Update app.py

app.py

@@ -131,7 +131,7 @@ def main():
   
   model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'})
 
-  #st.write('sequence classification loaded')
+  st.write('sequence classification loaded')
   pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer)
   for sent in result:
     pred = pipe1(sent)
@@ -139,7 +139,7 @@ def main():
         if lab['label'] == 'causal': #causal
             causal_sents.append(sent)
 
-  # st.write('causal sentence classification finished')
+  st.write('causal sentence classification finished')
   # st.write("--- %s seconds ---" % (time.time() - start_time))
 
   model_name = "distilbert-base-cased"
@@ -149,7 +149,7 @@ def main():
   
   model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'}
   pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True
-  #st.write('DistilBERT loaded')
+  st.write('DistilBERT loaded')
   sentence_pred = []
   class_list = []
   entity_list = []