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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):
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:
# First Churn Prediction Tab
with gr.Tab("Churn Prediction 1"):
with gr.Group():
gr.Markdown("## Churn Prediction App 1")
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 1")
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])
# Second Churn Prediction Tab
with gr.Tab("Churn Prediction 2"):
with gr.Group():
gr.Markdown("## Churn Prediction App 2")
age_dup = gr.Slider(label="Age", minimum=18, maximum=100, step=1, value=30)
gender_dup = gr.Dropdown(label="Gender", choices=["Male", "Female"], value="Male")
location_dup = gr.Dropdown(label="Location", choices=["Urban", "Rural", "Suburban"], value="Urban")
subscription_length_dup = gr.Slider(label="Subscription Length (Months)", minimum=1, maximum=60, step=1, value=12)
monthly_bill_dup = gr.Slider(label="Monthly Bill", minimum=10, maximum=1000, step=1, value=50)
avg_internet_usage_dup = gr.Slider(label="Average Internet Usage", minimum=1, maximum=200, step=1, value=50)
num_tickets_dup = gr.Slider(label="Number of Tickets", minimum=0, maximum=20, step=1, value=2)
avg_talktime_dup = gr.Slider(label="Average Talktime Usage", minimum=1, maximum=200, step=1, value=50)
social_class_dup = gr.Slider(label="Social Class", minimum=1, maximum=3, step=1, value=1)
subscription_type_dup = gr.Dropdown(label="Subscription Type", choices=["A", "B", "C", "D", "E"], value="B")
base_charge_dup = gr.Slider(label="Base Charge", minimum=10, maximum=500, step=1, value=30)
with gr.Group():
gr.Markdown("## Results 2")
predicted_group_dup = gr.Text(label="Predicted Group (Duplicate)")
predicted_churn_dup = gr.Text(label="Predicted Churn (Duplicate)")
suggested_plan_dup = gr.Text(label="Suggested Plan (Duplicate)")
predict_button_dup = gr.Button("Predict (Duplicate)")
predict_button_dup.click(predict, inputs=[age_dup, gender_dup, location_dup, subscription_length_dup, monthly_bill_dup, avg_internet_usage_dup, num_tickets_dup, avg_talktime_dup, social_class_dup, subscription_type_dup, base_charge_dup], outputs=[predicted_group_dup, predicted_churn_dup, suggested_plan_dup])
# Upload CSV Tab
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()
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