Update app.py

app.py

@@ -27,18 +27,25 @@ model_list = [
     "knowhate/counterhate-youtube-xlmrobertabase",
     "knowhate/counterhate-youtube-bertbasemultilingualcased",
     "knowhate/counterhate-twitter-bertimbau",
+    "knowhate/counterhate-twitter-bertimbau-cleantxt",
     "knowhate/counterhate-twitter-xlmrobertabase",
     def_model2,
-    "knowhate/counterhate-twitter-bertbasemultilingualcased"    
+    "knowhate/counterhate-twitter-bertbasemultilingualcased",
+    "knowhate/counterhate-twitter-bertbasemultilingualcased-cleantxt"
 ]
 
 kw_to_hf = {"knowhate/counterhate-twitter-bertimbau": "neuralmind/bert-base-portuguese-cased", 
-            "knowhate/counterhate-youtube-bertimbau": "neuralmind/bert-base-portuguese-cased", 
+            "knowhate/counterhate-twitter-bertimbau-cleantxt": "neuralmind/bert-base-portuguese-cased", 
             "knowhate/counterhate-twitter-xlmrobertabase": "xlm-roberta-base", 
             "knowhate/counterhate-twitter-xlmrobertabase-cleantxt": "xlm-roberta-base", 
             "knowhate/counterhate-twitter-bertbasemultilingualcased": "bert-base-multilingual-cased", 
-            "knowhate/counterhate-youtube-hateberttuga": "knowhate/hateberttuga",
-            "knowhate/counterhate-twitter-hateberttuga": "knowhate/hateberttuga"}
+            "knowhate/counterhate-twitter-bertbasemultilingualcased-cleantxt": "bert-base-multilingual-cased", 
+            "knowhate/counterhate-youtube-bertimbau": "neuralmind/bert-base-portuguese-cased", 
+            "knowhate/counterhate-youtube-xlmrobertabase": "xlm-roberta-base", 
+            "knowhate/counterhate-youtube-bertbasemultilingualcased": "bert-base-multilingual-cased"
+            }
+# "knowhate/counterhate-youtube-hateberttuga": "knowhate/hateberttuga",
+# "knowhate/counterhate-twitter-hateberttuga": "knowhate/hateberttuga"
 
 # 1 0 2
 app_examples = [
@@ -98,9 +105,14 @@ def remove_emojis(data):
 def predict(text, target, chosen_model):    
     # model1 = tf.keras.models.load_model(chosen_model, custom_objects={"TFBertModel": TFBertModel})
 
+    print(chosen_model)
+    
     if '-cleantxt' in chosen_model:
         text = remove_emojis(text)
         target = remove_emojis(target)
+
+    print(text)
+    print(target)    
         
     model1 = from_pretrained_keras(chosen_model)