Update app.py

app.py

@@ -4,6 +4,7 @@ import plotly.graph_objects as go
 import gradio as gr
 import re
 import logging
+import os
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
@@ -12,43 +13,48 @@ logger = logging.getLogger(__name__)
 # Load and preprocess the dataset
 file_path = "cbinsights_data.csv"  # Replace with your actual file path
 
-try:
-    data = pd.read_csv(file_path, skiprows=1)
-    logger.info("CSV file loaded successfully.")
-except FileNotFoundError:
-    logger.error(f"File not found: {file_path}")
-    raise
-except Exception as e:
-    logger.error(f"Error loading CSV file: {e}")
-    raise
-
-# Standardize column names
-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",
-    "date_joined": "Date_Joined",
-    "country": "Country",
-    "city": "City",
-    "industry": "Industry",
-    "select_investors": "Select_Investors"
-}, inplace=True)
-
-logger.info("Data cleaned and columns renamed.")
+def load_data():
+    if not os.path.exists(file_path):
+        logger.error(f"File not found: {file_path}")
+        raise FileNotFoundError(f"File not found: {file_path}")
+
+    try:
+        data = pd.read_csv(file_path, skiprows=1)
+        logger.info("CSV file loaded successfully.")
+    except Exception as e:
+        logger.error(f"Error loading CSV file: {e}")
+        raise
+
+    # Standardize column names
+    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",
+        "date_joined": "Date_Joined",
+        "country": "Country",
+        "city": "City",
+        "industry": "Industry",
+        "select_investors": "Select_Investors"
+    }, inplace=True)
+
+    logger.info("Data cleaned and columns renamed.")
+    return data
+
+data = load_data()
 
 # Build investor-company mapping
 def build_investor_company_mapping(df):
@@ -73,11 +79,11 @@ def filter_investors(selected_country, selected_industry, selected_investors, se
         filtered_data = filtered_data[filtered_data["Country"] == selected_country]
     if selected_industry != "All":
         filtered_data = filtered_data[filtered_data["Industry"] == selected_industry]
+    if selected_company != "All":
+        filtered_data = filtered_data[filtered_data["Company"] == selected_company]
     if selected_investors:
         pattern = '|'.join([re.escape(inv) for inv in selected_investors])
         filtered_data = filtered_data[filtered_data["Select_Investors"].str.contains(pattern, na=False)]
-    if selected_company != "All":
-        filtered_data = filtered_data[filtered_data["Company"] == selected_company]
 
     investor_company_mapping_filtered = build_investor_company_mapping(filtered_data)
     filtered_investors = list(investor_company_mapping_filtered.keys())
@@ -105,9 +111,9 @@ def generate_graph(investors, filtered_data):
     ]
     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()
@@ -115,17 +121,17 @@ 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 = []
-    
+
     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,
@@ -133,14 +139,14 @@ def generate_graph(investors, filtered_data):
         hoverinfo='none',
         mode='lines'
     )
-    
+
     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)
@@ -168,7 +174,7 @@ def generate_graph(investors, filtered_data):
             if len(valuation) > 0 and not pd.isnull(valuation[0]):
                 hovertext += f"<br>Valuation: ${valuation[0]}B"
             node_hovertext.append(hovertext)
-    
+
     node_trace = go.Scatter(
         x=node_x,
         y=node_y,
@@ -185,7 +191,7 @@ def generate_graph(investors, filtered_data):
         textposition="middle center",
         textfont=dict(size=12, color="#000000")
     )
-    
+
     legend_items = []
     for investor in unique_investors:
         legend_items.append(
@@ -202,7 +208,7 @@ def generate_graph(investors, filtered_data):
                 name=investor
             )
         )
-    
+
     fig = go.Figure(data=legend_items + [edge_trace, node_trace])
     fig.update_layout(
         title="Venture Networks",
@@ -212,13 +218,13 @@ def generate_graph(investors, filtered_data):
         width=1200,
         height=800
     )
-    
+
     fig.update_layout(
         autosize=True,
         xaxis={'showgrid': False, 'zeroline': False, 'visible': False},
         yaxis={'showgrid': False, 'zeroline': False, 'visible': False}
     )
-    
+
     return fig
 
 # Gradio app
@@ -235,7 +241,7 @@ def main():
     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
@@ -249,19 +255,29 @@ def main():
         with gr.Row():
             investor_output = gr.Textbox(label="Filtered Investors", interactive=False)
         graph_output = gr.Plot(label="Network Graph")
-        
+
         inputs = [country_filter, industry_filter, company_filter, investor_filter]
         outputs = [investor_output, graph_output]
-        
+
+        # Update the graph when any filter changes
         country_filter.change(app, inputs, outputs)
         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.
+        **Instructions:**
+        - **Country**: Filter companies by country.
+        - **Industry**: Filter companies by industry.
+        - **Company**: Select a specific company to focus on.
+        - **Select Investors**: Choose investors to visualize their network connections.
+
+        **Tips:**
+        - Hover over nodes to see more information.
+        - Use the legend to identify investor nodes.
+        - Adjust filters to refine your network view.
         """)
-    
+
     demo.launch()
 
 if __name__ == "__main__":