app.py

import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from io import BytesIO
from PIL import Image
import gradio as gr
import logging

# 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: strip whitespace and convert to lowercase
data.columns = data.columns.str.strip().str.lower()
logger.info(f"Standardized Column Names: {data.columns.tolist()}")

# Identify the valuation column dynamically
valuation_columns = [col for col in data.columns if 'valuation' in col.lower()]
if not valuation_columns:
    logger.error("No column containing 'Valuation' found in the dataset.")
    raise ValueError("Data Error: Unable to find the valuation column. Please check your CSV file.")
elif len(valuation_columns) > 1:
    logger.error("Multiple columns containing 'Valuation' found in the dataset.")
    raise ValueError("Data Error: Multiple valuation columns detected. Please ensure only one valuation column exists.")
else:
    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')
logger.info("Valuation data cleaned and converted to numeric.")

# Strip whitespace from all string columns
data = data.apply(lambda col: col.str.strip() if col.dtype == "object" else col)
logger.info("Whitespace stripped from all string columns.")

# Rename columns for consistency
expected_columns = {
    "company": "Company",
    "valuation_billions": "Valuation_Billions",
    "date_joined": "Date_Joined",
    "country": "Country",
    "city": "City",
    "industry": "Industry",
    "select_investors": "Select_Investors"
}

missing_columns = set(expected_columns.keys()) - set(data.columns)
if missing_columns:
    logger.error(f"Missing columns in the dataset: {missing_columns}")
    raise ValueError(f"Data Error: Missing columns {missing_columns} in the dataset.")

data = data.rename(columns=expected_columns)
logger.info("Columns renamed for consistency.")

# Parse the "Select_Investors" column to map investors to companies
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:  # Ensure investor is not an empty string
                    mapping.setdefault(investor, []).append(company)
    return mapping

investor_company_mapping = build_investor_company_mapping(data)
logger.info("Investor to company mapping created.")

# Function to filter investors based on selected country and industry
def filter_investors_by_country_and_industry(selected_country, selected_industry):
    filtered_data = data.copy()
    logger.info(f"Filtering data for Country: {selected_country}, Industry: {selected_industry}")
    
    if selected_country != "All":
        filtered_data = filtered_data[filtered_data["Country"] == selected_country]
        logger.info(f"Data filtered by country: {selected_country}. Remaining records: {len(filtered_data)}")
    if selected_industry != "All":
        filtered_data = filtered_data[filtered_data["Industry"] == selected_industry]
        logger.info(f"Data filtered by industry: {selected_industry}. Remaining records: {len(filtered_data)}")
    
    investor_company_mapping_filtered = build_investor_company_mapping(filtered_data)
    
    # Calculate total valuation per investor
    investor_valuations = {}
    for investor, companies in investor_company_mapping_filtered.items():
        total_valuation = filtered_data[filtered_data["Company"].isin(companies)]["Valuation_Billions"].sum()
        if total_valuation >= 20:  # Investors with >= 20B total valuation
            investor_valuations[investor] = total_valuation
    
    logger.info(f"Filtered investors with total valuation >= 20B: {len(investor_valuations)}")
    
    return list(investor_valuations.keys()), filtered_data

# Function to generate the graph
def generate_graph(selected_investors, filtered_data):
    if not selected_investors:
        logger.warning("No investors selected. Returning None for graph.")
        return None

    investor_company_mapping_filtered = build_investor_company_mapping(filtered_data)
    filtered_mapping = {inv: investor_company_mapping_filtered[inv] for inv in selected_investors if inv in investor_company_mapping_filtered}
    
    logger.info(f"Generating graph for {len(filtered_mapping)} investors.")
    
    # Build the graph
    G = nx.Graph()
    for investor, companies in filtered_mapping.items():
        for company in companies:
            G.add_edge(investor, company)
    
    # Node size based on valuation
    max_valuation = filtered_data["Valuation_Billions"].max()
    node_sizes = []
    for node in G.nodes:
        if node in filtered_mapping:
            node_sizes.append(1500)  # Fixed size for investors
        else:
            valuation = filtered_data.loc[filtered_data["Company"] == node, "Valuation_Billions"].sum()
            size = (valuation / max_valuation) * 1500 if max_valuation else 100
            node_sizes.append(size)
    
    # Node color: Investors (orange), Companies (green)
    node_colors = ["#FF8C00" if node in filtered_mapping else "#32CD32" for node in G.nodes]
    
    # Draw the graph
    plt.figure(figsize=(15, 15))
    pos = nx.spring_layout(G, k=0.2, seed=42)
    nx.draw(
        G, pos,
        with_labels=True,
        node_size=node_sizes,
        node_color=node_colors,
        font_size=10,
        edge_color="#A9A9A9",  # Light gray edges
        alpha=0.9
    )
    
    # Legend
    from matplotlib.lines import Line2D
    legend_elements = [
        Line2D([0], [0], marker='o', color='w', label='Investor', markersize=10, markerfacecolor='#FF8C00'),
        Line2D([0], [0], marker='o', color='w', label='Company', markersize=10, markerfacecolor='#32CD32')
    ]
    plt.legend(handles=legend_elements, loc='upper left')
    
    plt.title("Venture Network Visualization", fontsize=20)
    plt.axis("off")
    
    # Save plot to BytesIO
    buf = BytesIO()
    plt.savefig(buf, format="png", bbox_inches="tight")
    plt.close()
    buf.seek(0)
    
    logger.info("Graph generated successfully.")
    
    return Image.open(buf)

# Gradio app function
def app(selected_country, selected_industry):
    investor_list, filtered_data = filter_investors_by_country_and_industry(selected_country, selected_industry)
    logger.info("Updating CheckboxGroup and filtered data holder.")
    
    # Use gr.update() to create an update dictionary for the CheckboxGroup
    return gr.update(
        choices=investor_list,
        value=investor_list,
        visible=True
    ), filtered_data

# Gradio Interface
def main():
    country_list = ["All"] + sorted(data["Country"].dropna().unique())
    industry_list = ["All"] + sorted(data["Industry"].dropna().unique())
    
    logger.info(f"Available countries: {country_list}")
    logger.info(f"Available industries: {industry_list}")
    
    with gr.Blocks() as demo:
        with gr.Row():
            # Set default value to "US" for country and "Enterprise Tech" for industry
            country_filter = gr.Dropdown(choices=country_list, label="Filter by Country", value="United States")
            industry_filter = gr.Dropdown(choices=industry_list, label="Filter by Industry", value="Enterprise Tech")
        
        filtered_investor_list = gr.CheckboxGroup(choices=[], label="Select Investors", visible=False)
        graph_output = gr.Image(type="pil", label="Venture Network Graph")
        
        filtered_data_holder = gr.State()
        
        country_filter.change(
            app,
            inputs=[country_filter, industry_filter],
            outputs=[filtered_investor_list, filtered_data_holder]
        )
        industry_filter.change(
            app,
            inputs=[country_filter, industry_filter],
            outputs=[filtered_investor_list, filtered_data_holder]
        )
        
        filtered_investor_list.change(
            generate_graph,
            inputs=[filtered_investor_list, filtered_data_holder],
            outputs=graph_output
        )
    
    demo.launch()

if __name__ == "__main__":
    main()