# import gradio as gr
# import torch
# from transformers import AutoModelForCausalLM, AutoTokenizer

# def load_model():
#     model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", trust_remote_code=True)
#     tok = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat")
#     return model, tok

# def inference(model, tok, PROMPT):
#     x = tok.encode(PROMPT, return_tensors="pt").cuda()
#     x = model.generate(x, max_new_tokens=512).cpu()
#     return tok.batch_decode(x)


# gr.ChatInterface(inference).queue().launch()


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

#tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
#model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1", torch_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat")
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 predict(message, history):

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

    messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]])  #curr_system_message +
                for item in history_transformer_format])

    #     x = tok.encode(PROMPT, return_tensors="pt").cuda()
    #     x = model.generate(x, max_new_tokens=512).cpu()
    #     return tok.batch_decode(x)

    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(predict).queue().launch()



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

    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo',
        messages= history_openai_format,
        temperature=1.0,
        stream=True
    )

    partial_message = ""
    for chunk in response:
        if len(chunk['choices'][0]['delta']) != 0:
            partial_message = partial_message + chunk['choices'][0]['delta']['content']
            yield partial_message