Update app.py

app.py

@@ -1,19 +1,14 @@
-
-
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 import torch
 
-
 title = "IEEE CIS Summer School - 2023"
 description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
 examples = [["How are you?"]]
 
-
-tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill", padding_side='left')  # Set padding_side='left'
 model = AutoModelForCausalLM.from_pretrained("facebook/blenderbot-400M-distill")
 
-
 def predict(input, history=[]):
     # tokenize the new input sentence
     new_user_input_ids = tokenizer.encode(
@@ -29,7 +24,7 @@ def predict(input, history=[]):
     ).tolist()
 
     # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response = tokenizer.decode(history[0]).split("")
     # print('decoded_response-->>'+str(response))
     response = [
         (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
@@ -37,7 +32,6 @@ def predict(input, history=[]):
     # print('response-->>'+str(response))
     return response, history
 
-
 gr.Interface(
     fn=predict,
     title=title,