Update src/streamlit_app.py

src/streamlit_app.py

@@ -18,7 +18,7 @@ USER_CREDENTIALS = {
 }
 
 # --- Login Panel ---
-st.set_page_config(page_title="Login Panel", layout="centered")
+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):
@@ -28,104 +28,103 @@ with st.form("login_form", clear_on_submit=False):
 
 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.")
-    else:
-        st.error("❌ Invalid username or password")
-
-
-
-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}")
+        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")