app.py

import gradio as gr
import pandas as pd
from utilfuncs import (
    load_kmeans_model,
    load_churn_model,
    encode_and_scale,
    get_cluster,
    get_churn_label,
    load_encoder,
    load_scaler,
    create_tsne_plot
)

def app():
    kmeans_model = load_kmeans_model('models/kmeans_model_main.pkl')
    churn_model = load_churn_model('models/neural_network_model_final.pth')
    encoders = load_encoder('models/label_encoders.pkl')
    scaler = load_scaler('models/scaler.pkl')

    cluster_plans = {
    0: "Premium Subscription",
    1: "Basic Subscription",
    2: "Family Plan",
    3: "Small Business Plan",
    4: "Unlimited Usage Plan",
    5: "Student Discount Plan",
    6: "Senior Citizen Plan",
    7: "Power User Plan"
    }

    def get_plan_recommendation(cluster):
        """
        Given a cluster number, returns the recommended subscription plan.
        
        Parameters:
        cluster (int): The cluster number the user belongs to.
        
        Returns:
        str: The recommended subscription plan.
        """
        if cluster in cluster_plans:
            return cluster_plans[cluster]
        else:
            return "No specific plan recommendation available"

    def predict(age, gender, location, subscription_length, monthly_bill, avg_internet_usage, num_tickets, avg_talktime, social_class, subscription_type, base_charge):
        data = {
            'Age': [age],
            'Gender': [gender],
            'Location': [location],
            'Subscription_Length_Months': [subscription_length],
            'Monthly_Bill': [monthly_bill],
            'Average_Internet_Usage': [avg_internet_usage],
            'No_of_Tickets': [num_tickets],
            'Average_Talktime_Usage': [avg_talktime],
            'Social_Class': [social_class],
            'Subscription_Type': [subscription_type],
            'Base_Charge': [base_charge]
        }
        df = pd.DataFrame(data)
        scaled_df = encode_and_scale(df, encoders, scaler)
        group = get_cluster(scaled_df, kmeans_model)
        predicted_group_text = group[0]
        suggested_plan = get_plan_recommendation(predicted_group_text)
        churn_label = get_churn_label(scaled_df, churn_model)
        predicted_churn_text = 'Yes' if churn_label[0][0] == 1 else 'No'
        
        return predicted_group_text, predicted_churn_text, suggested_plan

    def process_csv(file):
        df = pd.read_csv(file)

        required_columns = ['CustomerID', 'Name', 'Age', 'Gender', 'Location',
                            'Subscription_Length_Months', 'Monthly_Bill', 'Average_Internet_Usage',
                            'No_of_Tickets', 'Average_Talktime_Usage', 'Social_Class',
                            'Subscription_Type', 'Base_Charge']
        
        if not all(col in df.columns for col in required_columns):
            return "Error: The CSV file must have the following columns: " + ", ".join(required_columns)
        
        df = df.drop(columns=['CustomerID', 'Name'])

        scaled_df = encode_and_scale(df, encoders, scaler)
        groups = get_cluster(scaled_df, kmeans_model)
        churn_labels = get_churn_label(scaled_df, churn_model)

        tsne_plot = create_tsne_plot(scaled_df, groups)

        churn_percentage = (churn_labels[:, 0] == 1).float().mean() * 100

        return tsne_plot, f"Predicted Churn Percentage: {churn_percentage:.2f}%"

    with gr.Blocks() as app:
        with gr.Tab("Churn Prediction"):
            with gr.Group():
                gr.Markdown("## Churn Prediction App")
                age = gr.Slider(label="Age", minimum=18, maximum=100, step=1, value=30)
                gender = gr.Dropdown(label="Gender", choices=["Male", "Female"], value="Male")
                location = gr.Dropdown(label="Location", choices=["Urban", "Rural", "Suburban"], value="Urban")
                subscription_length = gr.Slider(label="Subscription Length (Months)", minimum=1, maximum=60, step=1, value=12)
                monthly_bill = gr.Slider(label="Monthly Bill", minimum=10, maximum=1000, step=1, value=50)
                avg_internet_usage = gr.Slider(label="Average Internet Usage", minimum=1, maximum=200, step=1, value=50)
                num_tickets = gr.Slider(label="Number of Tickets", minimum=0, maximum=20, step=1, value=2)
                avg_talktime = gr.Slider(label="Average Talktime Usage", minimum=1, maximum=200, step=1, value=50)
                social_class = gr.Slider(label="Social Class", minimum=1, maximum=3, step=1, value=1)
                subscription_type = gr.Dropdown(label="Subscription Type", choices=["A", "B", "C", "D", "E"], value="B")
                base_charge = gr.Slider(label="Base Charge", minimum=10, maximum=500, step=1, value=30)

            with gr.Group():
                gr.Markdown("## Results")
                predicted_group = gr.Text(label="Predicted Group")
                predicted_churn = gr.Text(label="Predicted Churn")
                suggested_plan = gr.Text(label="Suggested Plan")

            predict_button = gr.Button("Predict")
            predict_button.click(predict, inputs=[age, gender, location, subscription_length, monthly_bill, avg_internet_usage, num_tickets, avg_talktime, social_class, subscription_type, base_charge], outputs=[predicted_group, predicted_churn, suggested_plan])

        with gr.Tab("Upload CSV"):
            with gr.Group():
                gr.Markdown("## Upload CSV for Analysis")
                csv_file = gr.File(label="Upload CSV")
                process_button = gr.Button("Process CSV")

            with gr.Group():
                gr.Markdown("## Results")
                tsne_plot = gr.Image(label="t-SNE Plot")
                churn_percentage = gr.Text(label="Predicted Churn Percentage")

            process_button.click(process_csv, inputs=[csv_file], outputs=[tsne_plot, churn_percentage])

    app.launch()

if __name__ == "__main__":
    app()