Upload app.py

app.py

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+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("keras-io/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%}'
+
+
+# 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'),
+          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(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
+title = "Structured Data Classification 🧮"
+description =  "Binary classification of structured data including numerical and categorical features for Heart Disease prediction."
+
+article = "Author: <a href=\"https://huggingface.co/buio\">Marco Buiani</a>. Based on this <a href=\"https://keras.io/examples/structured_data/structured_data_classification_from_scratch/\">keras example</a> by <a href=\"https://twitter.com/fchollet\">François Chollet.</a> HuggingFace Model <a href=\"https://huggingface.co/buio/structured-data-classification\">here</a> "
+
+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, article=article, live=True).launch()