Update app.py

app.py

@@ -21,56 +21,7 @@ COMPANY_DICT = {
     "Netflix": "NFLX"
 }
 
-def fetch_esg_data(company_name: str) -> Tuple[Optional[str], dict, dict, dict]:
-    """
-    Fetch and process ESG data for the selected company.
-    
-    Args:
-        company_name (str): Name of the company to fetch ESG data for
-        
-    Returns:
-        Tuple containing:
-        - 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
-        
-        if esg_data is None:
-            return None, None, None, None
-            
-        # Process ESG data
-        esg_df = pd.DataFrame(esg_data)
-        esg_scores = esg_df.loc[["environmentScore", "socialScore", "governanceScore"], :].dropna().astype(float)
-        
-        # Create plotting DataFrame
-        plot_df = pd.DataFrame({
-            "ESG Category": ["Environment", "Social", "Governance"],
-            "Score": esg_scores.squeeze().values
-        })
-        
-        # Save to CSV
-        csv_filename = f"{ticker}_esg_data.csv"
-        esg_df.to_csv(csv_filename)
-        
-        # 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 csv_filename, line_fig, scatter_fig, bar_fig
-        
-    except Exception as e:
-        logger.error(f"Error fetching ESG data: {str(e)}")
-        return None, None, None, None
-
-def create_line_plot(df: pd.DataFrame) -> dict:
+def create_line_plot(df: pd.DataFrame) -> go.Figure:
     """Create a line plot using plotly"""
     fig = go.Figure()
     fig.add_trace(go.Scatter(
@@ -85,9 +36,9 @@ def create_line_plot(df: pd.DataFrame) -> dict:
         yaxis_title="Score",
         yaxis_range=[0, 100]
     )
-    return fig.to_dict()
+    return fig
 
-def create_scatter_plot(df: pd.DataFrame) -> dict:
+def create_scatter_plot(df: pd.DataFrame) -> go.Figure:
     """Create a scatter plot using plotly"""
     fig = go.Figure()
     fig.add_trace(go.Scatter(
@@ -103,9 +54,9 @@ def create_scatter_plot(df: pd.DataFrame) -> dict:
         yaxis_title="Score",
         yaxis_range=[0, 100]
     )
-    return fig.to_dict()
+    return fig
 
-def create_bar_plot(df: pd.DataFrame) -> dict:
+def create_bar_plot(df: pd.DataFrame) -> go.Figure:
     """Create a bar plot using plotly"""
     fig = go.Figure()
     fig.add_trace(go.Bar(
@@ -119,7 +70,51 @@ def create_bar_plot(df: pd.DataFrame) -> dict:
         yaxis_title="Score",
         yaxis_range=[0, 100]
     )
-    return fig.to_dict()
+    return fig
+
+def fetch_esg_data(company_name: str) -> Tuple[Optional[str], str]:
+    """
+    Fetch and process ESG data for the selected company.
+    
+    Args:
+        company_name (str): Name of the company to fetch ESG data for
+        
+    Returns:
+        Tuple containing:
+        - DataFrame with processed data
+        - Status message
+    """
+    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
+        
+        if esg_data is None:
+            return None, 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)
+        
+        # Create plotting DataFrame
+        plot_df = pd.DataFrame({
+            "ESG Category": ["Environment", "Social", "Governance"],
+            "Score": esg_scores.squeeze().values
+        })
+        
+        # Save to CSV
+        csv_filename = f"{ticker}_esg_data.csv"
+        esg_df.to_csv(csv_filename)
+        
+        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"""
@@ -136,60 +131,61 @@ def create_interface() -> gr.Blocks:
                         value="Apple"
                     )
                     plot_button = gr.Button("Generate ESG Analysis", variant="primary")
-                    
-                    with gr.Row():
-                        csv_output = gr.File(label="Download Full ESG Data")
+                    status_message = gr.Textbox(
+                        label="Status",
+                        interactive=False,
+                        visible=True
+                    )
             
             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")
-                    
-            status_message = gr.Textbox(
-                label="Status",
-                interactive=False,
-                visible=True
-            )
+                    plot = gr.Plot(label="ESG Scores Analysis")
             
-            def process_esg_data(company_name):
-                try:
-                    csv_file, line, scatter, bar = fetch_esg_data(company_name)
-                    
-                    if csv_file is None:
-                        return (
-                            None,
-                            None,
-                            None,
-                            None,
-                            f"No ESG data available for {company_name}"
-                        )
-                    
-                    return (
-                        csv_file,
-                        line,
-                        scatter,
-                        bar,
-                        f"Successfully generated ESG analysis for {company_name}"
+            def process_esg_request(company_name: str) -> Tuple[go.Figure, str]:
+                # Fetch the data
+                plot_df, status = fetch_esg_data(company_name)
+                
+                if plot_df is None:
+                    # Create an empty figure with an error message
+                    fig = go.Figure()
+                    fig.add_annotation(
+                        text=status,
+                        xref="paper",
+                        yref="paper",
+                        x=0.5,
+                        y=0.5,
+                        showarrow=False
                     )
-                except Exception as e:
-                    return None, None, None, None, f"Error: {str(e)}"
+                    return fig, status
+                
+                # Create a combined figure with subplots
+                fig = go.Figure()
+                
+                # Add bar chart
+                fig.add_trace(go.Bar(
+                    x=plot_df["ESG Category"],
+                    y=plot_df["Score"],
+                    name="Score",
+                    marker_color="rgb(55, 83, 109)"
+                ))
+                
+                # Update layout
+                fig.update_layout(
+                    title=f"ESG Analysis for {company_name}",
+                    xaxis_title="ESG Category",
+                    yaxis_title="Score",
+                    yaxis_range=[0, 100],
+                    showlegend=True,
+                    height=500
+                )
+                
+                return fig, status
             
             # Connect the button click to the process function
             plot_button.click(
-                fn=process_esg_data,
+                fn=process_esg_request,
                 inputs=company,
-                outputs=[
-                    csv_output,
-                    line_plot,
-                    scatter_plot,
-                    bar_plot,
-                    status_message
-                ],
+                outputs=[plot, status_message],
                 api_name="generate_esg_analysis"
             )
         
@@ -201,8 +197,8 @@ def create_interface() -> gr.Blocks:
                 
                 ### 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
+                2. Click 'Generate ESG Analysis' to view the visualization
+                3. The plot shows the Environmental, Social, and Governance scores for the selected company
                 
                 ### Metrics Explained
                 - **Environmental Score**: Measures company's environmental impact and sustainability initiatives
@@ -214,4 +210,4 @@ def create_interface() -> gr.Blocks:
 
 if __name__ == "__main__":
     app = create_interface()
-    app.launch(share=True, debug=True)
+    app.launch(share=True, debug=True)