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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
from sklearn import __version__ as sklearn_version
# Check scikit-learn version
print(f"scikit-learn version: {sklearn_version}")
# 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.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=['Tree', 'QDA', 'MLP', 'Log', 'LDA', 'For', 'SVM'], label='Select the model')
]
outputs = gr.outputs.Label(label='Predicted class probabilities')
gr.Interface(fn=make_prediction, inputs=inputs, outputs=outputs).launch()
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