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import gradio as gr
import numpy as np
from sklearn.preprocessing import StandardScaler
import joblib
# Load the trained model
model = joblib.load('model.pkl')
# Define the function to preprocess user inputs
def preprocess_input(age, sex, cp_type, resting_bp, cholesterol, fasting_bs, ecg, max_hr, exercise_angina, oldpeak, st_slope):
# Convert categorical features to one-hot encoding
sex_mapping = {'F': [0, 1], 'M': [1, 0]}
sex_encoded = sex_mapping.get(sex, [0, 0])
cp_mapping = {'ASY': [1, 0, 0, 0], 'ATA': [0, 1, 0, 0], 'NAP': [0, 0, 1, 0], 'TA': [0, 0, 0, 1]}
cp_encoded = cp_mapping.get(cp_type, [0, 0, 0, 0])
ecg_mapping = {'Normal': [1, 0, 0], 'ST': [0, 1, 0], 'LVH': [0, 0, 1]}
ecg_encoded = ecg_mapping.get(ecg, [0, 0, 0])
# Normalize numerical features
numerical_features = [age, resting_bp, cholesterol, max_hr, oldpeak]
numerical_features = np.array(numerical_features).reshape(1, -1)
scaler = StandardScaler()
numerical_features_scaled = scaler.fit_transform(numerical_features)
# Combine all features
processed_input = np.concatenate([numerical_features_scaled, [fasting_bs], sex_encoded, cp_encoded, ecg_encoded, [exercise_angina], [st_slope]], axis=1)
return processed_input
# Define the function to make predictions
def predict_heart_disease(age, sex, cp_type, resting_bp, cholesterol, fasting_bs, ecg, max_hr, exercise_angina, oldpeak, st_slope):
processed_input = preprocess_input(age, sex, cp_type, resting_bp, cholesterol, fasting_bs, ecg, max_hr, exercise_angina, oldpeak, st_slope)
prediction = model.predict(processed_input)[0]
return "Heart Disease Likely" if prediction == 1 else "No Heart Disease"
# Create Gradio interface
inputs = [
gr.Interface.Slider(0, 100, default=50, label="Age"),
gr.Interface.Radio(['F', 'M'], label="Sex"),
gr.Interface.Dropdown(['TA', 'ATA', 'NAP', 'ASY'], label="Chest Pain Type"),
gr.Interface.Slider(80, 200, default=120, label="Resting Blood Pressure"),
gr.Interface.Slider(100, 600, default=200, label="Cholesterol"),
gr.Interface.Checkbox(label="Fasting Blood Sugar > 120 mg/dl"),
gr.Interface.Dropdown(['Normal', 'ST', 'LVH'], label="Resting ECG"),
gr.Interface.Slider(60, 202, default=100, label="Maximum Heart Rate Achieved"),
gr.Interface.Radio(['Y', 'N'], label="Exercise-induced Angina"),
gr.Interface.Slider(0, 10, default=5, label="Oldpeak"),
gr.Interface.Dropdown(['Up', 'Flat', 'Down'], label="ST Slope")
]
output = gr.outputs.Textbox(label="Heart Disease Prediction")
gr.Interface(predict_heart_disease, inputs, output, title="Heart Disease Prediction", description="Enter patient information to predict likelihood of heart disease.").launch()
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