Update app.py

app.py

@@ -13,10 +13,10 @@ The bot is trained on blended_skill_talk dataset using facebook/blenderbot-400M-
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.06616' target='_blank'>Recipes for building an open-domain chatbot</a></p><p style='text-align: center'><a href='https://parl.ai/projects/recipes/' target='_blank'>Original PARLAI Code</a></p></center></p>"
 
 import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, BlenderbotForCausalLM, BlenderbotTokenizer
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BlenderbotForConditionalGeneration, BlenderbotForCausalLM, BlenderbotTokenizer
 
 tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-model = BlenderbotForCausalLM.from_pretrained("facebook/blenderbot-400M-distill",add_cross_attention=False)
+model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill",add_cross_attention=False)
 
 def predict(input, history=[]):
     # tokenize the new input sentence
@@ -26,7 +26,7 @@ def predict(input, history=[]):
     bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
 
     # generate a response 
-    history = model.generate(new_user_input_ids , max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
 
     # convert the tokens to text, and then split the responses into the right format
     response = tokenizer.decode(history[0]).replace("<s>","").split("</s>")