Update app.py

app.py

@@ -2,25 +2,22 @@ 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")
 
-tokenizer = AutoTokenizer.from_pretrained("gpt2-large")
-model = AutoModelForCausalLM.from_pretrained("gpt2-large")
+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"
 
-def predict(input, history=[]):
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+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
 
-    # 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()