Update app.py

app.py

@@ -2,22 +2,25 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
 
-model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50")
-tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
 
-src_text = " UN Chief Says There Is No Military Solution in Syria"
-tgt_text =  "Şeful ONU declară că nu există o soluţie militară în Siria"
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-model_inputs = tokenizer(src_text, return_tensors="pt")
-with tokenizer.as_target_tokenizer():
-    labels = tokenizer(tgt_text, return_tensors="pt").input_ids
-
-model(**model_inputs, labels=labels) # forward pass
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(input + tokenizer.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)
 
+    # generate a response 
+    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 lines
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
+    return response, history
 
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot()