Update app.py

app.py

@@ -30,17 +30,16 @@ def get_murmur_from_recordings(audio):
 
 
 def get_patient_outcome(age, sex, height, weight, is_pregnant, murmur):
-    # model = keras.models.load_model('patient_outcome_classifier.keras')
+    keras_model = keras.models.load_model('patient_outcome_classifier_v2.keras')
     model = joblib.load("patient_outcome_classifier_v3.joblib")
+
+    # Data prep
     is_pregnant = 1 if is_pregnant else 0
     sex2int = {'Male': 0, 'Female': 1}
     sex = sex2int[sex]
-
     age2int = {'Neonate': 0, 'Infant': 1, 'Child': 2, 'Adolescent': 3}
     age = age2int[age]
-
     murmur = 0 if murmur == 'Absent' else (1 if murmur == 'Present' else 2)
-
     data = pd.DataFrame({
         'Age': float(age),
         'Sex': float(sex),
@@ -50,14 +49,20 @@ def get_patient_outcome(age, sex, height, weight, is_pregnant, murmur):
         'Murmur': float(murmur),
     }, index=[0])
 
-    output = model.predict(data)[0]
-    # 0 - Normal, 1 - Abnormal
-    results = {
-        'Normal': output == 0,
-        'Abnormal': output == 1
+    # Predict Keras
+    output = keras_model.predict(data)[0]
+    # 0 - Normal, 1 - Abnormal -> %
+    results_keras = {
+        'Normal': output[0],
+        'Abnormal': output[1]
     }
 
-    return results
+    # Predict SVC
+    output = model.predict(data)[0]
+    # 0 - Normal or 1 - Abnormal
+    results_svc = 'Normal' if x == 0 else 'Abnormal'
+
+    return results_keras, results_svc
 
 
 def predict(audio, age, sex, height, weight, is_pregnant):
@@ -88,7 +93,10 @@ demo = gr.Interface(
         gr.Number(label="Weight", value="38.1"),
         gr.Checkbox(label="Pregnant", value=False)
     ],
-    outputs="label",
+    outputs=[
+        gr.Label(label="keras_pred"),
+        gr.Label(label="svc_pred"),
+    ],
     cache_examples=True,
     examples=[
         abnormal,