cfggg/B3L

1import gradio as gr2import tensorflow as tf3import pandas as pd4from sklearn.preprocessing import StandardScaler5from joblib import load6 7scaler = load('scaler.pkl')8 9model = tf.keras.models.load_model('model.h5')10 11columns = ['wiek', 'płeć', 'ból_w_klatce', 'ciśnienie_krwi', 'cholesterol', 12    'cukier_we_krwi', 'wynik_EKG', 'tętno', 'ból_wysiłkowy', 13    'depresja_ST', 'nachylenie_ST', 'naczynia', 'thal', 'choroba_serca']14 15data = pd.read_csv('heart.csv')16 17inputs = [gr.Textbox(label=feature) for feature in data.columns[:-1]]18 19def predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):20    try:21        input_data = pd.DataFrame([[22            age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal23        ]], columns=columns[:-1])24 25        prediction = model.predict(scaler.transform(input_data))26 27        return "Heart Disease Detected" if round(prediction[0][0]) == 1 else "No Heart Disease"28    except Exception as e:29        return f"Error: {e}"30 31 32iface = gr.Interface(33    fn=predict_heart_disease,34    inputs=inputs,35    outputs="text",36    title="HDP-Heart Disease Prediction",37    description="Enter patient details to predict heart disease."38)39 40iface.launch(share=True)