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4a54a97 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | 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()
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