Update app.py

app.py

@@ -1,86 +1,195 @@
 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 = {
+COMPANY_DICT = {
     "Apple": "AAPL",
     "Google": "GOOGL",
     "Microsoft": "MSFT",
     "Amazon": "AMZN",
-    "Tesla": "TSLA"
+    "Tesla": "TSLA",
+    "Meta": "META",
+    "NVIDIA": "NVDA",
+    "Netflix": "NFLX"
 }
 
-# Function to fetch ESG data for the selected company
-def fetch_esg_data(company_name):
-    # Get the ticker symbol from the company name
-    ticker = company_dict[company_name]
-    
-    # Fetch ESG data from Yahoo Finance
-    stock = yf.Ticker(ticker)
-    esg_data = stock.sustainability
+def fetch_esg_data(company_name: str) -> Tuple[pd.DataFrame, Optional[str], dict, dict, dict]:
+    """
+    Fetch and process ESG data for the selected company.
     
-    # If ESG data is available, process it into a DataFrame
-    if esg_data is not None:
-        esg_df = pd.DataFrame(esg_data)
+    Args:
+        company_name (str): Name of the company to fetch ESG data for
+        
+    Returns:
+        Tuple containing:
+        - DataFrame with ESG scores
+        - Path to saved CSV file
+        - Three plotly figures for different visualizations
+    """
+    try:
+        # Get the ticker symbol
+        ticker = COMPANY_DICT[company_name]
+        logger.info(f"Fetching ESG data for {company_name} ({ticker})")
+        
+        # Fetch ESG data
+        stock = yf.Ticker(ticker)
+        esg_data = stock.sustainability
         
-        # Extract only the relevant ESG scores and convert to a DataFrame
+        if esg_data is None:
+            raise ValueError(f"No ESG data available for {company_name}")
+            
+        # Process ESG data
+        esg_df = pd.DataFrame(esg_data)
         esg_scores = esg_df.loc[["environmentScore", "socialScore", "governanceScore"], :].dropna().astype(float)
         
-        # Prepare a DataFrame for plotting
+        # Create plotting DataFrame
         plot_df = pd.DataFrame({
             "ESG Category": ["Environment", "Social", "Governance"],
             "Score": esg_scores.squeeze().values
         })
         
-        # Save the ESG data to a CSV file
+        # Save to CSV
         csv_filename = f"{ticker}_esg_data.csv"
         esg_df.to_csv(csv_filename)
-        return plot_df, csv_filename, plot_df, plot_df
-    else:
-        # Return empty DataFrames and None if no data is available
-        return pd.DataFrame(), None, pd.DataFrame(), pd.DataFrame()
+        
+        # Create different plot types using plotly
+        line_fig = create_line_plot(plot_df)
+        scatter_fig = create_scatter_plot(plot_df)
+        bar_fig = create_bar_plot(plot_df)
+        
+        return plot_df, csv_filename, line_fig, scatter_fig, bar_fig
+        
+    except Exception as e:
+        logger.error(f"Error fetching ESG data: {str(e)}")
+        return pd.DataFrame(), None, {}, {}, {}
 
-# Gradio interface with multiple tabs
-def app_interface():
-    with gr.Blocks() as app:
-        with gr.Tab("ESG Data Analysis"):
-            # Dropdown to select company name
-            company = gr.Dropdown(
-                label="Select Company",
-                choices=list(company_dict.keys()),
-                value="Apple"
-            )
-            
-            # Button to fetch and plot ESG data
-            plot_button = gr.Button("Generate ESG Plot")
+def create_line_plot(df: pd.DataFrame) -> dict:
+    """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'
+    ))
+    fig.update_layout(
+        title="ESG Scores Trend",
+        xaxis_title="ESG Category",
+        yaxis_title="Score",
+        yaxis_range=[0, 100]
+    )
+    return fig.to_dict()
+
+def create_scatter_plot(df: pd.DataFrame) -> dict:
+    """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=12),
+        name='ESG Score'
+    ))
+    fig.update_layout(
+        title="ESG Scores Distribution",
+        xaxis_title="ESG Category",
+        yaxis_title="Score",
+        yaxis_range=[0, 100]
+    )
+    return fig.to_dict()
+
+def create_bar_plot(df: pd.DataFrame) -> dict:
+    """Create a bar plot using plotly"""
+    fig = go.Figure()
+    fig.add_trace(go.Bar(
+        x=df["ESG Category"],
+        y=df["Score"],
+        name='ESG Score'
+    ))
+    fig.update_layout(
+        title="ESG Scores Comparison",
+        xaxis_title="ESG Category",
+        yaxis_title="Score",
+        yaxis_range=[0, 100]
+    )
+    return fig.to_dict()
+
+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")
+                    
+                    with gr.Row():
+                        csv_output = gr.File(label="Download Full ESG Data")
             
-            # LinePlot component for displaying the ESG data
-            line_plot = gr.LinePlot(label="ESG Scores Line Plot", x="ESG Category", y="Score", overlay_point=True)
-            # ScatterPlot component for displaying the ESG data
-            scatter_plot = gr.ScatterPlot(plot_df, x="ESG Category", y="Score", overlay_point=True)
-            # BarPlot component for displaying the ESG data
-            bar_plot = gr.BarPlot(plot_df, x="ESG Category", y="Score")
+            with gr.Row():
+                with gr.Column():
+                    line_plot = gr.Plot(label="ESG Scores Trend")
+                    
+            with gr.Row():
+                with gr.Column():
+                    scatter_plot = gr.Plot(label="ESG Score Distribution")
+                with gr.Column():
+                    bar_plot = gr.Plot(label="ESG Score Comparison")
+                    
+            # Error message display
+            error_message = gr.Markdown(visible=False)
             
-            # File output for CSV download
-            csv_output = gr.File(label="Download CSV")
+            def handle_error(error):
+                return gr.Markdown.update(visible=True, value=f"⚠️ Error: {error}")
             
-            # Define the action when the "Generate ESG Plot" button is clicked
-            plot_button.click(fn=fetch_esg_data, inputs=company, outputs=[line_plot, csv_output, scatter_plot, bar_plot])
-        
-        with gr.Tab("Tab 2"):
-            gr.Markdown("This is Tab 2. You can add more content here.")
-        
-        with gr.Tab("Tab 3"):
-            gr.Markdown("This is Tab 3. Add your custom functionality here.")
+            # Connect the button click to the fetch function
+            plot_button.click(
+                fn=fetch_esg_data,
+                inputs=company,
+                outputs=[
+                    error_message,
+                    csv_output,
+                    line_plot,
+                    scatter_plot,
+                    bar_plot
+                ],
+                api_name="generate_esg_analysis"
+            )
         
-        with gr.Tab("Tab 4"):
-            gr.Markdown("This is Tab 4. Add more features or visualization here.")
+        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 the visualizations
+                3. Download the full ESG data as CSV for detailed analysis
+                
+                ### 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
 
-# Launch the Gradio app
-app = app_interface()
 if __name__ == "__main__":
-    app.launch()
-
+    app = create_interface()
+    app.launch(share=True, debug=True)