Upload app.py

app.py

@@ -36,11 +36,11 @@ def perform_asde_inference(text, dataset, model_id):
         random_i = np.random.randint(low=0, high=df.shape[0], size=(1,)).flat[0]
         selected_df = df.iloc[random_i]
         text = selected_df['clean_text']
-        print(type(selected_df['actual_aspects']))
-        print(type(selected_df['actual_sentiments']))
-        print(selected_df['actual_aspects'])
         true_aspect = selected_df['actual_aspects']
         true_sentiment = selected_df['actual_sentiments']
+        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([["",""]],columns=['Aspect','Sentiment'])
 
     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-
@@ -74,11 +74,6 @@ def perform_asde_inference(text, dataset, model_id):
 
     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([["",""]],columns=['Aspect','Sentiment'])
-
     return pred_doubles, true_doubles, text, model_generated
 
 def run_demo(text, dataset, model_id):