# app.py
import gradio as gr
import pandas as pd
import joblib

# --- Step 1: Load the pre-calculated analysis data ---
print("Loading network analysis data...")
try:
    df_wallets = joblib.load('wallet_influence.joblib')
    df_domains = joblib.load('domain_centrality.joblib')
    
    # Format the scores for better readability
    df_wallets['Influence Score'] = (df_wallets['Influence Score'] * 10000).round(4) # Scale score to be more readable
    df_domains['Centrality Score'] = (df_domains['Centrality Score'] * 10000).round(4)

    print("✅ Network analysis data loaded successfully.")
except FileNotFoundError:
    print("❌ ERROR: Data files not found. Make sure 'wallet_influence.joblib' and 'domain_centrality.joblib' are uploaded.")
    df_wallets = pd.DataFrame({'Error': ['wallet_influence.joblib not found.']})
    df_domains = pd.DataFrame({'Error': ['domain_centrality.joblib not found.']})


# --- Step 2: Define the functions to display the data ---
# These functions simply return the top N results from the loaded dataframes.
def get_top_wallets(top_n=20):
    return df_wallets.head(int(top_n))

def get_top_domains(top_n=20):
    return df_domains.head(int(top_n))

# --- Step 3: Create and launch the Gradio Tabbed Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Doma Wallet & Domain Influence Analyzer (Model 5)")
    gr.Markdown("This tool uses the PageRank algorithm on the Doma ownership graph to identify the most influential wallets and centrally important domains in the ecosystem.")
    
    with gr.Tabs():
        with gr.TabItem("🏆 Top Influential Wallets"):
            wallet_slider = gr.Slider(10, 100, value=20, step=10, label="Number of Wallets to Display")
            wallet_output = gr.DataFrame(headers=['Wallet', 'Influence Score'], datatype=['str', 'number'])
            wallet_slider.change(get_top_wallets, inputs=wallet_slider, outputs=wallet_output)
            # Load initial data
            demo.load(get_top_wallets, inputs=wallet_slider, outputs=wallet_output)

        with gr.TabItem("💎 Top Central Domains"):
            domain_slider = gr.Slider(10, 100, value=20, step=10, label="Number of Domains to Display")
            domain_output = gr.DataFrame(headers=['Domain', 'Centrality Score'], datatype=['str', 'number'])
            domain_slider.change(get_top_domains, inputs=domain_slider, outputs=domain_output)
            # Load initial data
            demo.load(get_top_domains, inputs=domain_slider, outputs=domain_output)

# Launch the app
if __name__ == "__main__":
    demo.launch()