Update please

app.py

@@ -18,26 +18,17 @@ def app():
     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"
+        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:
@@ -91,9 +82,10 @@ def app():
         return tsne_plot, f"Predicted Churn Percentage: {churn_percentage:.2f}%"
 
     with gr.Blocks() as app:
-        with gr.Tab("Churn Prediction"):
+        # First Churn Prediction Tab
+        with gr.Tab("Churn Prediction 1"):
             with gr.Group():
-                gr.Markdown("## Churn Prediction App")
+                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")
@@ -107,7 +99,7 @@ def app():
                 base_charge = gr.Slider(label="Base Charge", minimum=10, maximum=500, step=1, value=30)
 
             with gr.Group():
-                gr.Markdown("## Results")
+                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")
@@ -115,6 +107,32 @@ def app():
             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")
@@ -131,4 +149,4 @@ def app():
     app.launch()
 
 if __name__ == "__main__":
-    app()
+    app()