app.py

import gradio as gr
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
from typing import Tuple, Optional
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Mapping company names to their ticker symbols
COMPANY_DICT = {
    "Apple": "AAPL",
    "Google": "GOOGL",
    "Microsoft": "MSFT",
    "Amazon": "AMZN",
    "Tesla": "TSLA",
    "Meta": "META",
    "NVIDIA": "NVDA",
    "Netflix": "NFLX"
}

def create_line_plot(df: pd.DataFrame, company_name: str) -> go.Figure:
    """Create a line plot using plotly"""
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=df["ESG Category"],
        y=df["Score"],
        mode='lines+markers',
        name='ESG Score',
        line=dict(color='rgb(55, 83, 109)', width=2),
        marker=dict(size=10)
    ))
    fig.update_layout(
        title=f"ESG Scores Trend for {company_name}",
        xaxis_title="ESG Category",
        yaxis_title="Score",
        yaxis_range=[0, 100],
        height=400,
        template='plotly_white'
    )
    return fig

def create_scatter_plot(df: pd.DataFrame, company_name: str) -> go.Figure:
    """Create a scatter plot using plotly"""
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=df["ESG Category"],
        y=df["Score"],
        mode='markers',
        marker=dict(
            size=15,
            color='rgb(55, 83, 109)',
            line=dict(
                color='rgb(8,48,107)',
                width=2
            )
        ),
        name='ESG Score'
    ))
    fig.update_layout(
        title=f"ESG Score Distribution for {company_name}",
        xaxis_title="ESG Category",
        yaxis_title="Score",
        yaxis_range=[0, 100],
        height=400,
        template='plotly_white'
    )
    return fig

def create_bar_plot(df: pd.DataFrame, company_name: str) -> go.Figure:
    """Create a bar plot using plotly"""
    fig = go.Figure()
    fig.add_trace(go.Bar(
        x=df["ESG Category"],
        y=df["Score"],
        marker_color='rgb(55, 83, 109)',
        name='ESG Score'
    ))
    fig.update_layout(
        title=f"ESG Scores Comparison for {company_name}",
        xaxis_title="ESG Category",
        yaxis_title="Score",
        yaxis_range=[0, 100],
        height=400,
        template='plotly_white'
    )
    return fig
def create_pie_chart(df: pd.DataFrame, company_name: str) -> go.Figure:
    """Create a pie chart using plotly"""
    total_score = df['Score'].sum()
    percentages = (df['Score'] / total_score * 100).round(1)
    
    # Create labels with both category and percentage
    labels = [f"{cat} ({pct}%)" for cat, pct in zip(df['ESG Category'], percentages)]
    
    fig = go.Figure()
    fig.add_trace(go.Pie(
        labels=labels,
        values=df['Score'],
        hole=0.4,  # Creates a donut chart
        marker=dict(
            colors=['rgb(55, 83, 109)', 'rgb(26, 118, 255)', 'rgb(178, 200, 223)']
        ),
        textinfo='label+value',
        textposition='outside',
        texttemplate='%{label}<br>Score: %{value:.1f}'
    ))
    fig.update_layout(
        title=f"ESG Score Distribution for {company_name}",
        height=400,
        template='plotly_white',
        showlegend=False
    )
    return fig


def create_empty_plot(message: str) -> go.Figure:
    """Create an empty plot with an error message"""
    fig = go.Figure()
    fig.add_annotation(
        text=message,
        xref="paper",
        yref="paper",
        x=0.5,
        y=0.5,
        showarrow=False,
        font=dict(size=14)
    )
    fig.update_layout(
        xaxis_visible=False,
        yaxis_visible=False,
        height=400
    )
    return fig

def fetch_esg_data(company_name: str) -> Tuple[Optional[pd.DataFrame], str]:
    """
    Fetch and process ESG data for the selected company.
    """
    try:
        ticker = COMPANY_DICT[company_name]
        logger.info(f"Fetching ESG data for {company_name} ({ticker})")
        
        stock = yf.Ticker(ticker)
        esg_data = stock.sustainability
        
        if esg_data is None:
            return None, f"No ESG data available for {company_name}"
            
        esg_df = pd.DataFrame(esg_data)
        esg_scores = esg_df.loc[["environmentScore", "socialScore", "governanceScore"], :].dropna().astype(float)
        
        plot_df = pd.DataFrame({
            "ESG Category": ["Environment", "Social", "Governance"],
            "Score": esg_scores.squeeze().values
        })
        
        return plot_df, f"Successfully fetched ESG data for {company_name}"
        
    except Exception as e:
        logger.error(f"Error fetching ESG data: {str(e)}")
        return None, f"Error fetching ESG data: {str(e)}"

def create_interface() -> gr.Blocks:
    """Create the Gradio interface"""
    with gr.Blocks(theme=gr.themes.Soft()) as app:
        gr.Markdown("# ESG Data Visualization Dashboard")
        gr.Markdown("Analyze Environmental, Social, and Governance scores for major tech companies.")
        
        with gr.Tab("ESG Analysis"):
            with gr.Row():
                with gr.Column():
                    company = gr.Dropdown(
                        label="Select Company",
                        choices=list(COMPANY_DICT.keys()),
                        value="Apple"
                    )
                    plot_button = gr.Button("Generate ESG Analysis", variant="primary")
                    status_message = gr.Textbox(
                        label="Status",
                        interactive=False,
                        visible=True
                    )
            
            with gr.Row():
                with gr.Column():
                    line_plot = gr.Plot(label="Trend Analysis")
                with gr.Column():
                    pie_plot = gr.Plot(label="Distribution Analysis")
            
            with gr.Row():
                with gr.Column():
                    scatter_plot = gr.Plot(label="Score Distribution")
                with gr.Column():
                    bar_plot = gr.Plot(label="Score Comparison")
            
            def process_esg_request(company_name: str) -> Tuple[go.Figure, go.Figure, go.Figure, go.Figure, str]:
                # Fetch the data
                plot_df, status = fetch_esg_data(company_name)
                
                if plot_df is None:
                    empty_plot = create_empty_plot(status)
                    return empty_plot, empty_plot, empty_plot, empty_plot, status
                
                # Create all plots
                line = create_line_plot(plot_df, company_name)
                scatter = create_scatter_plot(plot_df, company_name)
                bar = create_bar_plot(plot_df, company_name)
                pie = create_pie_chart(plot_df, company_name)
                
                return line, pie, scatter, bar, status
            
            # Connect the button click to the process function
            plot_button.click(
                fn=process_esg_request,
                inputs=company,
                outputs=[line_plot, pie_plot, scatter_plot, bar_plot, status_message],
                api_name="generate_esg_analysis"
            )
        
        with gr.Tab("About"):
            gr.Markdown("""
                ## About This Dashboard
                
                This dashboard provides ESG (Environmental, Social, and Governance) data visualization for major technology companies. The data is sourced from Yahoo Finance and updated regularly.
                
                ### How to Use
                1. Select a company from the dropdown menu
                2. Click 'Generate ESG Analysis' to view multiple visualizations:
                   - Trend Analysis: Shows the progression across ESG categories
                   - Distribution Analysis: Shows the relative proportion of each ESG component
                   - Score Distribution: Displays the spread of ESG scores
                   - Score Comparison: Compares ESG scores side by side
                
                ### Metrics Explained
                - **Environmental Score**: Measures company's environmental impact and sustainability initiatives
                - **Social Score**: Evaluates company's relationships with employees, suppliers, customers, and communities
                - **Governance Score**: Assesses company's leadership, executive pay, audits, internal controls, and shareholder rights
            """)
    
    return app

if __name__ == "__main__":
    app = create_interface()
    app.launch(share=True, debug=True)