.

app.py

@@ -10,10 +10,9 @@ healthcare_stroke_data['gender'] = encoder.fit_transform(healthcare_stroke_data[
 encoder= ce.OrdinalEncoder(cols=['work_type'],return_df=True, mapping=[{'col':'work_type', 'mapping':{0: 1, 1: 2, 'children': 3, '2': 4, 'Never_worked': 5}}])
 healthcare_stroke_data['work_type'] = encoder.fit_transform(healthcare_stroke_data['work_type'])
 
-best = compare_models()
-compare_model_results = pull()
+adaclassifier3= create_model('ada',fold = 10)
             
-model = gr.inputs.Dropdown(list(compare_model_results['Model']),label="Model")
+model = gr.inputs.Dropdown(list(compare_model_results[adaclassifier3]),label="Model")
 gender = gr.inputs.Dropdown(choices=["Male", "Female"],label = 'gender')
 age = gr.inputs.Slider(minimum=1, maximum=100, default=data['age'].mean(), label = 'age')
 hypertension = gr.inputs.Dropdown(choices=["1", "0"],label = 'hypertension')