upload 4 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_churn.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,122 @@
+import streamlit as st
+import numpy as np
+import pickle  # Change: 'pickle' imported instead of 'joblib'
+from tensorflow.keras.models import load_model
+
+# --- Load Model and Scaler ---
+@st.cache_resource
+def load_keras_model():
+    """Load the saved Keras model."""
+    try:
+        model = load_model("model_churn.keras")
+        return model
+    except Exception as e:
+        st.error(f"Error loading Keras model (model_churn.keras): {e}")
+        return None
+
+# ----- START: UPDATED SECTION -----
+@st.cache_resource
+def load_scaler_model():
+    """Load the saved Scaler (pickle)."""
+    try:
+        # Pickle files must be opened in 'rb' (read binary) mode
+        with open("scaler_churn", "rb") as f:
+            scaler = pickle.load(f)
+        return scaler
+    except FileNotFoundError:
+        st.error("Error: 'scaler_churn' file not found. Please make sure the file is uploaded.")
+        return None
+    except Exception as e:
+        st.error(f"Error loading Scaler (scaler_churn): {e}")
+        return None
+# ----- END: UPDATED SECTION -----
+
+# Load models
+model = load_keras_model()
+scaler = load_scaler_model()
+
+# Order of the 11 features expected by your model/scaler
+MODEL_INPUT_COLUMNS_ORDER = [
+    'CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',
+    'IsActiveMember', 'EstimatedSalary', 'Geography_Germany', 'Geography_Spain',
+    'Gender_Male'
+]
+
+# --- User Interface (UI) ---
+st.set_page_config(page_title="Customer Churn Prediction", layout="wide")
+st.title("🏦 Customer Churn Prediction Model")
+
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    st.header("Customer Information")
+    st.caption("Please enter customer information.")
+    
+    geography = st.selectbox("Country", ("France", "Spain", "Germany"), index=0)
+    gender = st.radio("Gender", ("Female", "Male"), index=0)
+    has_cr_card = st.radio("Has Credit Card?", ("Yes", "No"), index=0)
+    is_active_member = st.radio("Active Member?", ("Yes", "No"), index=0)
+    
+    credit_score = st.number_input("Credit Score", min_value=300, max_value=900, value=619)
+    age = st.number_input("Age", min_value=18, max_value=100, value=42)
+    tenure = st.number_input("Tenure (Years)", min_value=0, max_value=10, value=2)
+    balance = st.number_input("Balance", value=0.00, format="%.2f")
+    num_of_products = st.number_input("Number of Products", min_value=1, max_value=4, value=1)
+    estimated_salary = st.number_input("Estimated Salary", value=101348.88, format="%.2f")
+
+    predict_button = st.button("Calculate Risk", type="primary")
+
+with col2:
+    st.header("Prediction Result")
+    st.caption("Model's churn probability prediction.")
+
+    if predict_button:
+        if model is None or scaler is None:
+            st.error("Models could not be loaded. Please contact administrator.")
+        else:
+            try:
+                # --- Step 1: Collect All Raw Inputs in a Dictionary ---
+                raw_data_dict = {
+                    'CreditScore': credit_score,
+                    'Age': age,
+                    'Tenure': tenure,
+                    'Balance': balance,
+                    'NumOfProducts': float(num_of_products),
+                    'HasCrCard': 1.0 if has_cr_card == "Yes" else 0.0,
+                    'IsActiveMember': 1.0 if is_active_member == "Yes" else 0.0,
+                    'EstimatedSalary': estimated_salary,
+                    'Geography_Germany': 1.0 if geography == "Germany" else 0.0,
+                    'Geography_Spain': 1.0 if geography == "Spain" else 0.0,
+                    'Gender_Male': 1.0 if gender == "Male" else 0.0
+                }
+                
+                # --- Step 2: Order Data According to Model's Expected Order ---
+                raw_input_features = [raw_data_dict[col] for col in MODEL_INPUT_COLUMNS_ORDER]
+                raw_input_array = np.array(raw_input_features).reshape(1, -1)
+                
+                # --- Step 3: Scaling ---
+                scaled_input_array = scaler.transform(raw_input_array)
+                
+                # --- Step 4: Make Prediction ---
+                with st.spinner("Model running, making prediction..."):
+                    prediction_proba = model.predict(scaled_input_array)[0][0]
+                    churn_probability_percent = prediction_proba * 100
+                    threshold = 50.0
+
+                    # --- Step 5: Display Result ---
+                    if churn_probability_percent > threshold:
+                        st.error(f"Customer Churn Probability: {churn_probability_percent:.2f}%")
+                        st.warning("This customer has HIGH churn risk. 🚨")
+                    else:
+                        st.success(f"Customer Churn Probability: {churn_probability_percent:.2f}%")
+                        st.info("This customer has LOW churn risk. ✅")
+                
+                with st.expander("Processed (Scaled) Data Seen by Model"):
+                    scaled_features_list = scaled_input_array.flatten().tolist()
+                    st.json({col: val for col, val in zip(MODEL_INPUT_COLUMNS_ORDER, scaled_features_list)})
+                
+                with st.expander("Raw Data Entered to Scaler (for verification)"):
+                    st.json({col: val for col, val in zip(MODEL_INPUT_COLUMNS_ORDER, raw_input_features)})
+
+            except Exception as e:
+                st.error(f"An error occurred during prediction: {e}")

model_churn.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:697b0bf586f6fd52eb0190ae6da379661318005e0cd7ff33a4e0efb02116f297
+size 186234

requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+streamlit
+tensorflow
+scikit-learn
+numpy