Update app.py

app.py

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+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('/content/drive/MyDrive/Colab Notebooks/Homework04/Tree.pkl', 'rb') as f:
+    tree_model = pickle.load(f)
+
+with open('/content/drive/MyDrive/Colab Notebooks/Homework04/svm.pkl', 'rb') as f:
+    svm_model = pickle.load(f)
+
+with open('/content/drive/MyDrive/Colab Notebooks/Homework04/QDA.pkl', 'rb') as f:
+    qda_model = pickle.load(f)
+
+with open('/content/drive/MyDrive/Colab Notebooks/Homework04/MLP.pkl', 'rb') as f:
+    mlp_model = pickle.load(f)
+
+with open('/content/drive/MyDrive/Colab Notebooks/Homework04/Log.pkl', 'rb') as f:
+    log_model = pickle.load(f)
+
+with open('/content/drive/MyDrive/Colab Notebooks/Homework04/LDA.pkl', 'rb') as f:
+    lda_model = pickle.load(f)
+
+with open('/content/drive/MyDrive/Colab Notebooks/Homework04/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()