Update app.py

app.py

@@ -1,328 +1,327 @@
-import streamlit as st
-import joblib
-import pandas as pd
-
-
-# Page config must be first command
-st.set_page_config(
-    page_title="❤️ Heart Disease Prediction System",
-    page_icon="❤️",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-
-# Load the pre-trained model
-@st.cache_resource
-def load_model():
-    try:
-        production_model = joblib.load('models/uci_heart_disease_model.pkl')
-        return production_model['model'], production_model['metadata']['threshold']
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-        st.stop()
-
-
-model, optimal_threshold = load_model()
-
-
-# Function to process input and make predictions
-def predict_heart_disease(user_input):
-    try:
-        # Feature engineering
-        user_input['hr_age_ratio'] = user_input['thalach'] / (user_input['age'] + 1e-5)
-        user_input['bp_oldpeak'] = user_input['trestbps'] * (user_input['oldpeak'] + 1)
-        user_input['risk_score'] = (user_input['age'] / 50 + user_input['chol'] / 200 + user_input['trestbps'] / 140)
-
-        # Make prediction
-        probabilities = model.predict_proba(user_input)[:, 1]
-        predictions = (probabilities >= optimal_threshold).astype(int)
-
-        # Create results DataFrame
-        results = pd.DataFrame({
-            'Prediction': predictions,
-            'Diagnosis': ['Heart Disease' if p == 1 else 'Healthy' for p in predictions],
-            'Probability': probabilities,
-        })
-
-        # Combine with input features for display
-        display_data = pd.concat([user_input[['age', 'sex', 'cp', 'trestbps', 'chol']], results], axis=1)
-
-        return results, display_data
-
-    except Exception as e:
-        st.error(f"Prediction error: {e}")
-        return None, None
-
-
-# Main app interface
-st.title("❤️ Heart Disease Prediction")
-
-# Create tabs
-tab1, tab2 ,tab3= st.tabs(["Single Prediction", "Batch Prediction","Data & Model Info"])
-
-with tab1:
-    st.header("Single Patient Prediction")
-
-    # Input form
-    with st.form("prediction_form"):
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.subheader("Patient Information")
-            age = st.slider("Age", 18, 100, 50)
-            sex = st.radio("Sex", ["Male (1)", "Female (0)"], index=0)
-            cp = st.selectbox("Chest Pain Type",
-                              ["Typical angina (1)", "Atypical angina (2)",
-                               "Non-anginal pain (3)", "Asymptomatic (4)"])
-            trestbps = st.slider("Resting Blood Pressure (mmHg)", 90, 200, 120)
-            chol = st.slider("Serum Cholesterol (mg/dl)", 150, 350, 200)
-
-        with col2:
-            st.subheader("Clinical Measurements")
-            fbs = st.radio("Fasting Blood Sugar > 120 mg/dl", ["Yes (1)", "No (0)"], index=1)
-            restecg = st.selectbox("Resting ECG Results",
-                                   ["Normal (0)", "ST-T wave abnormality (1)",
-                                    "Left ventricular hypertrophy (2)"])
-            thalach = st.slider("Maximum Heart Rate Achieved (bpm)", 60, 200, 150)
-            exang = st.radio("Exercise Induced Angina", ["Yes (1)", "No (0)"], index=1)
-            oldpeak = st.slider("ST Depression Induced by Exercise", 0.0, 6.0, 1.0, step=0.1)
-            slope = st.selectbox("Slope of Peak Exercise ST Segment",
-                                 ["Upsloping (1)", "Flat (2)", "Downsloping (3)"])
-            ca = st.slider("Number of Major Vessels", 0, 4, 0)
-            thal = st.selectbox("Thalassemia",
-                                ["Normal (3)", "Fixed defect (6)", "Reversible defect (7)"])
-
-        submitted = st.form_submit_button("Predict Heart Disease Risk")
-
-    if submitted:
-        # Preprocess inputs
-        user_input = pd.DataFrame({
-            'age': [age],
-            'sex': [1 if sex.startswith("Male") else 0],
-            'cp': [int(cp.split("(")[1].strip(")"))],
-            'trestbps': [trestbps],
-            'chol': [chol],
-            'fbs': [1 if fbs.startswith("Yes") else 0],
-            'restecg': [int(restecg.split("(")[1].strip(")"))],
-            'thalach': [thalach],
-            'exang': [1 if exang.startswith("Yes") else 0],
-            'oldpeak': [oldpeak],
-            'slope': [int(slope.split("(")[1].strip(")"))],
-            'ca': [ca],
-            'thal': [int(thal.split("(")[1].strip(")"))],
-        })
-
-        # Get predictions
-        results, display_data = predict_heart_disease(user_input)
-
-        if results is not None:
-            st.subheader("Prediction Results")
-
-            # Display the formatted results
-            st.markdown(f"""
-            ### Heart Disease Prediction Results
-            **Using threshold:** {optimal_threshold:.3f}
-            """)
-
-            # Show detailed results in expandable section
-            with st.expander("View Detailed Results"):
-                st.dataframe(display_data)
-
-            # Show risk assessment
-            probability = results['Probability'].iloc[0]
-            prediction = results['Diagnosis'].iloc[0]
-
-            if probability > 0.7:
-                risk_level = "High"
-                recommendation = "Immediate consultation with cardiologist recommended"
-                color = "red"
-            elif probability > 0.4:
-                risk_level = "Medium"
-                recommendation = "Further tests recommended"
-                color = "orange"
-            else:
-                risk_level = "Low"
-                recommendation = "No immediate concerns, maintain regular checkups"
-                color = "green"
-
-            # Display metrics in columns
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                st.metric("Prediction", prediction)
-            with col2:
-                st.metric("Probability", f"{probability * 100:.2f}%")
-            with col3:
-                st.metric("Risk Level", risk_level)
-
-            # Show recommendation
-            st.markdown(f"""
-            <div style='background-color:#f0f2f6; padding:10px; border-radius:5px;'>
-                <h4 style='color:{color};'>Recommendation: {recommendation}</h4>
-            </div>
-            """, unsafe_allow_html=True)
-
-with tab2:
-    st.header("Batch Prediction")
-
-    uploaded_file = st.file_uploader("Upload CSV file with patient data", type=["csv"])
-
-    if uploaded_file is not None:
-        try:
-            test_data = pd.read_csv(uploaded_file)
-            st.success("File uploaded successfully!")
-
-            # Check for required columns
-            required_cols = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg',
-                             'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
-
-            missing_cols = [col for col in required_cols if col not in test_data.columns]
-            if missing_cols:
-                st.error(f"Missing required columns: {', '.join(missing_cols)}")
-            else:
-                # Get predictions
-                results, display_data = predict_heart_disease(test_data)
-
-                if results is not None:
-                    st.subheader("Prediction Results")
-
-                    # Show summary statistics
-                    st.markdown(f"""
-                    ### Batch Prediction Results
-                    **Using threshold:** {optimal_threshold:.3f}
-                    """)
-
-                    # Combine results with original data
-                    full_results = test_data.copy()
-                    full_results['Probability'] = results['Probability']
-                    full_results['Prediction'] = results['Prediction']
-                    full_results['Diagnosis'] = results['Diagnosis']
-
-                    # Show results in expandable section
-                    with st.expander("View All Predictions"):
-                        st.dataframe(full_results)
-
-                    # Show statistics
-                    st.subheader("Statistics")
-                    col1, col2, col3 = st.columns(3)
-                    with col1:
-                        st.metric("Total Patients", len(full_results))
-                    with col2:
-                        st.metric("Heart Disease Cases", full_results['Prediction'].sum())
-                    with col3:
-                        st.metric("Healthy Cases", len(full_results) - full_results['Prediction'].sum())
-
-                    # Add download button
-                    csv = full_results.to_csv(index=False)
-                    st.download_button(
-                        "Download Results",
-                        csv,
-                        "heart_disease_predictions.csv",
-                        "text/csv"
-                    )
-
-        except Exception as e:
-            st.error(f"Error processing file: {e}")
-
-sample_data = pd.DataFrame({
-    'age': [52, 63, 45, 67, 58],
-    'sex': [1, 1, 0, 0, 1],
-    'cp': [3, 4, 2, 3, 4],
-    'trestbps': [125, 145, 130, 120, 136],
-    'chol': [212, 233, 204, 228, 319],
-    'fbs': [0, 1, 0, 0, 0],
-    'restecg': [0, 1, 0, 1, 0],
-    'thalach': [168, 150, 172, 129, 152],
-    'exang': [0, 0, 0, 1, 0],
-    'oldpeak': [1.0, 2.3, 1.4, 2.6, 0.0],
-    'slope': [2, 3, 1, 2, 1],
-    'ca': [2, 0, 0, 1, 0],
-    'thal': [3, 3, 3, 7, 3]
-})
-
-with tab3:
-    st.header("Data & Model Information")
-
-    st.subheader("Dataset Information")
-    st.markdown("""
-    The model was trained on the UCI Heart Disease Dataset containing the following features:
-    - **Demographic**: Age, Sex
-    - **Clinical**: Blood Pressure, Cholesterol, etc.
-    - **Electrocardiographic**: Resting ECG, Exercise ST segment, etc.
-    """)
-
-    st.subheader("Sample Data")
-    st.dataframe(sample_data)
-
-    st.subheader("Model Performance")
-    st.markdown("""
-    - **Accuracy**: 85.2% (on test set)
-    - **Precision**: 83.1%
-    - **Recall**: 87.5%
-    - **F1-score**: 85.2%
-    
-    **📈 Additional Metrics:**
-    - **ROC AUC:** `0.909`
-    - **Sensitivity (Recall):** `0.95` _(for Heart Disease)_
-    - **Specificity:** `0.76` _(for Healthy)_
-    - **Balanced Accuracy:** `0.855`
-    - **False Positive Rate (FPR):** `0.24`
-    - **False Negative Rate (FNR):** `0.05`
-    - **Precision (Heart Disease):** `0.80`
-    - **Precision (Healthy):** `0.95`
-    - **F1 Score (Overall):** `0.85`
-    - **Support Size:** `46` patients
-    """)
-
-    st.subheader("Risk Interpretation Guide")
-    st.markdown("""
-    - **High Risk (>70%)**: Strong recommendation for cardiologist consultation
-    - **Medium Risk (40-70%)**: Suggest additional tests
-    - **Low Risk (<40%)**: Likely healthy, maintain regular checkups
-    """)
-
-    st.subheader("Terms of Use")
-    st.markdown("""
-    This tool is for informational purposes only and should not replace 
-    professional medical advice. Always consult a healthcare provider 
-    for medical diagnosis and treatment.
-    """)
-
-# Sidebar with info
-with st.sidebar:
-    st.title("❤️ Heart Disease Prediction")
-    st.markdown("""
-    ## About This App
-    This application predicts the likelihood of heart disease based on clinical features using a machine learning model.
-
-    ### Model Information
-    - **Algorithm**: Random Forest Classifier
-    - **Dataset**: UCI Heart Disease Dataset
-    - **Optimal Threshold**: {:.3f}
-    - **Version**: 1.1
-
-    ### How It Works
-    1. Enter patient details
-    2. Click 'Predict' button
-    3. View prediction results
-    """.format(optimal_threshold))
-
-    st.markdown("---")
-    st.markdown("""
-    ### Feature Descriptions
-    - **Age**: Patient's age in years
-    - **Sex**: Gender (1 = Male, 0 = Female)
-    - **CP**: Chest pain type (1-4)
-    - **Trestbps**: Resting blood pressure (mmHg)
-    - **Chol**: Serum cholesterol (mg/dl)
-    - **FBS**: Fasting blood sugar > 120 mg/dl
-    - **Restecg**: Resting ECG results
-    - **Thalach**: Maximum heart rate achieved
-    - **Exang**: Exercise induced angina
-    - **Oldpeak**: ST depression induced by exercise
-    - **Slope**: Slope of peak exercise ST segment
-    - **CA**: Number of major vessels colored by fluoroscopy
-    - **Thal**: Thalassemia (3,6,7)
-    """)
-if __name__ == "__main__":
-    st.run()
+import streamlit as st
+import joblib
+import pandas as pd
+
+
+# Page config 
+st.set_page_config(
+    page_title="❤️ Heart Disease Prediction System",
+    page_icon="❤️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+
+# trained model
+@st.cache_resource
+def load_model():
+    try:
+        production_model = joblib.load('models/uci_heart_disease_model.pkl')
+        return production_model['model'], production_model['metadata']['threshold']
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        st.stop()
+
+
+model, optimal_threshold = load_model()
+
+
+
+def predict_heart_disease(user_input):
+    try:
+        # Feature engineering
+        user_input['hr_age_ratio'] = user_input['thalach'] / (user_input['age'] + 1e-5)
+        user_input['bp_oldpeak'] = user_input['trestbps'] * (user_input['oldpeak'] + 1)
+        user_input['risk_score'] = (user_input['age'] / 50 + user_input['chol'] / 200 + user_input['trestbps'] / 140)
+
+        #prediction
+        probabilities = model.predict_proba(user_input)[:, 1]
+        predictions = (probabilities >= optimal_threshold).astype(int)
+
+        # results DataFrame
+        results = pd.DataFrame({
+            'Prediction': predictions,
+            'Diagnosis': ['Heart Disease' if p == 1 else 'Healthy' for p in predictions],
+            'Probability': probabilities,
+        })
+
+        # input features for display
+        display_data = pd.concat([user_input[['age', 'sex', 'cp', 'trestbps', 'chol']], results], axis=1)
+
+        return results, display_data
+
+    except Exception as e:
+        st.error(f"Prediction error: {e}")
+        return None, None
+
+
+# Main app interface
+st.title("❤️ Heart Disease Prediction")
+
+# tabs
+tab1, tab2 ,tab3= st.tabs(["Single Prediction", "Batch Prediction","Data & Model Info"])
+
+with tab1:
+    st.header("Single Patient Prediction")
+
+    # Input form
+    with st.form("prediction_form"):
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.subheader("Patient Information")
+            age = st.slider("Age", 18, 100, 50)
+            sex = st.radio("Sex", ["Male (1)", "Female (0)"], index=0)
+            cp = st.selectbox("Chest Pain Type",
+                              ["Typical angina (1)", "Atypical angina (2)",
+                               "Non-anginal pain (3)", "Asymptomatic (4)"])
+            trestbps = st.slider("Resting Blood Pressure (mmHg)", 90, 200, 120)
+            chol = st.slider("Serum Cholesterol (mg/dl)", 150, 350, 200)
+
+        with col2:
+            st.subheader("Clinical Measurements")
+            fbs = st.radio("Fasting Blood Sugar > 120 mg/dl", ["Yes (1)", "No (0)"], index=1)
+            restecg = st.selectbox("Resting ECG Results",
+                                   ["Normal (0)", "ST-T wave abnormality (1)",
+                                    "Left ventricular hypertrophy (2)"])
+            thalach = st.slider("Maximum Heart Rate Achieved (bpm)", 60, 200, 150)
+            exang = st.radio("Exercise Induced Angina", ["Yes (1)", "No (0)"], index=1)
+            oldpeak = st.slider("ST Depression Induced by Exercise", 0.0, 6.0, 1.0, step=0.1)
+            slope = st.selectbox("Slope of Peak Exercise ST Segment",
+                                 ["Upsloping (1)", "Flat (2)", "Downsloping (3)"])
+            ca = st.slider("Number of Major Vessels", 0, 4, 0)
+            thal = st.selectbox("Thalassemia",
+                                ["Normal (3)", "Fixed defect (6)", "Reversible defect (7)"])
+
+        submitted = st.form_submit_button("Predict Heart Disease Risk")
+
+    if submitted:
+        # Preprocess inputs
+        user_input = pd.DataFrame({
+            'age': [age],
+            'sex': [1 if sex.startswith("Male") else 0],
+            'cp': [int(cp.split("(")[1].strip(")"))],
+            'trestbps': [trestbps],
+            'chol': [chol],
+            'fbs': [1 if fbs.startswith("Yes") else 0],
+            'restecg': [int(restecg.split("(")[1].strip(")"))],
+            'thalach': [thalach],
+            'exang': [1 if exang.startswith("Yes") else 0],
+            'oldpeak': [oldpeak],
+            'slope': [int(slope.split("(")[1].strip(")"))],
+            'ca': [ca],
+            'thal': [int(thal.split("(")[1].strip(")"))],
+        })
+
+        # predictions
+        results, display_data = predict_heart_disease(user_input)
+
+        if results is not None:
+            st.subheader("Prediction Results")
+
+            # formatted results
+            st.markdown(f"""
+            ### Heart Disease Prediction Results
+            **Using threshold:** {optimal_threshold:.3f}
+            """)
+
+            # results section
+            with st.expander("View Detailed Results"):
+                st.dataframe(display_data)
+
+            # risk assessment
+            probability = results['Probability'].iloc[0]
+            prediction = results['Diagnosis'].iloc[0]
+
+            if probability > 0.7:
+                risk_level = "High"
+                recommendation = "Immediate consultation with cardiologist recommended"
+                color = "red"
+            elif probability > 0.4:
+                risk_level = "Medium"
+                recommendation = "Further tests recommended"
+                color = "orange"
+            else:
+                risk_level = "Low"
+                recommendation = "No immediate concerns, maintain regular checkups"
+                color = "green"
+
+            
+            col1, col2, col3 = st.columns(3)
+            with col1:
+                st.metric("Prediction", prediction)
+            with col2:
+                st.metric("Probability", f"{probability * 100:.2f}%")
+            with col3:
+                st.metric("Risk Level", risk_level)
+
+            # recommendation
+            st.markdown(f"""
+            <div style='background-color:#f0f2f6; padding:10px; border-radius:5px;'>
+                <h4 style='color:{color};'>Recommendation: {recommendation}</h4>
+            </div>
+            """, unsafe_allow_html=True)
+
+with tab2:
+    st.header("Batch Prediction")
+
+    uploaded_file = st.file_uploader("Upload CSV file with patient data", type=["csv"])
+
+    if uploaded_file is not None:
+        try:
+            test_data = pd.read_csv(uploaded_file)
+            st.success("File uploaded successfully!")
+
+            # required columns
+            required_cols = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg',
+                             'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
+
+            missing_cols = [col for col in required_cols if col not in test_data.columns]
+            if missing_cols:
+                st.error(f"Missing required columns: {', '.join(missing_cols)}")
+            else:
+                #  predictions
+                results, display_data = predict_heart_disease(test_data)
+
+                if results is not None:
+                    st.subheader("Prediction Results")
+
+                    # summary statistics
+                    st.markdown(f"""
+                    ### Batch Prediction Results
+                    **Using threshold:** {optimal_threshold:.3f}
+                    """)
+
+                    # results with original data
+                    full_results = test_data.copy()
+                    full_results['Probability'] = results['Probability']
+                    full_results['Prediction'] = results['Prediction']
+                    full_results['Diagnosis'] = results['Diagnosis']
+
+                    # results section
+                    with st.expander("View All Predictions"):
+                        st.dataframe(full_results)
+
+                    # statistics
+                    st.subheader("Statistics")
+                    col1, col2, col3 = st.columns(3)
+                    with col1:
+                        st.metric("Total Patients", len(full_results))
+                    with col2:
+                        st.metric("Heart Disease Cases", full_results['Prediction'].sum())
+                    with col3:
+                        st.metric("Healthy Cases", len(full_results) - full_results['Prediction'].sum())
+
+                    #download button
+                    csv = full_results.to_csv(index=False)
+                    st.download_button(
+                        "Download Results",
+                        csv,
+                        "heart_disease_predictions.csv",
+                        "text/csv"
+                    )
+
+        except Exception as e:
+            st.error(f"Error processing file: {e}")
+
+sample_data = pd.DataFrame({
+    'age': [52, 63, 45, 67, 58],
+    'sex': [1, 1, 0, 0, 1],
+    'cp': [3, 4, 2, 3, 4],
+    'trestbps': [125, 145, 130, 120, 136],
+    'chol': [212, 233, 204, 228, 319],
+    'fbs': [0, 1, 0, 0, 0],
+    'restecg': [0, 1, 0, 1, 0],
+    'thalach': [168, 150, 172, 129, 152],
+    'exang': [0, 0, 0, 1, 0],
+    'oldpeak': [1.0, 2.3, 1.4, 2.6, 0.0],
+    'slope': [2, 3, 1, 2, 1],
+    'ca': [2, 0, 0, 1, 0],
+    'thal': [3, 3, 3, 7, 3]
+})
+
+with tab3:
+    st.header("Data & Model Information")
+
+    st.subheader("Dataset Information")
+    st.markdown("""
+    The model was trained on the UCI Heart Disease Dataset containing the following features:
+    - **Demographic**: Age, Sex
+    - **Clinical**: Blood Pressure, Cholesterol, etc.
+    - **Electrocardiographic**: Resting ECG, Exercise ST segment, etc.
+    """)
+
+    st.subheader("Sample Data")
+    st.dataframe(sample_data)
+
+    st.subheader("Model Performance")
+    st.markdown("""
+    - **Accuracy**: 85.2% (on test set)
+    - **Precision**: 83.1%
+    - **Recall**: 87.5%
+    - **F1-score**: 85.2%
+    
+    **📈 Additional Metrics:**
+    - **ROC AUC:** `0.909`
+    - **Sensitivity (Recall):** `0.95` _(for Heart Disease)_
+    - **Specificity:** `0.76` _(for Healthy)_
+    - **Balanced Accuracy:** `0.855`
+    - **False Positive Rate (FPR):** `0.24`
+    - **False Negative Rate (FNR):** `0.05`
+    - **Precision (Heart Disease):** `0.80`
+    - **Precision (Healthy):** `0.95`
+    - **F1 Score (Overall):** `0.85`
+    - **Support Size:** `46` patients
+    """)
+
+    st.subheader("Risk Interpretation Guide")
+    st.markdown("""
+    - **High Risk (>70%)**: Strong recommendation for cardiologist consultation
+    - **Medium Risk (40-70%)**: Suggest additional tests
+    - **Low Risk (<40%)**: Likely healthy, maintain regular checkups
+    """)
+
+    st.subheader("Terms of Use")
+    st.markdown("""
+    This tool is for informational purposes only and should not replace 
+    professional medical advice. Always consult a healthcare provider 
+    for medical diagnosis and treatment.
+    """)
+
+# Sidebar with info
+with st.sidebar:
+    st.title("❤️ Heart Disease Prediction")
+    st.markdown("""
+    ## About This App
+    This application predicts the likelihood of heart disease based on clinical features using a machine learning model.
+
+    ### Model Information
+    - **Algorithm**: Random Forest Classifier
+    - **Dataset**: UCI Heart Disease Dataset
+    - **Optimal Threshold**: {:.3f}
+    - **Version**: 1.1
+
+    ### How It Works
+    1. Enter patient details
+    2. Click 'Predict' button
+    3. View prediction results
+    """.format(optimal_threshold))
+
+    st.markdown("---")
+    st.markdown("""
+    ### Feature Descriptions
+    - **Age**: Patient's age in years
+    - **Sex**: Gender (1 = Male, 0 = Female)
+    - **CP**: Chest pain type (1-4)
+    - **Trestbps**: Resting blood pressure (mmHg)
+    - **Chol**: Serum cholesterol (mg/dl)
+    - **FBS**: Fasting blood sugar > 120 mg/dl
+    - **Restecg**: Resting ECG results
+    - **Thalach**: Maximum heart rate achieved
+    - **Exang**: Exercise induced angina
+    - **Oldpeak**: ST depression induced by exercise
+    - **Slope**: Slope of peak exercise ST segment
+    - **CA**: Number of major vessels colored by fluoroscopy
+    - **Thal**: Thalassemia (3,6,7)
+    """)
+