Update app.py

app.py

@@ -1,38 +1,40 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+
+
 import torch
 
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+chat_tkn = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+mdl = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
 
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+def converse(user_input, chat_history=[]):
+    
+    user_input_ids = chat_tkn.encode(user_input + chat_tkn.eos_token, return_tensors='pt')
 
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+    # create a combined tensor with chat history
+    bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1)
 
     # generate a response 
-    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
-    print(history)
+    chat_history = mdl.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tkn.eos_token_id).tolist()
+    print (chat_history)
+
     # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response = chat_tkn.decode(chat_history[0]).split("<|endoftext|>",skip_special_tokens=True)
+    #response.remove("")
     print("starting to print response")
     print(response)
-    response.remove("")
-    
     
     # write some HTML
     html = "<div class='chatbot'>"
     for m, msg in enumerate(response):
-       
         cls = "user" if m%2 == 0 else "bot"
         print("value of m")
         print(m)
+        print("message")
         print (msg)
         html += "<div class='msg {}'> {}</div>".format(cls, msg)
     html += "</div>"
     print(html)
-    return html, history
+    return html, chat_history
 
 import gradio as gr
 
@@ -44,7 +46,7 @@ css = """
 .footer {display:none !important}
 """
 
-gr.Interface(fn=predict,
+gr.Interface(fn=converse,
              theme="default",
              inputs=[gr.inputs.Textbox(placeholder="How are you?"), "state"],
              outputs=["html", "state"],