Upload 4 files

app.py

@@ -0,0 +1,95 @@
+import pandas as pd
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler
+import streamlit as st
+import joblib
+import os
+
+# Load dataset
+df = pd.read_csv('heart.csv')
+
+# Load trained model safely
+model_path = 'classification_model.pkl'
+if os.path.exists(model_path):
+    model = joblib.load(model_path)
+else:
+    st.error("Error: Model file not found. Please check 'classification_model.pkl'.")
+    model = None
+
+# Validate model
+if model is None:
+    raise ValueError("The loaded model is None. Check the file path or format.")
+
+# Define preprocessing
+preprocessor = ColumnTransformer(
+    transformers=[ 
+        ('num', StandardScaler(), ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
+                                   'exang', 'oldpeak', 'slope', 'ca', 'thal'])
+    ]
+)
+
+# Create a pipeline with the classifier
+pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', model)])
+
+# Fit the pipeline only if the model is valid
+X = df[['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']]
+y = df['target']
+try:
+    pipeline.fit(X, y)
+except Exception as e:
+    st.error(f"Pipeline fitting error: {e}")
+
+# Prediction function
+def predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
+    input_data = pd.DataFrame({
+        'age': [age], 'sex': [sex], 'cp': [cp], 'trestbps': [trestbps], 'chol': [chol],
+        'fbs': [fbs], 'restecg': [restecg], 'thalach': [thalach], 'exang': [exang],
+        'oldpeak': [oldpeak], 'slope': [slope], 'ca': [ca], 'thal': [thal]
+    })
+    prediction = pipeline.predict(input_data)[0]
+    return "\U0001F534 High Risk: Heart Disease Detected" if prediction == 1 else "\U0001F7E2 Low Risk: No Heart Disease"
+
+# Streamlit App
+def main():
+    st.markdown("""
+        <h1 style='text-align: center; color: #FF4B4B;'>❤️ Heart Disease Prediction ❤️</h1>
+        <p style='text-align: center;'>Enter your health details in the sidebar to check your risk level.</p>
+    """, unsafe_allow_html=True)
+
+    # Sidebar for user inputs
+    st.sidebar.header("🔍 Enter Your Health Details")
+    age = st.sidebar.number_input('Age', min_value=0, max_value=120, step=1)
+    sex = st.sidebar.selectbox('Sex', ["Female (0)", "Male (1)"])
+    cp = st.sidebar.selectbox('Chest Pain Type', ["0 - Typical Angina", "1 - Atypical Angina", "2 - Non-Anginal", "3 - Asymptomatic"])
+    trestbps = st.sidebar.number_input('Resting Blood Pressure (mm Hg)', min_value=80, max_value=200, step=1)
+    chol = st.sidebar.number_input('Serum Cholesterol (mg/dl)', min_value=100, max_value=600, step=1)
+    fbs = st.sidebar.selectbox('Fasting Blood Sugar > 120 mg/dl', ["No (0)", "Yes (1)"])
+    restecg = st.sidebar.selectbox('Resting ECG', ["0 - Normal", "1 - ST-T Wave Abnormality", "2 - LV Hypertrophy"])
+    thalach = st.sidebar.number_input('Max Heart Rate Achieved', min_value=60, max_value=220, step=1)
+    exang = st.sidebar.selectbox('Exercise-Induced Angina', ["No (0)", "Yes (1)"])
+    oldpeak = st.sidebar.number_input('ST Depression Induced by Exercise', min_value=0.0, max_value=6.2, step=0.1)
+    slope = st.sidebar.selectbox('Slope of Peak Exercise ST', ["0 - Upsloping", "1 - Flat", "2 - Downsloping"])
+    ca = st.sidebar.selectbox('Major Vessels Colored by Fluoroscopy', [0, 1, 2, 3, 4])
+    thal = st.sidebar.selectbox('Thalassemia', ["1 - Normal", "2 - Fixed defect", "3 - Reversible defect"])
+    
+    # Convert categorical inputs to numeric
+    sex = 1 if sex == "Male (1)" else 0
+    cp = int(cp[0])
+    fbs = 1 if fbs == "Yes (1)" else 0
+    restecg = int(restecg[0])
+    exang = 1 if exang == "Yes (1)" else 0
+    slope = int(slope[0])
+    thal = int(thal[0])
+
+    if st.sidebar.button('🔎 Predict'):
+        prediction = predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal)
+        st.markdown(f"""
+            <div style='text-align: center; font-size: 24px; font-weight: bold; padding: 15px; 
+                border-radius: 10px; color: white; background-color: {'#FF4B4B' if 'High' in prediction else '#4CAF50'};'>
+                {prediction}
+            </div>
+        """, unsafe_allow_html=True)
+
+if __name__ == '__main__':
+    main()

classification_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a883734595d1f337c03b79b151dfdfcc5ad2d2919a0fe6888fd3466aa16f9256
+size 59654

heart.csv

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requirements.txt

@@ -0,0 +1,3 @@
+scikit-learn
+streamlit
+joblib