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

def hide_code_toggle():
    display(HTML('''
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                position: fixed;
                top: 20px;
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                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 column names to strings
total.columns = total.columns.astype(str)

# ===========================================
# USER INPUTS (Customize these)
# ===========================================
TARGET_COUNTRY = "China"  # Country to analyze
TRADE_TYPE = "Export"  # "Import" or "Export"
TARGET_YEAR = "2023"  # Year to highlight
YEARS_TO_DISPLAY = [str(year) for year in range(2014, 2024)]  # 2014-2023
TOP_N = 10  # Number of top partners to show


# ===========================================

def plot_top_trading_partners(data, trade_type, years, top_n, target_year):
    """
    Generate a bar chart of top trading partners with yearly breakdown.
    """
    # Filter data for the specified trade type (Import/Export)
    trade_data = data[data["Unnamed: 0"] == trade_type].copy()

    # Validate years exist in dataset
    missing_years = [year for year in years if year not in trade_data.columns]
    if missing_years:
        raise ValueError(f"Missing data for years: {missing_years}")

    # Sort countries by trade value in target year to get top partners
    sorted_data = trade_data.sort_values(by=target_year, ascending=False).head(top_n)
    top_countries = sorted_data["Country"].tolist()

    # Melt data to long format for Plotly
    melted_data = pd.melt(
        sorted_data,
        id_vars=["Country"],
        value_vars=years,
        var_name="Year",
        value_name="Trade Value (US$)"
    )

    # Create the bar chart
    fig = px.bar(
        melted_data,
        x="Country",
        y="Trade Value (US$)",
        color="Year",
        color_discrete_sequence=px.colors.sequential.RdBu,
        text="Trade Value (US$)",
        title=f"Top {top_n} {TRADE_TYPE} Partners of {TARGET_COUNTRY} (2014-2023)",
        labels={"Trade Value (US$)": "Trade Value (In Millions$)"},
        hover_data=["Country", "Year"],
        category_orders={"Year": years}  # Force chronological order
    )

    # Format values and layout
    fig.update_layout(
        yaxis=dict(tickprefix="$", tickformat=",.0f"),
        xaxis=dict(title="Counties", categoryorder='total descending'),
        hoverlabel=dict(bgcolor="white", font_size=12),
        template="plotly_white",
        legend=dict(title="Year", orientation="h", yanchor="bottom", y=1.02),
        font=dict(family="verdana"),
        margin=dict(b=100)  # Prevent bottom cutoff for country names
    )

    # Format text and bars
    fig.update_traces(
        texttemplate="%{text:$,.0f}",
        textposition="outside",
        marker_line_color="black",
        marker_line_width=0.5
    )

    return fig


# Generate the plot
fig = plot_top_trading_partners(
    data=total,
    trade_type=TRADE_TYPE,
    years=YEARS_TO_DISPLAY,
    top_n=TOP_N,
    target_year=TARGET_YEAR
)

# Show the plot
fig.show()

# ===========================================
# GENERATE IMPORT PLOT (Added code)
# ===========================================

# Override the TRADE_TYPE to "Import"
TRADE_TYPE = "Import"

# Generate the import plot using the same function
fig_import = plot_top_trading_partners(
    data=total,
    trade_type=TRADE_TYPE,
    years=YEARS_TO_DISPLAY,
    top_n=TOP_N,
    target_year=TARGET_YEAR
)

# Show the import plot
fig_import.show()