device_data[['Hour', 'Day_of_Week', 'Month', 'Day_of_Year']]
        y = device_data['Consumption']
        
        model = LinearRegression()
        model.fit(X, y)
        
        # Generate future dates
        future_dates = pd.date_range(
            start=df['Date'].max() + timedelta(hours=1),
            periods=days_ahead*24,
            freq='h'
        )
        
        future_X = pd.DataFrame({
            'Hour': future_dates.hour,
            'Day_of_Week': future_dates.dayofweek,
            'Month': future_dates.month,
            'Day_of_Year': future_dates.dayofyear
        })
        
        future_predictions = model.predict(future_X)
        
        for date, pred in zip(future_dates, future_predictions):
            predictions.append({
                'Date': date,
                'Device': device,
                'Predicted_Consumption': max(0, pred)
            })
    
    return pd.DataFrame(predictions)

# Streamlit UI
st.set_page_config(page_title="SEMS - Smart Energy Management System", layout="wide", initial_sidebar_state="expanded")

# Custom CSS
st.markdown("""
    <style>
    .main {
        padding: 2rem;
    }
    .stMetric {
        background-color: #f0f2f6;
        padding: 1rem;
        border-radius: 0.5rem;
    }
    .insight-card {
        background-color: #ffffff;
        padding: 1rem;
        border-radius: 0.5rem;
        margin: 0.5rem 0;
        border: 1px solid #e0e0e0;
    }
    </style>
    """, unsafe_allow_html=True)

st.title("🏢 SEMS - Smart Energy Management System")

# Sidebar configuration
st.sidebar.title("Configuration")
user_type = st.sidebar.radio("Select User Type", ["Home", "Organization"])
analysis_period = st.sidebar.slider("Analysis Period (Days)", 30, 180, 90)

# Generate data
data = generate_device_data(num_days=analysis_period, device_type=user_type.lower())

# Main tabs
tab1, tab2, tab3, tab4 = st.tabs([
    "📊 Usage Dashboard",
    "🔍 Detailed Analysis",
    "⚠️ Peak Usage Detection",
    "📈 Forecasting"
])

with tab1:
    st.header("Energy Usage Dashboard")
    
    # Key metrics
    col1, col2, col3 = st.columns(3)
    
    total_consumption = data['Consumption'].sum()
    avg_daily = data.groupby(data['Date'].dt.date)['Consumption'].sum().mean()
    peak_hour = data.groupby('Hour')['Consumption'].mean().idxmax()
    
    col1.metric("Total Consumption", f"{total_consumption:.1f} kWh")
    col2.metric("Average Daily Usage", f"{avg_daily:.1f} kWh")
    col3.metric("Peak Usage Hour", f"{peak_hour}:00")
    
    # Daily consumption trend
    st.subheader("Daily Consumption Trend")
    daily_consumption = data.groupby(['Date', 'Device'])['Consumption'].sum().reset_index()
    fig = px.line(daily_consumption, x='Date', y='Consumption', color='Device',
                  title='Energy Consumption Over Time')
    fig.update_layout(height=400)
    st.plotly_chart(fig, use_container_width=True)
    
    # Device-wise distribution
    col1, col2 = st.columns(2)
    
    with col1:
        device_total = data.groupby('Device')['Consumption'].sum().sort_values(ascending=True)
        fig = px.bar(device_total, orientation='h',
                    title='Total Consumption by Device')
        st.plotly_chart(fig, use_container_width=True)
    
    with col2:
        hourly_avg = data.groupby(['Hour', 'Device'])['Consumption'].mean().reset_index()
        fig = px.line(hourly_avg, x='Hour', y='Consumption', color='Device',
                     title='Average Hourly Consumption Pattern')
        st.plotly_chart(fig, use_container_width=True)

with tab2:
    st.header("Detailed Analysis")
    
    # Weekday vs Weekend analysis
    st.subheader("Weekday vs Weekend Consumption")
    weekly_pattern = data.groupby(['Weekday', 'Device'])['Consumption'].mean().reset_index()
    fig = px.bar(weekly_pattern, x='Weekday', y='Consumption', color='Device',
                 title='Average Consumption by Day of Week')
    st.plotly_chart(fig, use_container_width=True)
    
    # Hourly heatmap
    st.subheader("Hourly Consumption Heatmap")
    hourly_data = data.pivot_table(
        values='Consumption',
        index='Hour',
        columns='Weekday',
        aggfunc='mean'
    )
    
    fig = px.imshow(hourly_data,
                    labels=dict(x="Day of Week", y="Hour of Day", color="Consumption"),
                    aspect="auto",
                    title="Consumption Intensity by Hour and Day")
    st.plotly_chart(fig, use_container_width=True)
    
    # Display insights
    st.subheader("Key Insights")
    insights = generate_insights(data)
    
    for _, insight in insights.iterrows():
        with st.expander(f"{insight['Device']} - {insight['Type']} (Impact: {insight['Impact']})"):
            st.write(insight['Description'])

with tab3:
    st.header("Peak Usage Detection")
    
    # Detect anomalies
    anomalies = detect_anomalies(data)
    
    if not anomalies.empty:
        st.warning(f"Detected {len(anomalies)} anomalies in energy consumption")
        
        # Plot with anomalies
        fig = go.Figure()
        
        for device in data['Device'].unique():
            device_data = data[data['Device'] == device]
            device_anomalies = anomalies[anomalies['Device'] == device]
            
            fig.add_trace(go.Scatter(
                x=device_data['Date'],
                y=device_data['Consumption'],
                name=f"{device} (normal)",
                mode='lines'
            ))
            
            if not device_anomalies.empty:
                fig.add_trace(go.Scatter(
                    x=device_anomalies['Date'],
                    y=device_anomalies['Consumption'],
                    name=f"{device} (anomaly)",
                    mode='markers',
                    marker=dict(size=10, symbol='x', color='red')
                ))
        
        fig.update_layout(
            title='Energy Consumption with Detected Anomalies',
            height=500
        )
        st.plotly_chart(fig, use_container_width=True)
        
        # Anomaly details in an expandable table
        st.subheader("Peak Usage Details")
        for device in anomalies['Device'].unique():
            device_anomalies = anomalies[anomalies['Device'] == device].copy()
            device_anomalies['Date'] = device_anomalies['Date'].dt.strftime('%Y-%m-%d %H:%M')
            
            with st.expander(f"Anomalies for {device}"):
                st.dataframe(
                    device_anomalies[['Date', 'Consumption', 'Hour', 'Weekday']],
                    use_container_width=True
                )

with tab4:
    st.header("Consumption Forecasting")
    
    # Generate predictions
    predictions = predict_consumption(data)
    
    # Plot historical data and predictions
    st.subheader("Consumption Forecast")
    
    for device in predictions['Device'].unique():
        with st.expander(f"Forecast for {device}"):
            historical = data[data['Device'] == device]
            device_predictions = predictions[predictions['Device'] == device]
            
            fig = go.Figure()
            
            # Historical data
            fig.add_trace(go.Scatter(
                x=historical['Date'],
                y=historical['Consumption'],
                name='Historical',
                line=dict(color='blue')
            ))
            
            # Predictions
            fig.add_trace(go.Scatter(
                x=device_predictions['Date'],
                y=device_predictions['Predicted_Consumption'],
                name='Forecast',
                line=dict(color='red', dash='dash')
            ))
            
            fig.update_layout(
                title=f'Energy Consumption Forecast - {device}',
                xaxis_title='Date',
                yaxis_title='Consumption (kWh)',
                height=400
            )
            
            st.plotly_chart(fig, use_container_width=True)
            
            # Summary statistics
            col1, col2, col3 = st.columns(3)
            
            avg_historical = historical['Consumption'].mean()
            avg_predicted = device_predictions['Predicted_Consumption'].mean()
            change_pct = (avg_predicted - avg_historical) / avg_historical * 100
            
            col1.metric(
                "Average Historical Usage",
                f"{avg_historical:.2f} kWh"
            )
            col2.metric(
                "Average Predicted Usage",
                f"{avg_predicted:.2f} kWh"
            )
            col3.metric(
                "Expected Change",
                f"{change_pct:+.1f}%",
                delta_color="inverse"
            )

    # Additional insights section
    st.subheader("Energy Saving Opportunities")
    
    # Calculate potential savings based on patterns
    def calculate_savings_opportunities(historical_data, predictions_data):
        opportunities = []
        
        # Check for peak hour reduction potential
        peak_hours = historical_data.groupby('Hour')['Consumption'].mean()
        top_peak_hours = peak_hours.nlargest(3)
        potential_peak_savings = top_peak_hours.sum() * 0.2  # Assume 20% reduction possible
        
        opportunities.append({
            'Type': 'Peak Hour Reduction',
            'Description': f'Reduce usage during peak hours ({", ".join(map(str, top_peak_hours.index))}:00)',
            'Potential_Savings': f'{potential_peak_savings:.2f} kWh per day'
        })
        
        # Check for weekend optimization
        weekend_data = historical_data[historical_data['Weekend']]
        weekday_data = historical_data[~historical_data['Weekend']]
        if weekend_data['Consumption'].mean() > weekday_data['Consumption'].mean():
            weekend_savings = (weekend_data['Consumption'].mean() - weekday_data['Consumption'].mean()) * 2
            opportunities.append({
                'Type': 'Weekend Optimization',
                'Description': 'Optimize weekend consumption patterns',
                'Potential_Savings': f'{weekend_savings:.2f} kWh per weekend'
            })
        
        # Seasonal optimization
        seasonal_data = historical_data.copy()
        seasonal_data['Month'] = seasonal_data['Date'].dt.month
        monthly_avg = seasonal_data.groupby('Month')['Consumption'].mean()
        seasonal_variation = monthly_avg.max() - monthly_avg.min()
        
        if seasonal_variation > monthly_avg.mean() * 0.3:  # If variation is more than 30%
            opportunities.append({
                'Type': 'Seasonal Optimization',
                'Description': 'Implement seasonal usage strategies',
                'Potential_Savings': f'{seasonal_variation:.2f} kWh per month'
            })
        
        return pd.DataFrame(opportunities)

    savings_opportunities = calculate_savings_opportunities(data, predictions)
    
    for _, opportunity in savings_opportunities.iterrows():
        with st.expander(f"💡 {opportunity['Type']}"):
            st.write(f"**Description:** {opportunity['Description']}")
            st.write(f"**Potential Savings:** {opportunity['Potential_Savings']}")
            
            # Add specific recommendations based on opportunity type
            if opportunity['Type'] == 'Peak Hour Reduction':
                st.write("""
                **Recommendations:**
                - Schedule high-energy activities during off-peak hours
                - Use automated controls to limit non-essential usage during peak times
                - Consider energy storage solutions for peak shifting
                """)
            elif opportunity['Type'] == 'Weekend Optimization':
                st.write("""
                **Recommendations:**
                - Review weekend device scheduling
                - Implement automatic shutdown for unused equipment
                - Optimize temperature settings for unoccupied periods
                """)
            elif opportunity['Type'] == 'Seasonal Optimization':
                st.write("""
                **Recommendations:**
                - Adjust HVAC settings seasonally
                - Implement weather-based control strategies
                - Schedule maintenance during shoulder seasons
                """)

# Add export functionality
if st.sidebar.button("Export Analysis Report"):
    # Create report dataframe
    report_data = {
        'Metric': [
            'Total Consumption',
            'Average Daily Usage',
            'Peak Usage Hour',
            'Number of Anomalies',
            'Forecast Trend'
        ],
        'Value': [
            f"{total_consumption:.1f} kWh",
            f"{avg_daily:.1f} kWh",
            f"{peak_hour}:00",
            len(anomalies),
            f"{change_pct:+.1f}% (30-day forecast)"
        ]
    }
    report_df = pd.DataFrame(report_data)
    
    # Convert to CSV
    csv = report_df.to_csv(index=False)
    st.sidebar.download_button(
        label="Download Report",
        data=csv,
        file_name="energy_analysis_report.csv",
        mime="text/csv"
    )

# Add help section in sidebar
with st.sidebar.expander("ℹ️ Help"):
    st.write("""
    **Using the Dashboard:**
    1. Select your user type (Home/Organization)
    2. Adjust the analysis period using the slider
    3. Navigate through tabs to view different analyses
    4. Use expanders to see detailed information
    5. Export your analysis report using the button above
    
    For additional support, contact our team at support@sems.com
    """)

# Add system status
st.sidebar.markdown("---")
st.sidebar.markdown("### System Status")
st.sidebar.markdown("✅ All Systems Operational")
st.sidebar.markdown(f"Last Updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")