Create app.py

app.py

@@ -0,0 +1,67 @@
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+
+# download the already pushed model
+model = from_pretrained_keras("buio/structured-data-classification")
+
+def convert_and_predict(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
+
+    # some conversions from the gradio interface are needed
+    sample_converted = {
+    "age": age,
+    "sex": sex,
+    "cp": cp+1,
+    "trestbps": trestbps,
+    "chol": chol,
+    "fbs": 0 if fbs<=120 else 1,
+    "restecg": restecg,
+    "thalach": thalach,
+    "exang": exang,
+    "oldpeak": oldpeak,
+    "slope": slope+1,
+    "ca": ca,
+    "thal": thal,
+}
+
+    input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample_converted.items()}
+    predictions = model.predict(input_dict)
+
+    return f'{predictions[0][0]:.2%}'
+
+## gradio interface elements
+inputs = [
+          gr.Slider(minimum=1, maximum=120, step=1, label='age', value=60),
+          gr.Radio(choices=['female','male'], label='sex', type='index',value='male'),
+          gr.Radio(choices=['typical angina',
+                            'atypical angina',
+                            'non-anginal pain',
+                            'asymptomatic'], 
+                   type='index', label=f'chest pain type', value='typical angina'),
+          gr.Number(label='blood pressure in mmHg', value=145),
+          gr.Number(label='serum cholestoral in mg/dl', value=233),
+          gr.Number(label='fasting blood sugar in mg/dl', value=150),
+          gr.Radio(choices=['normal','T-T wave abnormality','probable or definite left ventricular hypertrophy'], 
+                   label='resting ecg', type='index',value='probable or definite left ventricular hypertrophy'),
+          gr.Number(label='maximum heart rate achieved', value=150),
+          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.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']]
+
+gr.Interface(convert_and_predict, inputs, output, examples= examples, allow_flagging=False, analytics_enabled=False,
+  title=title, description=description).launch()