import pandas as pd
import plotly.express as px
import numpy as np
from IPython.display import display, HTML

def hide_code_toggle():
    display(HTML('''
        <style>
            .code-toggle {
                position: fixed;
                top: 20px;
                left: 20px;
                z-index: 1000;
                padding: 10px 17px;
                background: #1570FF;  /* Bright blue color */
                color: white;
                border: none;
                border-radius: 6px;
                cursor: pointer;
                font-size: 14px;
                font-weight: bold;
                box-shadow: 0 2px 6px rgba(0,0,0,0.2);
                transition: all 0.3s ease;
            }
            .code-toggle:hover {
                background: #1976D2;
                box-shadow: 0 4px 8px rgba(0,0,0,0.3);
                transform: translateY(-1px);
            }
            .code-toggle:active {
                transform: translateY(1px);
            }
            .code-container {
                margin: 10px 0;
                transition: all 0.3s ease;
            }
        </style>
        <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
        <script>
        $(document).ready(function() {
            // Create toggle button
            var toggleButton = $('<button class="code-toggle">▼ Toggle Code</button>');
            $('body').prepend(toggleButton);
            
            // Wrap all code blocks in containers
            $('pre').each(function() {
                $(this).wrap('<div class="code-container"></div>');
            });

            // Set initial state
            var codeVisible = true;
            
            // Toggle functionality
            toggleButton.click(function() {
                codeVisible = !codeVisible;
                $('.code-container').slideToggle(300);
                $(this).html(codeVisible ? '▼ Hide Code' : '▲ Show Code');
                $(this).toggleClass('code-hidden', !codeVisible);
            });
        });
        </script>
    '''))
hide_code_toggle()
# Load the dataset
file_path = r"C:\Users\11\Desktop\Python\Python Project\Trade_China_2000-2023\trade_data_updated.xlsx"
total = pd.read_excel(file_path)

# Convert all column names to strings to avoid type issues
total.columns = total.columns.astype(str)

# Check if '2023' column exists (now as a string)
if '2023' in total.columns:
    # Convert the '2023' column to numeric, coercing errors to NaN
    total['2023'] = pd.to_numeric(total['2023'], errors='coerce')
    print(f"\nData type of '2023' column after conversion: {total['2023'].dtype}")

    # Identify the trade type column (first column)
    trade_type_col = total.columns[0]  # Usually 'Unnamed: 0'
    print(f"\nTrade type column identified as: '{trade_type_col}'")

    # ===========================================
    # Treemap for IMPORTS
    # ===========================================
    import_data = total[total[trade_type_col] == 'Import'].copy()
    if not import_data.empty:
        import_data['Trade Value (In Millions$)'] = import_data['2023']
        fig_import = px.treemap(
            import_data,
            path=[px.Constant('World'), 'Continent', 'Country'],
            values='Trade Value (In Millions$)',
            color='Trade Value (In Millions$)',
            hover_data=['ISO-3_Code'],
            color_continuous_scale='RdBu',
            color_continuous_midpoint=np.nanpercentile(import_data['Trade Value (In Millions$)'], 85),
            template='plotly_white'
        )
        fig_import.update_layout(
            title='<b>Top Importers of Chinese Goods (2023)</b> <br><sup><i>Hover for details</i></sup>',
            font_family="verdana",
            margin=dict(t=50, l=25, r=25, b=25),
            paper_bgcolor="#e9f1f5",
            plot_bgcolor="#e9f1f5"
        )
        fig_import.show()
    else:
        print("\nNo import data found!")

    # ===========================================
    # Treemap for EXPORTS (NEW ADDITION)
    # ===========================================
    export_data = total[total[trade_type_col] == 'Export'].copy()  # Filter for Export
    if not export_data.empty:
        export_data['Trade Value (In Millions$)'] = export_data['2023']
        fig_export = px.treemap(
            export_data,
            path=[px.Constant('World'), 'Continent', 'Country'],
            values='Trade Value (In Millions$)',
            color='Trade Value (In Millions$)',
            hover_data=['ISO-3_Code'],
            color_continuous_scale='RdBu_r',
            color_continuous_midpoint=np.nanpercentile(export_data['Trade Value (In Millions$)'], 85),
            template='plotly_white'
        )
        fig_export.update_layout(
            title='<b>Top Exporters of Chinese Goods (2023)</b> <br><sup><i>Hover for details</i></sup>',
            font_family="verdana",
            margin=dict(t=50, l=25, r=25, b=25),
            paper_bgcolor="#e9f1f5",
            plot_bgcolor="#e9f1f5"
        )
        fig_export.show()
    else:
        print("\nNo export data found!")

else:
    print("\nColumn '2023' not found. Check column names after conversion.")
    

Data type of '2023' column after conversion: float64

Trade type column identified as: 'Unnamed: 0'