Spaces:
Runtime error
Runtime error
File size: 2,583 Bytes
92b63f0 |
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 |
import streamlit as st
import pandas as pd
from make_prediction import get_prediction
import pickle
def project_ui():
st.image("assets/customer_churn_image.jpg",width=600)
st.title("Customer Churn Prediction")
age = st.number_input("**Age**", min_value=18, max_value=100, step=1)
gender = st.selectbox("**Gender**", options=["Male", "Female"])
gender_encoded = 1 if gender == "Male" else 0
tenure = st.number_input("**Tenure (months)**", min_value=0, step=1)
usage_frequency = st.number_input("**Usage Frequency**", min_value=0, step=1)
support_calls = st.number_input("**Support Calls**", min_value=0, step=1)
payment_delay = st.number_input("**Payment Delay**", min_value=0, step=1)
subscription_type = st.selectbox("**Subscription Type**", options=["Standard", "Basic", "Premium"])
subscription_type_encoded = {"Standard": 2, "Basic": 0, "Premium": 1}[subscription_type]
contract_length = st.selectbox("**Contract Length**", options=["Annual", "Monthly", "Quarterly"])
contract_length_encoded = {"Annual": 0, "Monthly": 1, "Quarterly": 2}[contract_length]
total_spend = st.number_input("**Total Spend**", min_value=0.0, step=1.0)
last_interaction = st.number_input("Last Interaction (days ago)", min_value=0, step=1)
# Create DataFrame of input data for the prediction
input_data = pd.DataFrame({
"Age": [age],
"Gender": [gender_encoded],
"Tenure": [tenure],
"Usage Frequency": [usage_frequency],
"Support Calls": [support_calls],
"Payment Delay": [payment_delay],
"Subscription Type": [subscription_type_encoded],
"Contract Length": [contract_length_encoded],
"Total Spend": [total_spend],
"Last Interaction": [last_interaction],
})
if st.button("Predict Churn"):
prediction = get_prediction(input_data)
if prediction is not None:
churn_value = int(prediction['predictions'][0])
churn_prediction = "Will Churn" if churn_value == 1 else "Won't Churn"
st.success(f"Prediction: {churn_prediction}")
else:
st.write("Prediction request failed. We are using local model ")
with open("backend/artifacts/XGBoost.pkl","rb") as file:
model= pickle.load(file)
result = model.predict(input_data)
churn_prediction = "Will Churn" if result ==1 else "Won't Churn"
st.success(f"Prediction : {churn_prediction}")
if __name__ == "__main__":
project_ui()
|