import gradio as gr
import pandas as pd
from datasets import load_dataset
import plotly.express as px
import plotly.graph_objects as go

# Load the dataset
@gr.cache
def load_data():
    dataset = load_dataset("irf23/canadian-parliamentary-expenditures")
    df = pd.DataFrame(dataset['train'])
    df['amount'] = pd.to_numeric(df['amount'])
    df['period_year'] = pd.to_numeric(df['period_year'])
    df['period_quarter'] = pd.to_numeric(df['period_quarter'])
    return df

# Create visualizations
def create_overview(df):
    total_spending = df['amount'].sum()
    total_records = len(df)
    unique_members = df['member_id'].nunique()
    
    return f"""
    ## Dataset Overview
    - **Total Spending**: ${total_spending:,.2f}
    - **Total Records**: {total_records:,}
    - **Unique Members**: {unique_members}
    - **Time Period**: {df['period_year'].min()} Q{df[df['period_year']==df['period_year'].min()]['period_quarter'].min()} to {df['period_year'].max()} Q{df[df['period_year']==df['period_year'].max()]['period_quarter'].max()}
    """

def spending_by_party(df):
    party_spending = df.groupby('party')['amount'].sum().sort_values(ascending=True)
    fig = px.bar(
        x=party_spending.values,
        y=party_spending.index,
        orientation='h',
        title='Total Spending by Party',
        labels={'x': 'Total Amount ($)', 'y': 'Party'}
    )
    return fig

def spending_by_category(df):
    category_spending = df.groupby('category')['amount'].sum().sort_values(ascending=False)
    fig = px.pie(
        values=category_spending.values,
        names=category_spending.index,
        title='Spending Distribution by Category'
    )
    return fig

def spending_over_time(df):
    quarterly_spending = df.groupby(['period_year', 'period_quarter'])['amount'].sum().reset_index()
    quarterly_spending['period'] = quarterly_spending['period_year'].astype(str) + ' Q' + quarterly_spending['period_quarter'].astype(str)
    
    fig = px.line(
        quarterly_spending,
        x='period',
        y='amount',
        title='Quarterly Spending Trends',
        labels={'amount': 'Total Amount ($)', 'period': 'Period'}
    )
    fig.update_xaxis(tickangle=-45)
    return fig

def top_spenders(df, n=20):
    top_members = df.groupby('member_name')['amount'].sum().nlargest(n).sort_values(ascending=True)
    fig = px.bar(
        x=top_members.values,
        y=top_members.index,
        orientation='h',
        title=f'Top {n} Spenders',
        labels={'x': 'Total Amount ($)', 'y': 'Member'}
    )
    fig.update_layout(height=600)
    return fig

def search_expenses(df, member_name="", min_amount=0, max_amount=1000000, category="All"):
    filtered_df = df.copy()
    
    if member_name:
        filtered_df = filtered_df[filtered_df['member_name'].str.contains(member_name, case=False, na=False)]
    
    filtered_df = filtered_df[(filtered_df['amount'] >= min_amount) & (filtered_df['amount'] <= max_amount)]
    
    if category != "All":
        filtered_df = filtered_df[filtered_df['category'] == category]
    
    # Get top 100 results
    result = filtered_df.nlargest(100, 'amount')[['member_name', 'category', 'amount', 'description', 'supplier', 'date_incurred']]
    
    return result

# Create Gradio interface
def main():
    df = load_data()
    
    with gr.Blocks(title="Canadian Parliamentary Expenditures Analysis") as app:
        gr.Markdown("# 🇨🇦 Canadian Parliamentary Expenditures Analysis")
        gr.Markdown("Explore spending data from the Canadian House of Commons (2018-2025)")
        
        with gr.Tab("Overview"):
            overview_text = gr.Markdown(create_overview(df))
            
            with gr.Row():
                party_chart = gr.Plot(spending_by_party(df))
                category_chart = gr.Plot(spending_by_category(df))
            
            time_chart = gr.Plot(spending_over_time(df))
        
        with gr.Tab("Top Spenders"):
            n_spenders = gr.Slider(10, 50, value=20, step=5, label="Number of top spenders to show")
            spenders_chart = gr.Plot(top_spenders(df, 20))
            n_spenders.change(lambda n: top_spenders(df, n), inputs=[n_spenders], outputs=[spenders_chart])
        
        with gr.Tab("Search Expenses"):
            gr.Markdown("### Search and Filter Expenses")
            
            with gr.Row():
                member_search = gr.Textbox(label="Member Name (partial match)", placeholder="e.g., Trudeau")
                category_filter = gr.Dropdown(
                    choices=["All"] + df['category'].unique().tolist(),
                    value="All",
                    label="Category"
                )
            
            with gr.Row():
                min_amount = gr.Number(value=0, label="Minimum Amount ($)")
                max_amount = gr.Number(value=1000000, label="Maximum Amount ($)")
            
            search_btn = gr.Button("Search", variant="primary")
            results_df = gr.Dataframe(
                headers=["Member", "Category", "Amount", "Description", "Supplier", "Date"],
                datatype=["str", "str", "number", "str", "str", "str"],
                row_count=10
            )
            
            search_btn.click(
                search_expenses,
                inputs=[df, member_search, min_amount, max_amount, category_filter],
                outputs=[results_df]
            )
    
    return app

if __name__ == "__main__":
    app = main()
    app.launch()