import dash
from dash import dcc, html, Input, Output
import pandas as pd
import plotly.express as px

# Load the data
def load_data():
    file_path = 'digital_identity_data.xlsx'
    return pd.read_excel(file_path)

data = load_data()

# Initialize Dash app
app = dash.Dash(__name__)
app.title = "Digital Identity Dashboard"

# Layout
def generate_layout():
    return html.Div([
        html.H1("Digital Identity Dashboard", style={"textAlign": "center"}),

        html.Div([
            html.Label("Select Countries:"),
            dcc.Checklist(
                id="country-filter",
                options=[{"label": country, "value": country} for country in data["Country"].unique()],
                value=data["Country"].unique().tolist(),
                inline=True
            ),

            html.Label("Select Genders:"),
            dcc.Checklist(
                id="gender-filter",
                options=[{"label": gender, "value": gender} for gender in data["Gender"].unique()],
                value=data["Gender"].unique().tolist(),
                inline=True
            ),

            html.Label("Select Account Status:"),
            dcc.Checklist(
                id="status-filter",
                options=[{"label": status, "value": status} for status in data["Account Status"].unique()],
                value=data["Account Status"].unique().tolist(),
                inline=True
            ),
        ], style={"marginBottom": "20px"}),

        html.Div(id="filtered-data-table"),

        html.Div([
            dcc.Graph(id="logins-by-country"),
            dcc.Graph(id="session-duration-by-gender")
        ], style={"display": "flex", "flexWrap": "wrap"}),

        html.Div([
            dcc.Graph(id="data-breaches-by-country"),
            dcc.Graph(id="two-fa-usage")
        ], style={"display": "flex", "flexWrap": "wrap"})
    ])

app.layout = generate_layout

# Callbacks for filtering data and updating graphs
@app.callback(
    [Output("logins-by-country", "figure"),
     Output("session-duration-by-gender", "figure"),
     Output("data-breaches-by-country", "figure"),
     Output("two-fa-usage", "figure"),
     Output("filtered-data-table", "children")],
    [Input("country-filter", "value"),
     Input("gender-filter", "value"),
     Input("status-filter", "value")]
)
def update_dashboard(selected_countries, selected_genders, selected_statuses):
    # Filter data
    filtered_data = data[
        (data["Country"].isin(selected_countries)) &
        (data["Gender"].isin(selected_genders)) &
        (data["Account Status"].isin(selected_statuses))
    ]

    # Logins by country
    logins_by_country = filtered_data.groupby("Country")["Number of Logins"].sum().reset_index()
    fig1 = px.bar(logins_by_country, x="Country", y="Number of Logins", title="Logins by Country", color="Country")

    # Session duration by gender
    session_duration_by_gender = filtered_data.groupby("Gender")["Session Duration (Minutes)"].mean().reset_index()
    fig2 = px.bar(session_duration_by_gender, x="Gender", y="Session Duration (Minutes)", title="Session Duration by Gender", color="Gender")

    # Data breaches by country
    fig3 = px.pie(filtered_data, names="Country", values="Data Breaches Reported", title="Data Breaches by Country")

    # 2FA usage
    two_fa_usage = filtered_data["2FA Enabled"].value_counts().reset_index()
    two_fa_usage.columns = ["2FA Enabled", "Count"]
    fig4 = px.pie(two_fa_usage, names="2FA Enabled", values="Count", title="2FA Usage")

    # Filtered data table
    table_html = html.Div([
        html.H3("Filtered Data Table"),
        dash.dash_table.DataTable(
            data=filtered_data.to_dict('records'),
            columns=[{"name": i, "id": i} for i in filtered_data.columns],
            page_size=10,
            style_table={"overflowX": "auto"}
        )
    ])

    return fig1, fig2, fig3, fig4, table_html

# Run app
if __name__ == "__main__":
    app.run_server(debug=True)