import gradio as gr
#import torch

#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 = 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, ""]]
    
    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
    return "Hello"

gr.ChatInterface(predict).launch()