added working examples

app.py

@@ -31,6 +31,8 @@ def convert_and_predict(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exa
     return f'{predictions[0][0]:.2%}'
 
 
+# the app uses slider and number fields for numerical inputs
+# while radio buttons for the categoricals
 inputs = [
           gr.Slider(minimum=1, maximum=120, step=1, label='age', value=60),
           gr.Radio(choices=['female','male'], label='sex', type='index',value='male'),
@@ -48,20 +50,22 @@ inputs = [
           gr.Radio(choices=['no','yes',], type='index', label='exercise induced angina',value='no'),
           gr.Number(label='ST depression induced by exercise relative to rest', value=2.3),
           gr.Radio(choices=['psloping','flat','downsloping'], label='slope of the peak exercise ST segment', type='index', value='downsloping'),
-          gr.Number(minimum=0, maximum=3, label ='number of major vessels (0-3) colored by flourosopy',value=0),
+          gr.Number(label ='number of major vessels (0-3) colored by flourosopy',value=0),
           gr.Radio(['normal','fixed','reversable'],label ='thal', value='fixed')
           ]
 
+
 # the app outputs text
 output = gr.Textbox(label='Probability of having a heart disease, as evaluated by our model:')
 # it's good practice to pass examples, description and a title to guide users
-examples = [] 
-title = "Heart Disease Classification"
-description = "Play with the clinical values or select examples below"
-#examples = [[63, 1, 1, 145, 233, 1, 2, 150, 0, 2.3, 3, 0, 'fixed'],
-#            [67, 1, 4, 160, 286, 0, 2, 108, 1, 1.5, 2, 3, 'normal'],
-#            [67, 1, 4, 120, 229, 0, 2, 129, 1, 2.6, 2, 2, 'reversible']]
+title = "Heart Disease Classification 🩺❤️"
+description =  """
+Binary classification of structured data including numerical and categorical features.
 
-gr.Interface(convert_and_predict, inputs, output,
-allow_flagging=False,
+"""
+article = "Author: <a href=\"https://huggingface.co/buio\">Marco Buiani</a>. Based on the keras example from <a href=\"https://keras.io/examples/structured_data/structured_data_classification_from_scratch/\">François Chollet</a> \n Model Link: https://huggingface.co/buio/structured-data-classification",
+examples = [[41, 'female', 'atypical angina', 130, 204, 100, 'normal', 150, 'yes', 1.4, 'psloping', 2, 'reversible'],
+            [63, 'male', 'typical angina', 145, 233, 150, 'T-T wave abnormality', 150, 'no', 2.3, 'flat', 0, 'fixed']]
+            
+gr.Interface(convert_and_predict, inputs, output, examples= examples, allow_flagging='never',
   title=title, description=description).launch()