Update src/streamlit_app.py

src/streamlit_app.py

@@ -10,121 +10,97 @@ from sklearn.metrics import classification_report, accuracy_score
 import streamlit_authenticator as stauth
 
 
+MODEL_FILENAME = "/tmp/insurance_churn_model.pkl"
+st.title("Insurance Churn Prediction App")
 
-# --- Hardcoded credentials ---
-USER_CREDENTIALS = {
-    "admin": "admin123",
-    "akshay": "user123"
-}
-
-# --- Login Panel ---
-st.set_page_config(page_title="Login to App", layout="centered")
-st.markdown("<h2 style='text-align: center;'>🔐 Login Panel</h2>", unsafe_allow_html=True)
-
-with st.form("login_form", clear_on_submit=False):
-    username = st.text_input("Username")
-    password = st.text_input("Password", type="password")
-    login_btn = st.form_submit_button("Login")
-
-if login_btn:
-    if username in USER_CREDENTIALS and USER_CREDENTIALS[username] == password:
-        
-        st.success(f"✅ Welcome, {username}!")
-        st.write("🎉 You are now logged in. Your app content goes here.")
-
-        MODEL_FILENAME = "/tmp/insurance_churn_model.pkl"
-        st.title("Insurance Churn Prediction App")
-        
-        menu = st.sidebar.radio("Navigation", ["Predict Churn","Train Model"])
-        
-        if menu == "Train Model":
-            # st.header("Upload Dataset and Train Model")
-            # uploaded_file = st.file_uploader("Upload Insurance Churn Dataset (CSV)", type=["csv"])
-            # if uploaded_file is not None:
-            data = pd.read_csv("src/insurance.csv")
-            st.subheader("Dataset Preview")
-            st.dataframe(data.head())
-        
-            st.subheader("Summary Statistics")
-            st.write(data.describe())
-        
-            if 'churn' in data.columns:
-                st.subheader("Churn Distribution")
-                fig, ax = plt.subplots()
-                sns.countplot(x='churn', data=data, ax=ax)
-                st.pyplot(fig)
-        
-            st.subheader("Model Training")
-            target_column = st.selectbox("Select Target Column", options=data.columns, index=data.columns.get_loc('churn') if 'churn' in data.columns else 0)
-            feature_columns = st.multiselect("Select Feature Columns", options=[col for col in data.columns if col != target_column])
-        
-            #if feature_columns and target_column:
-                # X = pd.get_dummies(data[feature_columns])
-                # y = data[target_column]
-        
-            # input features
-            # Automatically exclude identifier columns
-            # exclude_columns = ['Customer', 'Policy', 'Policy Number', 'Response']
-            # feature_columns = [col for col in data.columns if col not in exclude_columns]
-            # target_column = 'Response'
-        
-            # feature_columns = data.drop(columns=[target_column]).select_dtypes(include=[np.number]).columns.tolist()
-            X= data[feature_columns]
-            y = data[target_column]
-        
-        
-            # X = data.drop('Response', axis = 1)
-            # # output labels
-            # y = data['Response']
-        
-            agree = st.checkbox("Continue Training")
-            if agree:
-                st.write("Great! ML Model Training Started..")
-                X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-        
-                model = RandomForestClassifier()
-                model.fit(X_train, y_train)
-        
-                y_pred = model.predict(X_test)
-                st.subheader("Model Performance")
-                st.write("Accuracy:", accuracy_score(y_test, y_pred))
-                st.text("Classification Report:")
-                st.text(classification_report(y_test, y_pred))
-        
-                joblib.dump((model, X.columns.tolist()), MODEL_FILENAME)
-                st.success(f"Model trained and saved as {MODEL_FILENAME}")
-        
-        elif menu == "Predict Churn":
-            st.header("Insurance Churn Predictor")
-            st.markdown("To use Predictor, Please Train the ML Model if not done yet! ")
-            
-        
-            try:
-                model, feature_names = joblib.load(MODEL_FILENAME)
-                st.success("Model loaded successfully.")
-            except:
-                st.error("Model not found. Please train the model first.")
-                st.stop()
-        
-            st.subheader("Enter Customer Details")
-            input_data = {}
-            for feature in feature_names:
-                input_data[feature] = st.text_input(f"{feature}", "")
-        
-            if st.button("Predict Churn"):
-                try:
-                    input_df = pd.DataFrame([input_data])
-                    input_df = pd.get_dummies(input_df)
-        
-                    for col in feature_names:
-                        if col not in input_df.columns:
-                            input_df[col] = 0
-                    input_df = input_df[feature_names]
-        
-                    prediction = model.predict(input_df)[0]
-                    st.subheader("Prediction Result")
-                    st.write(f"Churn: {'Yes' if prediction == 1 else 'No'}")
-                except Exception as e:
-                    st.error(f"Error in prediction: {e}")
-    else:
-        st.error("❌ Invalid username or password")
+menu = st.sidebar.radio("Navigation", ["Predict Churn","Train Model"])
+
+if menu == "Train Model":
+    # st.header("Upload Dataset and Train Model")
+    # uploaded_file = st.file_uploader("Upload Insurance Churn Dataset (CSV)", type=["csv"])
+    # if uploaded_file is not None:
+    data = pd.read_csv("src/insurance.csv")
+    st.subheader("Dataset Preview")
+    st.dataframe(data.head())
+
+    st.subheader("Summary Statistics")
+    st.write(data.describe())
+
+    if 'churn' in data.columns:
+        st.subheader("Churn Distribution")
+        fig, ax = plt.subplots()
+        sns.countplot(x='churn', data=data, ax=ax)
+        st.pyplot(fig)
+
+    st.subheader("Model Training")
+    target_column = st.selectbox("Select Target Column", options=data.columns, index=data.columns.get_loc('churn') if 'churn' in data.columns else 0)
+    feature_columns = st.multiselect("Select Feature Columns", options=[col for col in data.columns if col != target_column])
+
+    #if feature_columns and target_column:
+        # X = pd.get_dummies(data[feature_columns])
+        # y = data[target_column]
+
+    # input features
+    # Automatically exclude identifier columns
+    # exclude_columns = ['Customer', 'Policy', 'Policy Number', 'Response']
+    # feature_columns = [col for col in data.columns if col not in exclude_columns]
+    # target_column = 'Response'
+
+    # feature_columns = data.drop(columns=[target_column]).select_dtypes(include=[np.number]).columns.tolist()
+    X= data[feature_columns]
+    y = data[target_column]
+
+
+    # X = data.drop('Response', axis = 1)
+    # # output labels
+    # y = data['Response']
+
+    agree = st.checkbox("Continue Training")
+    if agree:
+        st.write("Great! ML Model Training Started..")
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        model = RandomForestClassifier()
+        model.fit(X_train, y_train)
+
+        y_pred = model.predict(X_test)
+        st.subheader("Model Performance")
+        st.write("Accuracy:", accuracy_score(y_test, y_pred))
+        st.text("Classification Report:")
+        st.text(classification_report(y_test, y_pred))
+
+        joblib.dump((model, X.columns.tolist()), MODEL_FILENAME)
+        st.success(f"Model trained and saved as {MODEL_FILENAME}")
+
+elif menu == "Predict Churn":
+    st.header("Insurance Churn Predictor")
+    st.markdown("To use Predictor, Please Train the ML Model if not done yet! ")
+    
+
+    try:
+        model, feature_names = joblib.load(MODEL_FILENAME)
+        st.success("Model loaded successfully.")
+    except:
+        st.error("Model not found. Please train the model first.")
+        st.stop()
+
+    st.subheader("Enter Customer Details")
+    input_data = {}
+    for feature in feature_names:
+        input_data[feature] = st.text_input(f"{feature}", "")
+
+    if st.button("Predict Churn"):
+        try:
+            input_df = pd.DataFrame([input_data])
+            input_df = pd.get_dummies(input_df)
+
+            for col in feature_names:
+                if col not in input_df.columns:
+                    input_df[col] = 0
+            input_df = input_df[feature_names]
+
+            prediction = model.predict(input_df)[0]
+            st.subheader("Prediction Result")
+            st.write(f"Churn: {'Yes' if prediction == 1 else 'No'}")
+        except Exception as e:
+            st.error(f"Error in prediction: {e}")