Upload app.py

app.py

@@ -30,7 +30,6 @@ except:
 
 '''
 def perform_asde_inference(text, dataset, model_id):
-    print(text)
     if not text:
         if model_id == "PyABSA_Hospital_English_allenai/tk-instruct-base-def-pos_FinedTuned_Model":
             df = pd.read_csv('pyabsa_english.csv')#validation dataset
@@ -39,7 +38,6 @@ def perform_asde_inference(text, dataset, model_id):
         text = selected_df['clean_text']
         true_aspect = selected_df['actual_aspects']
         true_sentiment = selected_df['actual_sentiments']
-        
 
     bos_instruction = """Definition: The output will be the aspects (both implicit and explicit) and the aspects sentiment polarity. In cases where there are no aspects the output should be noaspectterm:none.
         Positive example 1-
@@ -67,11 +65,15 @@ def perform_asde_inference(text, dataset, model_id):
         output = double_keybert_generator.generate(tokenized_text.input_ids,max_length=512)
         result = tokenizer_keybert.decode(output[0], skip_special_tokens=True)
     '''
-    pred_asp = [i.split(':')[0] for i in model_generated.split(',')]
-    pred_sent = [i.split(':')[1] for i in model_generated.split(',')]
+        pred_asp = [i.split(':')[0] for i in model_generated.split(',')]
+        pred_sent = [i.split(':')[1] for i in model_generated.split(',')]
 
-    pred_doubles = pd.DataFrame(list(map(list, zip(pred_asp, pred_sent))),columns=['Aspect','Sentiment'])
-    true_doubles = pd.DataFrame(list(map(list, zip(ast.literal_eval(true_aspect), ast.literal_eval(true_sentiment)))),columns=['Aspect','Sentiment'])
+        pred_doubles = pd.DataFrame(list(map(list, zip(pred_asp, pred_sent))),columns=['Aspect','Sentiment'])
+        
+        if not text:
+            true_doubles = pd.DataFrame(list(map(list, zip(ast.literal_eval(true_aspect), ast.literal_eval(true_sentiment)))),columns=['Aspect','Sentiment'])
+        else:
+            true_doubles = pd.DataFrame()
 
     return pred_doubles, true_doubles, text, model_generated