update

app.py

@@ -1,20 +1,23 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import streamlit as st
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
-
-# Let's chat for 5 lines
-for step in range(5):
-    # encode the new user input, add the eos_token and return a tensor in Pytorch
-    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
-
-    # generated a response while limiting the total chat history to 1000 tokens, 
-    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-
-    # pretty print last ouput tokens from bot
-    print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
+model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
+def generate(instruction, knowledge, dialog):
+    if knowledge != '':
+        knowledge = '[KNOWLEDGE] ' + knowledge
+    dialog = ' EOS '.join(dialog)
+    query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+    input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
+    outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True)
+    output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return output
+# Instruction for a chitchat task
+instruction = f'Instruction: given a dialog context, you need to response empathically.'
+# Leave the knowldge empty
+knowledge = ''
+dialog = [
+    'Does money buy happiness?',
+    'It is a question. Money buys you a lot of things, but not enough to buy happiness.',
+    'What is the best way to buy happiness ?'
+]
+response = generate(instruction, knowledge, dialog)
+print(response)