import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
import bitsandbytes

tokenizer = AutoTokenizer.from_pretrained("./model/")
model = AutoModelForCausalLM.from_pretrained("./model/", device_map="auto", load_in_4bit=True)
model = model.to('cuda:0')

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [29, 0]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

def chat(message, history):
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    messages = "".join("".join(["/n<human>:"+item[0], "/n<bot>:"+item[1]]) for item in history_transformer_format)
    model_inputs = tokenizer([messages], return_tensors="pt").to('cuda')
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=1.0,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    partial_message = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            yield partial_message

gr.ChatInterface(chat).launch()