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@@ -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
+assets/banner.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,328 @@
+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()

assets/UciClevelandHeartDisease.csv

@@ -0,0 +1,304 @@
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assets/test_data.csv

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+age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal
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models/uci_heart_disease_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:50ed19a94320cb3c8425310a7bf884281c148fd586cf10f3bf0fb8d6025a5021
+size 443747

models/uci_heart_disease_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8080a241474239ba64182d50ca9024547c00a4baca440c491e9ea224090db534
+size 442735

requirements.txt

@@ -0,0 +1,9 @@
+pandas==2.0.3
+numpy==1.24.3
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.3.2
+matplotlib==3.7.2
+seaborn==0.12.2
+streamlit==1.44.1
+

test_model.py

@@ -0,0 +1,48 @@
+import pandas as pd
+import joblib
+
+def test_heart_disease_model(test_data):
+    """
+    Function to test the trained heart disease prediction model.
+    Loads the model and makes predictions on a sample dataset.
+    """
+    try:
+        # model loading
+        production_model = joblib.load('models/uci_heart_disease_model.pkl')
+        model = production_model['model']
+        optimal_threshold = production_model['metadata']['threshold']
+
+
+
+        # engineered features to match training data
+        test_data['hr_age_ratio'] = test_data['thalach'] / (test_data['age'] + 1e-5)
+        test_data['bp_oldpeak'] = test_data['trestbps'] * (test_data['oldpeak'] + 1)
+        test_data['risk_score'] = (test_data['age']/50 + test_data['chol']/200 + test_data['trestbps']/140)
+
+        # Make predictions
+        probabilities = model.predict_proba(test_data)[:, 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,
+        })
+
+        # data for display
+        display_data = pd.concat([test_data[['age', 'sex', 'cp', 'trestbps', 'chol']], results], axis=1)
+
+        print("=== Heart Disease Prediction Results ===")
+        print(f"Using threshold: {optimal_threshold:.3f}\n")
+        print(display_data.to_string(index=False))
+
+        return results
+
+    except Exception as e:
+        print(f"Error testing model: {str(e)}")
+        return None
+
+
+# pf =pd.read_csv('dataset/test_data.csv')
+# test_results = test_heart_disease_model(pf)