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| import gradio as gr | |
| import pickle | |
| import pandas as pd | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.model_selection import train_test_split | |
| # Load the data | |
| heart = pd.read_csv('heart.dat', header=None, sep=' ', names=['age', 'sex', 'cp', 'trestbps', 'chol', | |
| 'fbs', 'restecg', 'thalach', 'exang', | |
| 'oldpeak', 'slope', 'ca', 'thal', 'heart disease']) | |
| # Load the saved models with error handling | |
| def load_model(filename): | |
| try: | |
| with open(filename, 'rb') as f: | |
| return pickle.load(f) | |
| except Exception as e: | |
| print(f"Error loading {filename}: {e}") | |
| return None | |
| models = { | |
| 'Tree': load_model('Tree.pkl'), | |
| 'SVM': load_model('svm.pkl'), | |
| 'QDA': load_model('QDA.pkl'), | |
| 'MLP': load_model('MLP.pkl'), | |
| 'Log': load_model('Log.pkl'), | |
| 'LDA': load_model('LDA.pkl'), | |
| 'For': load_model('For.pkl') | |
| } | |
| # Define the function to make predictions | |
| def make_prediction(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, model_name): | |
| # Create a pandas DataFrame from the inputs | |
| 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] | |
| }) | |
| # Feature scaling | |
| X = heart.drop('heart disease', axis=1) | |
| y = heart['heart disease'] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y) | |
| scaler = StandardScaler() | |
| X_train_std = scaler.fit_transform(X_train) | |
| # Choose the model and make prediction | |
| model = models.get(model_name) | |
| if model is None: | |
| return "Model not found or failed to load." | |
| input_data_std = scaler.transform(input_data) | |
| probas = model.predict_proba(input_data_std) | |
| return {f"Probability of Class {i+1}": proba for i, proba in enumerate(probas[0])} | |
| # Create the Gradio interface | |
| inputs = [ | |
| gr.Number(label='age'), | |
| gr.Radio(choices=[0,1], label='sex(m=1, f=0)'), | |
| gr.Dropdown(choices=[1,2,3,4], label='chest pain type'), | |
| gr.Number(label='resting blood pressure(NR=120/80)'), | |
| gr.Number(label='serum cholesterol(NR=<200mg/dl)'), | |
| gr.Radio(choices=[0,1], label='fasting blood sugar'), | |
| gr.Radio(choices=[0,1,2], label='resting electrocardiographic'), | |
| gr.Number(label='maximum heart rate'), | |
| gr.Radio(choices=[0,1], label='exercise induced angina'), | |
| gr.Number(label='oldpeak'), | |
| gr.Dropdown(choices=[1,2,3], label='slope ST'), | |
| gr.Dropdown(choices=[0,1,2,3], label='major vessels'), | |
| gr.Dropdown(choices=[3,6,7], label='thallessemia'), | |
| gr.Dropdown(choices=['Tree', 'QDA', 'MLP', 'Log', 'LDA', 'For', 'SVM'], label='Select the model') | |
| ] | |
| outputs = gr.Label(label='Predicted class probabilities') | |
| gr.Interface(fn=make_prediction, inputs=inputs, outputs=outputs).launch() | |