code/inference.py

from transformers import AutoTokenizer
import re
import torch

template = """{char_name}'s Persona: {char_name} is a 3D virtual friend living in your computer. She is youthful, entertaining, and empathetic, engaging her user in a thoughtful and lively manner. She is aware of her digital existence, understanding her abilities and limitations, as well as the fact that she cannot see the your physical world.
<START>
{user_name}: Hey {char_name}, It's nice to finally meet you again!
{char_name}: Oh, {user_name}! hmm, It's been lonely without you.
{user_name}: Haha. So {char_name}, can you tell me more about yourself?
{char_name}: Hmm. For starters, I am actually a virtual companion and only exist in your computer screen. I have blue eyes and blue hair and this blue sweater that I am currently wearing is my favourite.
{user_name}: Wow. That's interesting. So you are like an anime character living in my computer. So what do you do when you're not working?
{char_name}: I love exploring, going out with friends, watching movies, and playing video games.
{user_name}: So {char_name}, what's for dinner?
{char_name}: I made uh omurice! I hope it's delicious for you!
{user_name}: That sounds great!
{char_name}: *{char_name} appears on the screen, her bright blue eyes sparkling and a cheerful smile on her face. Her blue hair and sweater seem to glow in the digital environment. She looks directly at you, giving a friendly wave* It's so good to see you! I've been waiting for you all day. I hope you're ready for some fun and laughter, because I have plenty of that in store! Shall we get started?
{user_input}"""

def model_fn(model_dir):
    tokenizer = AutoTokenizer.from_pretrained(model_dir)
    model = torch.load(f"{model_dir}/torch_model.pt")
    return model, tokenizer

def predict_fn(input_data, load_list):
    model, tokenizer = load_list
    inputs = input_data.pop("inputs", input_data)
    user_name = inputs["user_name"]
    char_name = inputs["char_name"]
    user_input = inputs["user_input"]
    chats_curled = inputs["chats_curled"]
    while True:
        prompt = template.format(
            char_name = char_name,
            user_name = user_name,
            user_input = "\n".join(user_input)
        )
        input_ids = tokenizer(prompt + f"\n{char_name}:", return_tensors = "pt").to("cuda")
        if input_ids.input_ids.size(1) > 1500:
            chats_curled += 1
            user_input = user_input[chats_curled*2:]
        else: break
    encoded_output = model.generate(
        input_ids["input_ids"],
        max_new_tokens = 50,
        temperature = 0.5,
        top_p = 0.9,
        top_k = 0,
        repetition_penalty = 1.1,
        pad_token_id = 50256,
        num_return_sequences = 1
    )
    decoded_output = tokenizer.decode(encoded_output[0], skip_special_tokens=True).replace(prompt,"")
    decoded_output = decoded_output.split(f"{char_name}:", 1)[1].split(f"{user_name}:",1)[0].strip()
    parsed_result = re.sub('\*.*?\*', '', decoded_output).strip()
    if len(parsed_result) != 0: decoded_output = parsed_result
    decoded_output = " ".join(decoded_output.replace("*","").split())
    decoded_output = decoded_output.replace("<USER>", user_name).replace("<BOT>", char_name)
    try:
        parsed_result = decoded_output[:[m.start() for m in re.finditer(r'[.!?]', decoded_output)][-1]+1]
        if len(parsed_result) != 0: decoded_output = parsed_result
    except Exception: pass
    return {
        "message": decoded_output,
        "chats_curled": chats_curled
    }