Update app.py

app.py

@@ -21,95 +21,114 @@ COMPANY_DICT = {
     "Netflix": "NFLX"
 }
 
-def create_line_plot(df: pd.DataFrame) -> go.Figure:
+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'
+        name='ESG Score',
+        line=dict(color='rgb(55, 83, 109)', width=2),
+        marker=dict(size=10)
     ))
     fig.update_layout(
-        title="ESG Scores Trend",
+        title=f"ESG Scores Trend for {company_name}",
         xaxis_title="ESG Category",
         yaxis_title="Score",
-        yaxis_range=[0, 100]
+        yaxis_range=[0, 100],
+        height=400,
+        template='plotly_white'
     )
     return fig
 
-def create_scatter_plot(df: pd.DataFrame) -> go.Figure:
+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=12),
+        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="ESG Scores Distribution",
+        title=f"ESG Score Distribution for {company_name}",
         xaxis_title="ESG Category",
         yaxis_title="Score",
-        yaxis_range=[0, 100]
+        yaxis_range=[0, 100],
+        height=400,
+        template='plotly_white'
     )
     return fig
 
-def create_bar_plot(df: pd.DataFrame) -> go.Figure:
+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="ESG Scores Comparison",
+        title=f"ESG Scores Comparison for {company_name}",
         xaxis_title="ESG Category",
         yaxis_title="Score",
-        yaxis_range=[0, 100]
+        yaxis_range=[0, 100],
+        height=400,
+        template='plotly_white'
     )
     return fig
 
-def fetch_esg_data(company_name: str) -> Tuple[Optional[str], str]:
+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.
-    
-    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:
@@ -137,55 +156,35 @@ def create_interface() -> gr.Blocks:
                         visible=True
                     )
             
+            with gr.Row():
+                line_plot = gr.Plot(label="Trend Analysis")
+            
             with gr.Row():
                 with gr.Column():
-                    plot = gr.Plot(label="ESG Scores Analysis")
+                    scatter_plot = gr.Plot(label="Distribution Analysis")
+                with gr.Column():
+                    bar_plot = gr.Plot(label="Comparison Analysis")
             
-            def process_esg_request(company_name: str) -> Tuple[go.Figure, str]:
+            def process_esg_request(company_name: str) -> Tuple[go.Figure, go.Figure, 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
-                    )
-                    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)"
-                ))
+                    empty_plot = create_empty_plot(status)
+                    return empty_plot, empty_plot, empty_plot, status
                 
-                # 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
-                )
+                # Create all three 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)
                 
-                return fig, status
+                return line, scatter, bar, status
             
             # Connect the button click to the process function
             plot_button.click(
                 fn=process_esg_request,
                 inputs=company,
-                outputs=[plot, status_message],
+                outputs=[line_plot, scatter_plot, bar_plot, status_message],
                 api_name="generate_esg_analysis"
             )
         
@@ -197,8 +196,10 @@ def create_interface() -> gr.Blocks:
                 
                 ### How to Use
                 1. Select a company from the dropdown menu
-                2. Click 'Generate ESG Analysis' to view the visualization
-                3. The plot shows the Environmental, Social, and Governance scores for the selected company
+                2. Click 'Generate ESG Analysis' to view multiple visualizations:
+                   - Trend Analysis: Shows the progression across ESG categories
+                   - Distribution Analysis: Displays the spread of ESG scores
+                   - Comparison Analysis: Compares ESG scores side by side
                 
                 ### Metrics Explained
                 - **Environmental Score**: Measures company's environmental impact and sustainability initiatives