app.py

import pandas as pd
import networkx as nx
import plotly.graph_objects as go
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
data.columns = data.columns.str.strip().str.lower()
logger.info(f"Standardized Column Names: {data.columns.tolist()}")

# Clean and prepare data
data = data.apply(lambda col: col.str.strip() if col.dtype == "object" else col)
data.rename(columns={
    "company": "Company",
    "valuation": "Valuation",
    "date_joined": "Date_Joined",
    "country": "Country",
    "city": "City",
    "industry": "Industry",
    "select_investors": "Select_Investors"
}, inplace=True)

# Convert valuation to numeric for proportional node sizing
data["Valuation"] = pd.to_numeric(
    data["Valuation"].replace({"\$": "", ",": ""}, regex=True), errors="coerce"
)
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, and investor selection
def filter_investors(selected_country, selected_industry, selected_investors):
    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 != ["All"]:
        filtered_data = filtered_data[
            filtered_data["Select_Investors"].apply(
                lambda x: any(inv in x for inv in selected_investors) if pd.notnull(x) else False
            )
        ]

    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()

    G = nx.Graph()
    for investor in investors:
        companies = filtered_data[filtered_data["Select_Investors"].str.contains(investor, na=False)]["Company"].tolist()
        for company in companies:
            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,
        line=dict(width=1, color="#888"),
        hoverinfo="none",
        mode="lines"
    )

    node_x = []
    node_y = []
    node_text = []
    node_color = []
    node_size = []

    # Color palette for investors (color blind friendly)
    investor_colors = [
        "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"
    ]

    investor_color_map = {investor: investor_colors[i % len(investor_colors)] for i, investor in enumerate(investors)}

    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)  # Label investors
            node_color.append(investor_color_map[node])  # Assign distinct colors to investors
            node_size.append(20)  # Fixed size for investor nodes
        else:
            valuation = filtered_data.loc[filtered_data["Company"] == node, "Valuation"].sum()
            node_text.append("")  # Hide company labels by default
            node_color.append("lightgreen")  # Light green for companies
            node_size.append(max(10, valuation / 100))  # Size proportional to valuation

    node_trace = go.Scatter(
        x=node_x,
        y=node_y,
        text=node_text,
        mode="markers",
        hoverinfo="text",
        marker=dict(
            showscale=False,
            size=node_size,
            color=node_color,
        )
    )

    fig = go.Figure(data=[edge_trace, node_trace])
    fig.update_layout(
        showlegend=False,
        title="Venture Networks",
        titlefont_size=20,
        margin=dict(l=20, r=20, t=50, b=20),
        hovermode="closest",
        width=1200,
        height=800
    )
    return fig

# Gradio app
def app(selected_country, selected_industry, selected_investors):
    investors, filtered_data = filter_investors(selected_country, selected_industry, selected_investors)
    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())
    investor_list = ["All"] + sorted(investor_company_mapping.keys())

    with gr.Blocks() as demo:
        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")
            investor_filter = gr.CheckboxGroup(choices=investor_list, label="Investors", value=["All"])

        investor_output = gr.Textbox(label="Filtered Investors")
        graph_output = gr.Plot(label="Network Graph")

        country_filter.change(
            app, [country_filter, industry_filter, investor_filter], [investor_output, graph_output]
        )
        industry_filter.change(
            app, [country_filter, industry_filter, investor_filter], [investor_output, graph_output]
        )
        investor_filter.change(
            app, [country_filter, industry_filter, investor_filter], [investor_output, graph_output]
        )

    demo.launch()

if __name__ == "__main__":
    main()