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

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [50256, 50295]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

@spaces.GPU(duration=480)
def predict(message, history):
    torch.set_default_device("cuda")

    tokenizer = AutoTokenizer.from_pretrained(
        "cognitivecomputations/dolphin-2.8-mistral-7b-v02",
        trust_remote_code=True
    )
    model = AutoModelForCausalLM.from_pretrained(
        "cognitivecomputations/dolphin-2.8-mistral-7b-v02",
        torch_dtype="auto",
        load_in_4bit=True,
        trust_remote_code=True
    )
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    system_prompt = "<|im_start|>system\nYou are Dolphin, a helpful AI assistant.<|im_end|>"
    messages = system_prompt + "".join(["".join(["\n<|im_start|>user\n" + item[0], "<|im_end|>\n<|im_start|>assistant\n" + item[1]]) for item in history_transformer_format])
    input_ids = tokenizer([messages], return_tensors="pt").to('cuda')
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        input_ids,
        streamer=streamer,
        max_new_tokens=256,
        do_sample=True,
        top_p=0.95,
        top_k=50,
        temperature=0.7,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    partial_message = ""
    for new_token in streamer:
        partial_message += new_token
        if '<|im_end|>' in partial_message:
            break
        yield partial_message

gr.ChatInterface(predict).launch()