Create app.py

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+import joblib
+import pandas as pd
+from PIL import Image
+
+best_model = joblib.load("best_model.pkl")
+roc_img = Image.open("roc_curve_rf_tuned.png")
+
+def churn_prediction(age, gender, tenure, usage_frequency, support_calls,
+                     payment_delay, last_interaction, total_spend,
+                     subscription_type, contract_length):
+    try:
+
+        input_data = {
+            "Age": age,
+            "Gender_Male": 1 if gender == "Male" else 0,
+            "Gender_Female": 1 if gender == "Female" else 0,
+            "Usage Frequency": usage_frequency,
+            "Support Calls": support_calls,
+            "Contract Length_Monthly": 1 if contract_length == "Monthly" else 0,
+            "Contract Length_Quarterly": 1 if contract_length == "Quarterly" else 0,
+            "Contract Length_Annual": 1 if contract_length == "Annual" else 0,
+            "Payment Delay": payment_delay,
+            "Last Interaction": last_interaction,
+            "Total Spend": total_spend,
+            "Tenure": tenure,
+            "Subscription Type_Basic": 1 if subscription_type == "Basic" else 0,
+            "Subscription Type_Premium": 1 if subscription_type == "Premium" else 0,
+            "Subscription Type_Standard": 1 if subscription_type == "Standard" else 0,
+        }
+
+        input_df = pd.DataFrame([input_data])
+
+        # Predict churn and probability
+        prediction = best_model.predict(input_df)
+        prediction_proba = best_model.predict_proba(input_df)[:, 1]
+
+        churn_result = "Yes" if prediction[0] == 1 else "No"
+        churn_probability = f"{prediction_proba[0]:.2f}"
+
+        return churn_result, churn_probability, roc_img
+
+    except Exception as e:
+        return f"Error: {str(e)}", "N/A", None
+
+inputs = [
+    gr.Slider(18, 100, value=40, label="Age"),
+    gr.Dropdown(["Female", "Male"], value="Male", label="Gender"),
+    gr.Slider(1, 60, value=30, label="Tenure (months)"),
+    gr.Slider(1, 30, value=15, label="Usage Frequency"),
+    gr.Slider(0, 10, value=4, label="Support Calls"),
+    gr.Slider(0, 30, value=15, label="Payment Delay"),
+    gr.Slider(1, 30, value=15, label="Last Interaction (days ago)"),
+    gr.Slider(100, 1000, value=620, label="Total Spend"),
+    gr.Dropdown(["Premium", "Standard", "Basic"], value="Standard", label="Subscription Type"),
+    gr.Dropdown(["Monthly", "Quarterly", "Annual"], value="Annual", label="Contract Length")
+]
+
+outputs = [
+    gr.Textbox(label="Churn Prediction"),
+    gr.Textbox(label="Churn Probability"),
+    gr.Image(label="ROC Curve")
+]
+
+gr.Interface(
+    fn=churn_prediction,
+    inputs=inputs,
+    outputs=outputs,
+    title="Customer Churn Prediction"
+).launch()