Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import pickle | |
| import pandas as pd | |
| # 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 open('Tree.pkl', 'rb') as f: | |
| tree_model = pickle.load(f) | |
| with open('svm.pkl', 'rb') as f: | |
| svm_model = pickle.load(f) | |
| with open('QDA.pkl', 'rb') as f: | |
| qda_model = pickle.load(f) | |
| with open('MLP.pkl', 'rb') as f: | |
| mlp_model = pickle.load(f) | |
| with open('Log.pkl', 'rb') as f: | |
| log_model = pickle.load(f) | |
| with open('LDA.pkl', 'rb') as f: | |
| lda_model = pickle.load(f) | |
| with open('For.pkl', 'rb') as f: | |
| for_model = pickle.load(f) | |
| # 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 | |
| from sklearn.model_selection import train_test_split | |
| 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) | |
| from sklearn.preprocessing import StandardScaler | |
| scaler = StandardScaler() | |
| X_train_std = scaler.fit_transform(X_train) | |
| # choose the model and make prediction | |
| model_dict = {'Decision_Tree': tree_model, | |
| 'QDA': qda_model, | |
| 'Artificial_Neural_Networks': mlp_model, | |
| 'Logistic_Regression': log_model, | |
| 'LDA': lda_model, | |
| 'Random_Forest': for_model, | |
| 'SVM': svm_model} | |
| model = model_dict[model_name] | |
| input_data_std = scaler.transform(input_data) | |
| probas = model.predict_proba(input_data_std) | |
| outtext={1:'no heart_disease', 2:'heart disease'} | |
| return {f"Probability of Class {i+1}": proba for i, proba in enumerate(probas[0])} | |
| # Create the Gradio interface | |
| inputs = [ | |
| gr.inputs.Number(label='age'), | |
| gr.inputs.Radio(choices=[0,1], label='sex'), | |
| gr.inputs.Dropdown(choices=[1,2,3,4], label='chest pain type'), | |
| gr.inputs.Number(label='resting blood pressure'), | |
| gr.inputs.Number(label='serum cholestoral'), | |
| gr.inputs.Radio(choices=[0,1], label='fasting blood sugar'), | |
| gr.inputs.Radio(choices=[0,1,2], label='resting electrocardiographic'), | |
| gr.inputs.Number(label='maximum heart rate'), | |
| gr.inputs.Radio(choices=[0,1], label='exercise induced angina'), | |
| gr.inputs.Number(label='oldpeak'), | |
| gr.inputs.Dropdown(choices=[1,2,3], label='slope ST'), | |
| gr.inputs.Dropdown(choices=[0,1,2,3], label='major vessels'), | |
| gr.inputs.Dropdown(choices=[3,6,7], label='thal'), | |
| gr.inputs.Dropdown(choices=['Decision_Tree', 'QDA', 'Artificial_Neural_Networks', 'Logistic_Regression', 'LDA', 'Random_Forest', 'SVM'], label='Select the model') | |
| ] | |
| outputs = gr.outputs.Label(label='Predicted class probabilities') | |
| gr.Interface(fn=make_prediction, inputs=inputs, outputs=outputs).launch() |