Update app.py

app.py

@@ -25,22 +25,10 @@ except Exception as e:
 data.columns = data.columns.str.strip().str.lower()
 logger.info(f"Standardized Column Names: {data.columns.tolist()}")
 
-# Identify the valuation column
-valuation_columns = [col for col in data.columns if 'valuation' in col.lower()]
-if len(valuation_columns) != 1:
-    logger.error("Unable to identify a single valuation column.")
-    raise ValueError("Dataset should contain exactly one column with 'valuation' in its name.")
-
-valuation_column = valuation_columns[0]
-logger.info(f"Using valuation column: {valuation_column}")
-
 # Clean and prepare data
-data["valuation_billions"] = data[valuation_column].replace({'\$': '', ',': ''}, regex=True)
-data["valuation_billions"] = pd.to_numeric(data["valuation_billions"], errors='coerce')
 data = data.apply(lambda col: col.str.strip() if col.dtype == "object" else col)
 data.rename(columns={
     "company": "Company",
-    "valuation_billions": "Valuation_Billions",
     "date_joined": "Date_Joined",
     "country": "Country",
     "city": "City",
@@ -66,8 +54,8 @@ def build_investor_company_mapping(df):
 investor_company_mapping = build_investor_company_mapping(data)
 logger.info("Investor to company mapping created.")
 
-# Filter investors by country, industry, and valuation threshold
-def filter_investors(selected_country, selected_industry, valuation_threshold):
+# Filter investors by country and industry (removed valuation threshold)
+def filter_investors(selected_country, selected_industry):
     filtered_data = data.copy()
     if selected_country != "All":
         filtered_data = filtered_data[filtered_data["Country"] == selected_country]
@@ -75,14 +63,10 @@ def filter_investors(selected_country, selected_industry, valuation_threshold):
         filtered_data = filtered_data[filtered_data["Industry"] == selected_industry]
 
     investor_company_mapping_filtered = build_investor_company_mapping(filtered_data)
-    investor_valuations = {
-        investor: filtered_data[filtered_data["Company"].isin(companies)]["Valuation_Billions"].sum()
-        for investor, companies in investor_company_mapping_filtered.items()
-    }
-    filtered_investors = [investor for investor, total in investor_valuations.items() if total >= valuation_threshold]
+    filtered_investors = list(investor_company_mapping_filtered.keys())
     return filtered_investors, filtered_data
 
-# Generate Plotly graph
+# Generate Plotly graph with increased size and improved styling
 def generate_graph(investors, filtered_data):
     if not investors:
         logger.warning("No investors selected.")
@@ -107,7 +91,7 @@ def generate_graph(investors, filtered_data):
     edge_trace = go.Scatter(
         x=edge_x,
         y=edge_y,
-        line=dict(width=0.5, color='#888'),
+        line=dict(width=1, color='#888'),
         hoverinfo='none',
         mode='lines'
     )
@@ -122,48 +106,41 @@ def generate_graph(investors, filtered_data):
         node_x.append(x)
         node_y.append(y)
         node_text.append(node)
-        if node in investors:
-            node_color.append(20)  # Fixed color value for investors
-        else:
-            valuation = filtered_data.loc[filtered_data["Company"] == node, "Valuation_Billions"].sum()
-            node_color.append(valuation)
+        node_color.append(10)  # Use a fixed color or other logic
 
     node_trace = go.Scatter(
         x=node_x,
         y=node_y,
         text=node_text,
-        mode='markers',
+        mode='markers+text',
         hoverinfo='text',
         marker=dict(
-            showscale=True,
-            colorscale='YlGnBu',
-            size=13,
+            showscale=False,
+            size=15,  # Increased size
             color=node_color,
-            colorbar=dict(
-                thickness=15,
-                title="Valuation (B)",
-                xanchor='left',
-                titleside='right'
-            )
-        )
+        ),
+        textposition="top center"  # Improved label positioning
     )
 
     fig = go.Figure(data=[edge_trace, node_trace])
     fig.update_layout(
         showlegend=False,
         title="Venture Networks",
-        titlefont_size=16,
-        margin=dict(l=40, r=40, t=40, b=40),
-        hovermode='closest'
+        titlefont_size=20,
+        margin=dict(l=20, r=20, t=50, b=20),
+        hovermode='closest',
+        width=1200,  # Increased width
+        height=800   # Increased height
     )
     return fig
 
 # Gradio app
-def app(selected_country, selected_industry, valuation_threshold):
-    investors, filtered_data = filter_investors(selected_country, selected_industry, valuation_threshold)
+def app(selected_country, selected_industry):
+    investors, filtered_data = filter_investors(selected_country, selected_industry)
     graph = generate_graph(investors, filtered_data)
     return investors, graph
 
+# Main function
 def main():
     country_list = ["All"] + sorted(data["Country"].dropna().unique())
     industry_list = ["All"] + sorted(data["Industry"].dropna().unique())
@@ -172,14 +149,12 @@ def main():
         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")
-            valuation_slider = gr.Slider(0, 50, value=20, step=1, label="Valuation Threshold (B)")
 
         investor_output = gr.Textbox(label="Filtered Investors")
         graph_output = gr.Plot(label="Network Graph")
 
-        country_filter.change(app, [country_filter, industry_filter, valuation_slider], [investor_output, graph_output])
-        industry_filter.change(app, [country_filter, industry_filter, valuation_slider], [investor_output, graph_output])
-        valuation_slider.change(app, [country_filter, industry_filter, valuation_slider], [investor_output, graph_output])
+        country_filter.change(app, [country_filter, industry_filter], [investor_output, graph_output])
+        industry_filter.change(app, [country_filter, industry_filter], [investor_output, graph_output])
 
     demo.launch()