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import torch
from transformers import AutoTokenizer,AutoModelForMaskedLM, GPT2LMHeadModel,GPT2Tokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
tokenizer = GPT2Tokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = GPT2LMHeadModel.from_pretrained('Stage v3.0')
# Let's chat for 4 lines
for step in range(50):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> You:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([ 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=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100, # It controls the diversity of the generated output; the model considers the top 100 tokens
top_p=0.9,# tokens with a cumulative probability higher than 0.9 are excluded.
temperature=0.9 # It controls the randomness of the generated output
)
# pretty print last ouput tokens from bot
print("Chatbot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
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