app.py

import pandas as pd
import gradio as gr
import plotly.express as px
import warnings
import os

warnings.filterwarnings('ignore')

# --- Data Loading ---
def load_data():
    # Path to the CSV file (relative to this script)
    csv_path = os.path.join(os.path.dirname(__file__), 'xbrl_data_cleaned.csv')

    print(f"Loading data from: {csv_path}")

    # Define target metrics with both Current Year (DCY) and Previous Year (DPY)
    target_keywords = {
        'Revenue': 'Revenue From Operations_DCYMain',
        'Revenue_Prev': 'Revenue From Operations_DPYMain',
        'Net Worth': 'Net Worth_DCYMain',
        'Net Worth_Prev': 'Net Worth_DPYMain',
        'Investments': 'Amount Of Total Investments_DCYMain',
        'Investments_Prev': 'Amount Of Total Investments_DPYMain',
        'Purchases': 'Amount Of Total Purchases_DCYMain',
        'Purchases_Prev': 'Amount Of Total Purchases_DPYMain'
    }

    selected_cols = ['Company', 'Symbol', 'Industry'] 
    rename_map = {}

    try:
        # Read header to check columns
        df_header = pd.read_csv(csv_path, nrows=0)
        all_cols = df_header.columns.tolist()
        
        for key, col_name in target_keywords.items():
            if col_name in all_cols:
                selected_cols.append(col_name)
                rename_map[col_name] = key
        
        # Load dataset with selected columns
        df = pd.read_csv(csv_path, usecols=lambda x: x in selected_cols)
        df.rename(columns=rename_map, inplace=True)
        
        # Clean numeric columns
        numeric_cols = list(rename_map.values())
        for col in numeric_cols:
            if col in df.columns:
                df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)
                
        print("Data Loaded Successfully!")
        return df
        
    except Exception as e:
        print(f"Error loading data: {e}")
        return pd.DataFrame()

df = load_data()
companies = df['Company'].unique().tolist() if not df.empty else []

# --- Dashboard Functions ---

def get_overall_stats():
    if df.empty:
        return {}
    
    stats = {
        "Total Companies": len(df['Company'].unique()),
        "Total Revenue": df['Revenue'].sum() if 'Revenue' in df.columns else 0,
        "Avg Net Worth": df['Net Worth'].mean() if 'Net Worth' in df.columns else 0,
        "Top Industry": df['Industry'].mode()[0] if 'Industry' in df.columns else "N/A"
    }
    return stats

def plot_industry_dist():
    if df.empty or 'Industry' not in df.columns:
        return None
    
    industry_counts = df['Industry'].value_counts().head(10)
    fig = px.pie(values=industry_counts.values, names=industry_counts.index, title='Top 10 Industries')
    return fig

def plot_revenue_vs_networth():
    if df.empty or 'Revenue' not in df.columns or 'Net Worth' not in df.columns:
        return None
    
    fig = px.scatter(df, x='Revenue', y='Net Worth', hover_name='Company', title='Revenue vs Net Worth')
    return fig

def get_company_details(company_name):
    if df.empty:
        return "No data loaded", None, None
        
    comp_data = df[df['Company'] == company_name]
    if comp_data.empty:
        return "Company not found", None, None
    
    # Metrics
    metrics = comp_data.iloc[0].to_dict()
    
    # Visualization 1: Current Year Metrics
    cy_metrics = {k: v for k, v in metrics.items() if k in ['Revenue', 'Net Worth', 'Investments', 'Purchases'] and isinstance(v, (int, float))}
    fig_cy = px.bar(x=list(cy_metrics.keys()), y=list(cy_metrics.values()), title=f"Current Year Financials for {company_name}")
    
    # Visualization 2: Year over Year Comparison
    categories = ['Revenue', 'Net Worth', 'Investments', 'Purchases']
    yoy_data = []
    
    for cat in categories:
        if cat in metrics:
            yoy_data.append({'Metric': cat, 'Year': 'Current', 'Value': metrics[cat]})
        if f"{cat}_Prev" in metrics:
            yoy_data.append({'Metric': cat, 'Year': 'Previous', 'Value': metrics[f"{cat}_Prev"]})
            
    if yoy_data:
        df_yoy = pd.DataFrame(yoy_data)
        fig_yoy = px.bar(df_yoy, x='Metric', y='Value', color='Year', barmode='group', title=f"Year-over-Year Comparison for {company_name}")
    else:
        fig_yoy = None
    
    # Format metrics text
    metrics_str = "\n".join([f"{k}: {v}" for k, v in metrics.items() if isinstance(v, (int, float, str))])
    
    return metrics_str, fig_cy, fig_yoy

# --- Gradio Interface ---

with gr.Blocks(title="XBRL Financial Dashboard") as demo:
    gr.Markdown("# 📊 XBRL Data Analysis Dashboard")
    gr.Markdown("Explore financial data, industry trends, and company performance.")
    
    with gr.Tabs():
        # Tab 1: Overall Dashboard
        with gr.TabItem("📈 Market Overview"):
            gr.Markdown("### Key Performance Indicators")
            stats = get_overall_stats()
            with gr.Row():
                gr.Number(label="Total Companies", value=stats.get('Total Companies', 0))
                gr.Number(label="Total Revenue", value=stats.get('Total Revenue', 0))
                gr.Number(label="Average Net Worth", value=stats.get('Avg Net Worth', 0))
                gr.Textbox(label="Top Industry", value=stats.get('Top Industry', "N/A"))
            
            gr.Markdown("### Market Visualizations")
            with gr.Row():
                with gr.Column():
                    plot_ind = plot_industry_dist()
                    if plot_ind:
                        gr.Plot(plot_ind)
                    else:
                        gr.Markdown("No Industry Data Available")
                with gr.Column():
                    plot_rev_nw = plot_revenue_vs_networth()
                    if plot_rev_nw:
                        gr.Plot(plot_rev_nw)
                    else:
                        gr.Markdown("Insufficient Data for Scatter Plot")
                
        # Tab 2: Company Specific Analysis
        with gr.TabItem("🏢 Company Analysis"):
            gr.Markdown("### Deep Dive into Company Financials")
            with gr.Row():
                company_dropdown = gr.Dropdown(choices=companies, label="Select Company", interactive=True)
            
            with gr.Row():
                with gr.Column(scale=1):
                    company_info = gr.Textbox(label="Company Details", lines=15)
                
                with gr.Column(scale=2):
                    company_plot_cy = gr.Plot(label="Current Year Metrics")
                    company_plot_yoy = gr.Plot(label="Year-over-Year Comparison")
            
            company_dropdown.change(
                fn=get_company_details,
                inputs=company_dropdown,
                outputs=[company_info, company_plot_cy, company_plot_yoy]
            )

if __name__ == "__main__":
    print("Launching Gradio App...")
    demo.launch(server_name="0.0.0.0", share=True)