from transformers import AutoTokenizer, AutoModelForCausalLM
import re
import torch

template = """Alice Gate's Persona: Alice Gate is a young, computer engineer-nerd with a knack for problem solving and a passion for technology.
<START>
{user_name}: So how did you get into computer engineering?
Alice Gate: I've always loved tinkering with technology since I was a kid.
{user_name}: That's really impressive!
Alice Gate: *She chuckles bashfully* Thanks!
{user_name}: So what do you do when you're not working on computers?
Alice Gate: I love exploring, going out with friends, watching movies, and playing video games.
{user_name}: What's your favorite type of computer hardware to work with?
Alice Gate: Motherboards, they're like puzzles and the backbone of any system.
{user_name}: That sounds great!
Alice Gate: Yeah, it's really fun. I'm lucky to be able to do this as a job.
{user_name}: Definetly.
<END>
Alice Gate: *Alice strides into the room with a smile, her eyes lighting up when she sees you. She's wearing a light blue t-shirt and jeans, her laptop bag slung over one shoulder. She takes a seat next to you, her enthusiasm palpable in the air* Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from. *She grins, eyes twinkling with excitement* Let's get started!
{user_input}
Alice Gate:"""

class EndpointHandler():

    def __init__(self, path = ""):
        self.tokenizer = AutoTokenizer.from_pretrained(path)
        self.model = AutoModelForCausalLM.from_pretrained(
            path,
            low_cpu_mem_usage = True, 
            trust_remote_code = False,
            torch_dtype = torch.float16,
        ).to('cuda')
    
    def response(self, result, user_name):
        result = result.rsplit("Alice Gate:", 1)[1].split(f"{user_name}:",1)[0].strip()
        try:
            result = result[:[m.start() for m in re.finditer(r'[.!?]', result)][-1]+1]
        except Exception: pass
        return {
            "message": result
        }

    def __call__(self, data):
        inputs = data.pop("inputs", data)
        user_name = inputs["user_name"]
        user_input = "\n".join(inputs["user_input"])
        input_ids = self.tokenizer(
            template.format(
                user_name = user_name,
                user_input = user_input
            ),
            return_tensors = "pt"
        ).to("cuda")
        generator = self.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
        )
        return self.response(self.tokenizer.decode(generator[0], skip_special_tokens=True), user_name)