Update app.py

app.py

@@ -1,266 +1,144 @@
-import pandas as pd
 import networkx as nx
-import plotly.graph_objects as go
+import matplotlib.pyplot as plt
 import gradio as gr
-import re
-import logging
+from io import BytesIO
+from PIL import Image
+
+# Define investors and their companies
+investors = {
+    "Accel": ["Meta", "Dropbox", "Spotify", "Adroll", "PackLink", "Zoom", "Slack"],
+    "Andreessen Horowitz": [
+        "Airbnb", "Lyft", "Pinterest", "Coinbase", "Robinhood", "Slack"
+    ],
+    "Google Ventures": ["Uber", "LendingClub"],
+    "Greylock": ["Workday", "Palo Alto Networks"],
+    "Lightspeed Venture Partners": ["Snap", "Grubhub", "AppDynamics"],
+    "Benchmark": ["Snap", "Uber", "WeWork"],
+    "Norwest Venture Partners": ["LendingClub", "Opendoor"],
+    "Emergence Capital Partners": ["Zoom", "Box", "Salesforce"],
+    "Trinity Ventures": ["New Relic", "Care.com", "TubeMogul"],
+    "Citi Ventures": ["Square", "Nutanix"],
+    "Sequoia": ["Alphabet (Google)", "NVIDIA", "Dropbox", "Airbnb", "Meta"],
+    "Y Combinator": ["Dropbox", "Airbnb", "Coinbase", "DoorDash", "Reddit"]
+}
+
+# Example market capitalization values (in billions USD)
+market_cap = {
+    "Meta": 900,
+    "Dropbox": 10,
+    "Spotify": 30,
+    "Zoom": 20,
+    "Slack": 27,
+    "Airbnb": 100,
+    "Lyft": 4,
+    "Pinterest": 14,
+    "Coinbase": 70,
+    "Robinhood": 10,
+    "Uber": 60,
+    "LendingClub": 1,
+    "Snap": 18,
+    "Grubhub": 6,
+    "AppDynamics": 1,
+    "WeWork": 0.9,
+    "Opendoor": 3,
+    "Box": 4,
+    "Salesforce": 200,
+    "Square": 90,
+    "Nutanix": 10,
+    "Alphabet (Google)": 1500,
+    "NVIDIA": 1200
+}
+
+# Assign default size for missing companies
+default_size = 5
+
+# Define a color map for the investors
+investor_colors = {
+    "Accel": "#1f77b4",
+    "Andreessen Horowitz": "#ff7f0e",
+    "Google Ventures": "#2ca02c",
+    "Greylock": "#d62728",
+    "Lightspeed Venture Partners": "#9467bd",
+    "Benchmark": "#8c564b",
+    "Norwest Venture Partners": "#e377c2",
+    "Emergence Capital Partners": "#7f7f7f",
+    "Trinity Ventures": "#bcbd22",
+    "Citi Ventures": "#17becf",
+    "Sequoia": "#1b9e77",
+    "Y Combinator": "#d95f02"
+}
+
+def generate_graph(selected_investors):
+    if not selected_investors:
+        selected_investors = list(investors.keys())
 
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-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.")
-
-# Build investor-company mapping
-def build_investor_company_mapping(df):
-    mapping = {}
-    for _, row in df.iterrows():
-        company = row["Company"]
-        investors = row["Select_Investors"]
-        if pd.notnull(investors):
-            for investor in investors.split(","):
-                investor = investor.strip()
-                if investor:
-                    mapping.setdefault(investor, []).append(company)
-    return mapping
-
-investor_company_mapping = build_investor_company_mapping(data)
-logger.info("Investor to company mapping created.")
-
-# Filter investors by country, industry, investor selection, and company selection
-def filter_investors(selected_country, selected_industry, selected_investors, selected_company):
-    filtered_data = data.copy()
-    if selected_country != "All":
-        filtered_data = filtered_data[filtered_data["Country"] == selected_country]
-    if selected_industry != "All":
-        filtered_data = filtered_data[filtered_data["Industry"] == selected_industry]
-    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())
-    return filtered_investors, filtered_data
-
-# Generate Plotly graph
-def generate_graph(investors, filtered_data):
-    if not investors:
-        logger.warning("No investors selected.")
-        return go.Figure()
-
-    # Create a color map for investors
-    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
-    ]
-    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()
+
+    # Add edges and nodes based on selected investors
+    for investor in selected_investors:
+        companies = investors[investor]
         for company in companies:
-            G.add_node(company)
-            G.add_node(investor)
-            G.add_edge(investor, company)
-    
-    pos = nx.spring_layout(G, seed=142)
-    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,
-        line=dict(width=0.5, color='#aaaaaa'),
-        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)
-        node_y.append(y)
-        if node in investors:
-            node_text.append(node)
-            node_color.append(investor_color_map[node])
-            node_size.append(30)
-            node_hovertext.append(f"Investor: {node}")
+            G.add_edge(investor, company, color=investor_colors[investor])
+
+    # Get edge colors
+    edge_colors = [G[u][v]['color'] for u, v in G.edges]
+
+    # Set node colors and sizes
+    node_colors = []
+    node_sizes = []
+    for node in G.nodes:
+        if node in investor_colors:
+            node_colors.append(investor_colors[node])
+            node_sizes.append(2000)  # Fixed size for investors
         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("")
-            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"
-            node_hovertext.append(hovertext)
-    
-    node_trace = go.Scatter(
-        x=node_x,
-        y=node_y,
-        text=node_text,
-        mode='markers+text',
-        hoverinfo='text',
-        hovertext=node_hovertext,
-        marker=dict(
-            showscale=False,
-            size=node_size,
-            color=node_color,
-            line=dict(width=0.5, color='#333333')
-        ),
-        textposition="middle center",
-        textfont=dict(size=15, color="#000000")
-    )
-    
-    legend_items = []
-    for investor in unique_investors:
-        legend_items.append(
-            go.Scatter(
-                x=[None],
-                y=[None],
-                mode='markers',
-                marker=dict(
-                    size=14,
-                    color=investor_color_map[investor]
-                ),
-                legendgroup=investor,
-                showlegend=False,
-                name=investor
-            )
-        )
-    
-    fig = go.Figure(data=legend_items + [edge_trace, node_trace])
-    fig.update_layout(
-        title="Venture Capital Networks in September 2024",
-        titlefont_size=24,
-        margin=dict(l=20, r=20, t=20, b=20),
-        hovermode='closest',
-        width=1400,
-        height=1000
+            node_colors.append("#F0E68C")  # Khaki for companies
+            node_sizes.append(market_cap.get(node, default_size) * 100)  # Scale up sizes
+
+    # Create plot
+    plt.figure(figsize=(18, 18))
+    pos = nx.spring_layout(G, k=0.2, seed=42)  # Fixed seed for consistency
+    nx.draw(
+        G, pos,
+        with_labels=True,
+        node_size=node_sizes,
+        node_color=node_colors,
+        font_size=10,
+        font_weight="bold",
+        edge_color=edge_colors,
+        width=2
     )
-    
-    fig.update_layout(
-        autosize=True,
-        xaxis={'showgrid': False, 'zeroline': False, 'visible': False},
-        yaxis={'showgrid': False, 'zeroline': False, 'visible': False}
-    )
-    
-    return fig
+    plt.title("Venture Funded Companies as a Densely Connected Subgraph", fontsize=20)
+    plt.axis('off')
+
+    # Save plot to a BytesIO object
+    buf = BytesIO()
+    plt.savefig(buf, format="png", bbox_inches="tight")
+    plt.close()
+    buf.seek(0)
 
-# Gradio app
-def app(selected_country, selected_industry, selected_company, selected_investors):
-    investors, filtered_data = filter_investors(selected_country, selected_industry, selected_investors, selected_company)
-    if not investors:
-        return "No investors found with the selected filters.", go.Figure()
-    graph = generate_graph(investors, filtered_data)
-    return ', '.join(investors), graph
+    # Convert BytesIO to PIL image
+    image = Image.open(buf)
+    return image
 
-# Main function
+# Define Gradio interface
 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.
-        """)
-        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")
-            company_filter = gr.Dropdown(choices=company_list, label="Company", value="All")
-            investor_filter = gr.Dropdown(choices=investor_list, label="Select Investors", value=[], multiselect=True)
-        graph_output = gr.Plot(label="Network Graph")
-        
-        inputs = [country_filter, industry_filter, company_filter, investor_filter]
-        outputs = [investor_output, graph_output]
-        
-        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.
-        """)
-    
-    demo.launch()
+    # Create a sorted list of investors for better UX
+    investor_list = sorted(investors.keys())
+
+    iface = gr.Interface(
+        fn=generate_graph,
+        inputs=gr.CheckboxGroup(
+            choices=investor_list,
+            label="Select Investors",
+            value=investor_list  # Default to all selected
+        ),
+        outputs=gr.Image(type="pil", label="Venture Network Graph"),
+        title="Venture Networks Visualization",
+        description="Select investors to visualize their investments in various companies. The graph shows connections between investors and the companies they've invested in. Node sizes represent market capitalization.",
+        flagging_mode="never"
+    )
+
+    iface.launch()
 
 if __name__ == "__main__":
     main()