Update app.py

app.py

@@ -16,7 +16,7 @@ def load_model(model_name):
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)
     return model, tokenizer
-def extend(input_text, num_return_sequences, max_size=20, top_k=50, top_p=0.95, bad_words):
+def extend(input_text, num_return_sequences, bad_words, max_size=20, top_k=50, top_p=0.95):
     if len(input_text) == 0:
         input_text = ""
     encoded_prompt = tokenizer.encode(
@@ -25,8 +25,7 @@ def extend(input_text, num_return_sequences, max_size=20, top_k=50, top_p=0.95,
     if encoded_prompt.size()[-1] == 0:
         input_ids = None
     else:
-        input_ids = encoded_prompt
-    
+        input_ids = encoded_prompt    
     bad_words = bad_words.split()
     bad_word_ids = []
     for bad_word in bad_words: 
@@ -90,11 +89,11 @@ if __name__ == "__main__":
             if len(text_area.strip()) == 0:
                 text_area = random.choice(suggested_text_list)
             result = extend(input_text=text_area,
-                            num_return_sequences=int(num_return_sequences),                         
+                            num_return_sequences=int(num_return_sequences),  
+                            bad_words = bad_words,                       
                             max_size=int(max_len),                   
                             top_k=int(top_k),
-                            top_p=float(top_p),
-                            bad_words = bad_words)
+                            top_p=float(top_p))
             print("Done length: " + str(len(result)) + " bytes") 
             #<div class="rtl" dir="rtl" style="text-align:right;">
             st.markdown(f"{result}", unsafe_allow_html=True)