Update app.py

app.py

@@ -33,14 +33,16 @@ model_list = [
 ]
 
 def predict(text, target, chosen_model):    
-    model1 = tf.keras.models.load_model(chosen_model, custom_objects={"TFBertModel": TFBertModel})
+    # model1 = tf.keras.models.load_model(chosen_model, custom_objects={"TFBertModel": TFBertModel})
+    model1 = from_pretrained_keras(chosen_model)
+    
     checkpoint = "neuralmind/bert-base-portuguese-cased"
     tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=True, model_max_length=512)
-    tokpair = tokenizer(text, target, truncation=True, padding='max_length', return_tensors='np', return_token_type_ids=False)
+    tokpair = tokenizer(text, target, truncation=True, padding='max_length', return_tensors='tf', return_token_type_ids=False)
     
-    outp = model1(tokpair)
+    outp = model1.signatures["serving_default"](**tokpair)
     
-    proto_tensor = tf.make_tensor_proto(outp)
+    proto_tensor = tf.make_tensor_proto(outp['outp'])
     allscores = tf.make_ndarray(proto_tensor)[0]
         
     scores_dict = {