Update app.py

app.py

@@ -8,22 +8,25 @@ import requests
 # Load pre-trained model
 model = pickle.load(open("lapse_model.pkl", "rb"))
 
-# Salesforce (Optional)
-SALESFORCE_ENDPOINT = "https://orgfarm-ac78ff910d-dev-ed.develop.lightning.force.com/lightning/setup/SetupOneHome/home"
-SALESFORCE_AUTH_TOKEN = "AmmfRcd6IiYaRtSGntBnzNMQU"
+# Salesforce (Optional - replace with your actual endpoint and secure token handling!)
+SALESFORCE_ENDPOINT = "https://orgfarm-ac78ff910d-dev-ed.develop.lightning.force.com/services/data/vXX.0/sobjects/Lapse_Risk__c/"
+SALESFORCE_AUTH_TOKEN = "Bearer YOUR_SALESFORCE_TOKEN"  # Use environment variable in production!
 
-def predict_lapse(policy_id, last_premium_paid_date, payment_mode, policy_term, policy_age):
+def predict_lapse(policy_id, last_premium_paid_date, payment_mode, policy_term, policy_age, communication_score):
     # Map payment_mode to numeric
     payment_map = {"Annual": 0, "Semi-Annual": 1, "Quarterly": 2, "Monthly": 3}
     payment_encoded = payment_map.get(payment_mode, 0)
 
-    # Feature vector
-    features = np.array([[policy_term, policy_age, payment_encoded]])
-    
-    # Predict risk
-    risk_score = model.predict_proba(features)[0][1]
+    # Create feature array with 4 features
+    features = np.array([[policy_term, policy_age, payment_encoded, communication_score]])
 
-    # Save to Salesforce (Optional)
+    # Predict lapse risk
+    try:
+        risk_score = model.predict_proba(features)[0][1]
+    except Exception as e:
+        return f"Prediction failed: {e}"
+
+    # OPTIONAL: Send to Salesforce
     try:
         headers = {
             "Authorization": SALESFORCE_AUTH_TOKEN,
@@ -35,8 +38,8 @@ def predict_lapse(policy_id, last_premium_paid_date, payment_mode, policy_term,
             "Last_Paid_Date__c": last_premium_paid_date,
             "Premium_Payment_Mode__c": payment_mode,
             "Policy_Term__c": policy_term,
-            "Policy_Age__c": policy_age
-            #"Communication_Score__c": communication_score
+            "Policy_Age__c": policy_age,
+            "Communication_Score__c": communication_score
         }
         response = requests.post(SALESFORCE_ENDPOINT, json=data, headers=headers)
         print("Salesforce Response:", response.status_code, response.text)
@@ -45,7 +48,7 @@ def predict_lapse(policy_id, last_premium_paid_date, payment_mode, policy_term,
 
     return round(risk_score, 3)
 
-# Gradio Interface
+# Gradio UI
 demo = gr.Interface(
     fn=predict_lapse,
     inputs=[
@@ -53,8 +56,8 @@ demo = gr.Interface(
         gr.Text(label="Last Premium Paid Date (YYYY-MM-DD)"),
         gr.Dropdown(["Annual", "Semi-Annual", "Quarterly", "Monthly"], label="Payment Mode"),
         gr.Number(label="Policy Term (Years)"),
-        gr.Number(label="Policy Age (Years)")
-        #gr.Slider(0, 1, label="Communication Score (0 to 1)")  # a feature from comm. history analysis
+        gr.Number(label="Policy Age (Years)"),
+        gr.Slider(0, 1, step=0.01, label="Communication Score (0 to 1)")
     ],
     outputs=gr.Number(label="Lapse Risk Score (0 - 1)"),
     title="Lapse Risk Predictor",