import gradio as gr
#from huggingface_hub import InferenceClient
import torch
import bitsandbytes
from unsloth import FastLanguageModel
from transformers import TextStreamer, StoppingCriteriaList, StoppingCriteria, TextIteratorStreamer
from threading import Thread


model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "jjsprockel/Patologia_lora_model1",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
    )

FastLanguageModel.for_inference(model)

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]])
                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=2048,
        #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(debug=True)