app.py

import joblib
import pandas as pd
import matplotlib.pyplot as plt
import gradio as gr

# Load pre-trained encoder and KMeans model
encoder = joblib.load('fund_type_encoder.pkl')
model = joblib.load('investor_segmentation.pkl')

# Function to process the uploaded file and create plot
def analyze_investors(file):
    df = pd.read_csv(file.name)  # Read uploaded file

    # Transform the 'Fund_Type_Viewed_Most' column using encoder
    X_encoded = encoder.transform(df[['Fund_Type_Viewed_Most']])

    # Predict cluster using the pre-trained model
    df['Cluster'] = model.predict(X_encoded)

    # Assign meaningful labels to the clusters
    cluster_labels = (
        df.groupby('Cluster')['Fund_Type_Viewed_Most']
        .agg(lambda x: x.value_counts().idxmax())
        .to_dict()
    )

    # Map cluster numbers to readable labels
    df['Cluster_Label'] = df['Cluster'].map(lambda c: f"{cluster_labels[c]}")

    # Prepare pie chart data
    cluster_counts = df['Cluster_Label'].value_counts()

    # Prepare text box data
    cluster_summary = df.groupby('Cluster_Label')['News_Reads_Per_Week'].sum()
    text_str = "\n".join([f"{label}: {reads} reads/week" for label, reads in cluster_summary.items()])

    # Create Plot
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))

    # Pie Chart
    #ax1.pie(cluster_counts, labels=cluster_counts.index, autopct='%1.1f%%', startangle=90)
    # Donut Chart
    ax1.pie(cluster_counts, labels=cluster_counts.index, autopct='%1.1f%%', startangle=90, wedgeprops={'width': 0.3})
    ax1.set_title('Fund Type Distribution')
    ax1.axis('equal')

    # Text Box
    ax2.axis('off')
    props = dict(boxstyle='round', facecolor='lightgrey', alpha=0.4)
    ax2.text(0.95, 0.9, text_str, fontsize=12, va='top', ha='right', bbox=props)
    ax2.set_title('Total News Reads per Week', loc='right')

    return fig

# Set up Gradio interface
gr.Interface(
    fn=analyze_investors, 
    inputs=gr.File(label="Upload Investors CSV"),
    outputs=gr.Plot(label="Investors Clusters Visualization")
).launch()