def chat(model, tokenizer):
    print("type \"q\" to quit. Automatically quits after 5 messages")

    for step in range(5):
        message = input("MESSAGE: ")

        if message in ["", "q"]:  # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = tokenizer.encode(message + 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,  
            no_repeat_ngram_size=3,       
            do_sample=True, 
            top_k=100, 
            top_p=0.7,
            temperature = 0.8, 
        )
        # 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)))


{}
transformers pytorch gpt2 text-generation autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

def chat(model, tokenizer):
    print("type \"q\" to quit. Automatically quits after 5 messages")

    for step in range(5):
        message = input("MESSAGE: ")

        if message in ["", "q"]:  # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([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,  
            no_repeat_ngram_size=3,       
            do_sample=True, 
            top_k=100, 
            top_p=0.7,
            temperature = 0.8, 
        )
        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))


# if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([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,  
            no_repeat_ngram_size=3,       
            do_sample=True, 
            top_k=100, 
            top_p=0.7,
            temperature = 0.8, 
        )
        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([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,  
            no_repeat_ngram_size=3,       
            do_sample=True, 
            top_k=100, 
            top_p=0.7,
            temperature = 0.8, 
        )
        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# if the user doesn't wanna talk
            break

        # encode the new user input, add the eos_token and return a tensor in Pytorch
        new_user_input_ids = URL(message + tokenizer.eos_token, return_tensors='pt')

        # append the new user input tokens to the chat history
        bot_input_ids = URL([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,  
            no_repeat_ngram_size=3,       
            do_sample=True, 
            top_k=100, 
            top_p=0.7,
            temperature = 0.8, 
        )
        # pretty print last ouput tokens from bot
        print("DialoGPT: {}".format(URL(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))