Update app.py

app.py

@@ -26,8 +26,7 @@ except Exception as e:
 data.columns = data.columns.str.strip().str.lower()
 logger.info(f"Standardized Column Names: {data.columns.tolist()}")
 
-# Filter out Health since Healthcare is the correct Market Sergment 
-print(data.head())
+# Filter out Health since Healthcare is the correct Market Segment
 data = data[data.industry != 'Health']
 
 # Identify the valuation column
@@ -97,21 +96,14 @@ def generate_graph(investors, filtered_data):
     unique_investors = investors
     num_colors = len(unique_investors)
     color_palette = [
-        "#377eb8",  # Blue
-        "#e41a1c",  # Red
-        "#4daf4a",  # Green
-        "#984ea3",  # Purple
-        "#ff7f00",  # Orange
-        "#ffff33",  # Yellow
-        "#a65628",  # Brown
-        "#f781bf",  # Pink
-        "#999999",  # Grey
+        "#377eb8", "#e41a1c", "#4daf4a", "#984ea3",
+        "#ff7f00", "#ffff33", "#a65628", "#f781bf", "#999999"
     ]
     while num_colors > len(color_palette):
         color_palette.extend(color_palette)
-    
+
     investor_color_map = {investor: color_palette[i] for i, investor in enumerate(unique_investors)}
-    
+
     G = nx.Graph()
     for investor in investors:
         companies = filtered_data[filtered_data["Select_Investors"].str.contains(re.escape(investor), na=False)]["Company"].tolist()
@@ -119,17 +111,16 @@ def generate_graph(investors, filtered_data):
             G.add_node(company)
             G.add_node(investor)
             G.add_edge(investor, company)
-    
+
     pos = nx.spring_layout(G, seed=42)
-    edge_x = []
-    edge_y = []
-    
+    edge_x, edge_y = [], []
+
     for edge in G.edges():
         x0, y0 = pos[edge[0]]
         x1, y1 = pos[edge[1]]
         edge_x.extend([x0, x1, None])
         edge_y.extend([y0, y1, None])
-    
+
     edge_trace = go.Scatter(
         x=edge_x,
         y=edge_y,
@@ -137,47 +128,38 @@ def generate_graph(investors, filtered_data):
         hoverinfo='none',
         mode='lines'
     )
-    
-    node_x = []
-    node_y = []
-    node_text = []
-    node_color = []
-    node_size = []
-    node_hovertext = []
-    
+
+    node_x, node_y, node_text = [], [], []
+    node_color, node_size, node_hovertext = [], [], []
+
     for node in G.nodes():
         x, y = pos[node]
         node_x.append(x)
         node_y.append(y)
         if node in investors:
-            node_text.append(node)
+            node_text.append("")  # Remove investor labels
             node_color.append(investor_color_map[node])
             node_size.append(30)
             node_hovertext.append(f"Investor: {node}")
         else:
             valuation = filtered_data.loc[filtered_data["Company"] == node, "Valuation_Billions"].values
             industry = filtered_data.loc[filtered_data["Company"] == node, "Industry"].values
-            if len(valuation) > 0 and not pd.isnull(valuation[0]):
-                size = valuation[0] * 5
-                if size < 10:
-                    size = 10
-            else:
-                size = 15
-            node_size.append(size)
-            node_text.append("")
+            size = valuation[0] * 5 if len(valuation) > 0 and not pd.isnull(valuation[0]) else 15
+            node_size.append(max(size, 10))
+            node_text.append("")  # No text label for companies
             node_color.append("#a6d854")
             hovertext = f"Company: {node}"
             if len(industry) > 0 and not pd.isnull(industry[0]):
                 hovertext += f"<br>Industry: {industry[0]}"
             if len(valuation) > 0 and not pd.isnull(valuation[0]):
-                hovertext += f"<br>Valuation: ${valuation[0]}B"
+                hovertext += f"<br>Valuation: ${valuation[0]:.2f}B"
             node_hovertext.append(hovertext)
-    
+
     node_trace = go.Scatter(
         x=node_x,
         y=node_y,
         text=node_text,
-        mode='markers+text',
+        mode='markers',
         hoverinfo='text',
         hovertext=node_hovertext,
         marker=dict(
@@ -185,28 +167,20 @@ def generate_graph(investors, filtered_data):
             size=node_size,
             color=node_color,
             line=dict(width=0.5, color='#333333')
-        ),
-        textposition="middle center",
-        textfont=dict(size=12, color="#000000")
-    )
-    
-    legend_items = []
-    for investor in unique_investors:
-        legend_items.append(
-            go.Scatter(
-                x=[None],
-                y=[None],
-                mode='markers',
-                marker=dict(
-                    size=10,
-                    color=investor_color_map[investor]
-                ),
-                legendgroup=investor,
-                showlegend=True,
-                name=investor
-            )
         )
-    
+    )
+
+    legend_items = [
+        go.Scatter(
+            x=[None], y=[None], mode='markers',
+            marker=dict(size=10, color=investor_color_map[investor]),
+            legendgroup=investor, showlegend=True, name=investor
+        ) for investor in unique_investors
+    ]
+
+    # Compute total market cap
+    total_market_cap = filtered_data["Valuation_Billions"].sum()
+
     fig = go.Figure(data=legend_items + [edge_trace, node_trace])
     fig.update_layout(
         title="Venture Networks",
@@ -214,15 +188,19 @@ def generate_graph(investors, filtered_data):
         margin=dict(l=20, r=20, t=60, b=20),
         hovermode='closest',
         width=1200,
-        height=800
-    )
-    
-    fig.update_layout(
+        height=800,
         autosize=True,
         xaxis={'showgrid': False, 'zeroline': False, 'visible': False},
-        yaxis={'showgrid': False, 'zeroline': False, 'visible': False}
+        yaxis={'showgrid': False, 'zeroline': False, 'visible': False},
+        annotations=[
+            dict(
+                x=0.5, y=1.1, xref='paper', yref='paper',
+                text=f"Total Market Cap of Companies: ${total_market_cap:.2f}B",
+                showarrow=False, font=dict(size=14), xanchor='center'
+            )
+        ]
     )
-    
+
     return fig
 
 # Gradio app
@@ -233,18 +211,14 @@ def app(selected_country, selected_industry, selected_company, selected_investor
     graph = generate_graph(investors, filtered_data)
     return ', '.join(investors), graph
 
-# Main function
 def main():
     country_list = ["All"] + sorted(data["Country"].dropna().unique())
     industry_list = ["All"] + sorted(data["Industry"].dropna().unique())
     company_list = ["All"] + sorted(data["Company"].dropna().unique())
     investor_list = sorted(investor_company_mapping.keys())
-    
+
     with gr.Blocks(title="Venture Networks Visualization") as demo:
-        gr.Markdown("""
-        # Venture Networks Visualization
-        Explore the connections between investors and companies in the venture capital ecosystem. Use the filters below to customize the network graph.
-        """)
+        gr.Markdown("# Venture Networks Visualization")
         with gr.Row():
             country_filter = gr.Dropdown(choices=country_list, label="Country", value="All")
             industry_filter = gr.Dropdown(choices=industry_list, label="Industry", value="All")
@@ -261,10 +235,8 @@ def main():
         industry_filter.change(app, inputs, outputs)
         company_filter.change(app, inputs, outputs)
         investor_filter.change(app, inputs, outputs)
-        
-        gr.Markdown("""
-        **Instructions:** Use the dropdowns to filter the network graph.
-        """)
+
+        gr.Markdown("**Instructions:** Use the dropdowns to filter the network graph.")
     
     demo.launch()