pages/dashboard.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
import numpy as np

# Import utilities
from utils.storage import load_data, save_data
from utils.error_handling import show_error, show_success, show_warning
from utils.logging import get_logger

logger = get_logger(__name__)

def create_dashboard_page():
    """
    Create the main dashboard page for MONA
    """
    st.title("🤖 MONA - Monitoring & Analytics Dashboard")
    
    # Sidebar for navigation
    with st.sidebar:
        st.header("📊 Dashboard Controls")
        
        # Data source selection
        data_source = st.selectbox(
            "Select Data Source",
            ["Sample Data", "Upload File", "Real-time Feed"]
        )
        
        # Time range selection
        st.subheader("Time Range")
        time_range = st.selectbox(
            "Select Time Range",
            ["Last 24 Hours", "Last 7 Days", "Last 30 Days", "Custom"]
        )
        
        if time_range == "Custom":
            start_date = st.date_input("Start Date", datetime.now() - timedelta(days=7))
            end_date = st.date_input("End Date", datetime.now())
        
        # Refresh button
        if st.button("🔄 Refresh Data"):
            st.rerun()
    
    # Main dashboard content
    col1, col2, col3, col4 = st.columns(4)
    
    # Key metrics
    with col1:
        st.metric(
            label="System Status",
            value="Online",
            delta="✅"
        )
    
    with col2:
        st.metric(
            label="Active Connections",
            value="127",
            delta="5",
            delta_color="normal"
        )
    
    with col3:
        st.metric(
            label="Data Points",
            value="1,234",
            delta="156",
            delta_color="normal"
        )
    
    with col4:
        st.metric(
            label="Success Rate",
            value="99.8%",
            delta="0.2%",
            delta_color="normal"
        )
    
    # Charts section
    st.header("📈 Analytics")
    
    # Generate sample data for demonstration
    sample_data = generate_sample_data()
    
    # Time series chart
    col1, col2 = st.columns(2)
    
    with col1:
        st.subheader("Time Series Analysis")
        fig_line = px.line(
            sample_data,
            x='timestamp',
            y='value',
            title='Data Trends Over Time',
            color_discrete_sequence=['#1f77b4']
        )
        fig_line.update_layout(
            xaxis_title="Time",
            yaxis_title="Value",
            showlegend=False
        )
        st.plotly_chart(fig_line, use_container_width=True)
    
    with col2:
        st.subheader("Distribution Analysis")
        fig_hist = px.histogram(
            sample_data,
            x='value',
            bins=20,
            title='Value Distribution',
            color_discrete_sequence=['#ff7f0e']
        )
        fig_hist.update_layout(
            xaxis_title="Value",
            yaxis_title="Frequency",
            showlegend=False
        )
        st.plotly_chart(fig_hist, use_container_width=True)
    
    # Data table
    st.header("📋 Data Overview")
    
    # Data filtering options
    col1, col2, col3 = st.columns(3)
    
    with col1:
        show_raw_data = st.checkbox("Show Raw Data", value=False)
    
    with col2:
        max_rows = st.number_input("Max Rows", min_value=10, max_value=1000, value=100)
    
    with col3:
        if st.button("Export Data"):
            try:
                success = save_data(sample_data.to_dict('records'), 'dashboard_export.json')
                if success:
                    show_success("Data exported successfully!")
                else:
                    show_error("Failed to export data")
            except Exception as e:
                show_error(f"Export error: {e}")
    
    if show_raw_data:
        st.dataframe(
            sample_data.head(max_rows),
            use_container_width=True,
            hide_index=True
        )
    
    # System information
    with st.expander("🔧 System Information"):
        system_info = {
            "Application": "MONA Dashboard",
            "Version": "1.0.0",
            "Last Updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "Data Points": len(sample_data),
            "Status": "Operational"
        }
        
        for key, value in system_info.items():
            st.text(f"{key}: {value}")
    
    # Footer
    st.markdown("---")
    st.markdown(
        "**MONA Dashboard** - Monitoring & Analytics | "
        f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
    )

def generate_sample_data():
    """
    Generate sample data for demonstration
    """
    try:
        # Create time series data
        dates = pd.date_range(
            start=datetime.now() - timedelta(days=30),
            end=datetime.now(),
            freq='H'
        )
        
        # Generate synthetic data with trend and noise
        np.random.seed(42)  # For reproducible results
        trend = np.linspace(100, 200, len(dates))
        noise = np.random.normal(0, 10, len(dates))
        seasonal = 20 * np.sin(2 * np.pi * np.arange(len(dates)) / 24)
        
        values = trend + seasonal + noise
        
        df = pd.DataFrame({
            'timestamp': dates,
            'value': values,
            'category': np.random.choice(['A', 'B', 'C'], len(dates)),
            'status': np.random.choice(['Active', 'Inactive'], len(dates), p=[0.8, 0.2])
        })
        
        return df
        
    except Exception as e:
        logger.error(f"Error generating sample data: {e}")
        # Return minimal fallback data
        return pd.DataFrame({
            'timestamp': [datetime.now()],
            'value': [100],
            'category': ['A'],
            'status': ['Active']
        })

def load_dashboard_config():
    """
    Load dashboard configuration
    """
    try:
        config = load_data('dashboard_config.json')
        if config is None:
            # Create default config
            default_config = {
                'theme': 'light',
                'refresh_interval': 30,
                'max_data_points': 1000,
                'charts_enabled': True
            }
            save_data(default_config, 'dashboard_config.json')
            return default_config
        return config
    except Exception as e:
        logger.error(f"Error loading dashboard config: {e}")
        return {}

# Initialize when module is imported
if __name__ == "__main__":
    create_dashboard_page()