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

tokenizer = AutoTokenizer.from_pretrained("haidlir/bloom-chatml-id")
model = AutoModelForCausalLM.from_pretrained("haidlir/bloom-chatml-id")

def predict(message, history):

    history_chatml_format = []
    for human, assistant in history:
        history_chatml_format.append({"role": "user", "content": human })
        history_chatml_format.append({"role": "assistant", "content":assistant})
    history_chatml_format.append({"role": "user", "content": message})

    model_inputs = chat_tokenizer.apply_chat_template(
                    history_chatml_format,
                    tokenize=True,
                    add_generation_prompt=True,
                    return_tensors="pt",
                )

    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(predict).launch()