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app.py

@@ -1,11 +1,15 @@
-import streamlit as st
-import pandas as pd
-import requests
+
+# Load the trained model
+def load_model():
+    return joblib.load("backend_files/customer_prediction_model_v1_0.joblib")
+
+model = load_model()
 
 # Set the title of the Streamlit app
 st.title("ExtraaLearn Customer Predictor")
 st.subheader("Online Prediction")
 
+# Collect user input based on dataset columns
 # Collect user input for property features
 age                        = st.number_input("age", min_value=5, max_value=90, step=1, value=30)
 website_visits             = st.number_input("website_visits", min_value=0, step=1, value=1)
@@ -38,28 +42,13 @@ input_data = pd.DataFrame([{
 	'referral'                  :  'referral'
 }])
 
-# Make prediction when the "Predict" button is clicked
-if st.button("Predict"):
-    response = requests.post("https://<username>-<repo_id>.hf.space/v1/rental", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
-    if response.status_code == 200:
-        prediction = response.json()['Predicted Price (in dollars)']
-        st.success(f"Predicted Rental Price (in dollars): {prediction}")
-    else:
-        st.error("Error making prediction.")
-
-# Section for batch prediction
-st.subheader("Batch Prediction")
-
-# Allow users to upload a CSV file for batch prediction
-uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+# Set classification threshold
+classification_threshold = 0.5
 
-# Make batch prediction when the "Predict Batch" button is clicked
-if uploaded_file is not None:
-    if st.button("Predict Batch"):
-        response = requests.post("https://jackfroooot-AssignmentExtraaLearnBackend.hf.space/v1/rentalbatch", files={"file": uploaded_file})  # Send file to Flask API
-        if response.status_code == 200:
-            predictions = response.json()
-            st.success("Batch predictions completed!")
-            st.write(predictions)  # Display the predictions
-        else:
-            st.error("Error making batch prediction.")
+# Predict button
+if st.button("Predict"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "Join" if prediction == 1 else "not join"
+    st.write(f"Prediction: The customer is likely to **{result}**.")
+    st.write(f"Churn Probability: {prediction_proba:.2f}")

requirements.txt

@@ -1,3 +1,7 @@
+
 pandas==2.2.2
-requests==2.28.1
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
 streamlit==1.43.2