Create app.py

app.py

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+import gradio as gr
+import xgboost as xgb
+import numpy as np
+import pickle
+import json
+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"
+
+def predict_lapse(policy_id, last_premium_paid_date, payment_mode, policy_term, policy_age):
+    # 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]
+
+    # Save to Salesforce (Optional)
+    try:
+        headers = {
+            "Authorization": SALESFORCE_AUTH_TOKEN,
+            "Content-Type": "application/json"
+        }
+        data = {
+            "Name": policy_id,
+            "Lapse_Risk_Score__c": risk_score,
+            "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
+        }
+        response = requests.post(SALESFORCE_ENDPOINT, json=data, headers=headers)
+        print("Salesforce Response:", response.status_code, response.text)
+    except Exception as e:
+        print("Salesforce Integration Error:", e)
+
+    return round(risk_score, 3)
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict_lapse,
+    inputs=[
+        gr.Text(label="Policy ID"),
+        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
+    ],
+    outputs=gr.Number(label="Lapse Risk Score (0 - 1)"),
+    title="Lapse Risk Predictor",
+    description="Predict the likelihood of policy lapse using XGBoost model"
+)
+
+demo.launch()