Spaces:
Sleeping
Sleeping
File size: 2,494 Bytes
e103010 756dbf1 e103010 756dbf1 e103010 756dbf1 e103010 756dbf1 e103010 756dbf1 e103010 756dbf1 e103010 756dbf1 e103010 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
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 - 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, 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)
# Create feature array with 4 features
features = np.array([[policy_term, policy_age, payment_encoded, communication_score]])
# 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,
"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 UI
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, step=0.01, label="Communication Score (0 to 1)")
],
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()
|