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

TOKENIZER_REPO = "MediaTek-Research/Breeze-7B-Instruct-v1_0"
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_REPO,local_files_only=False,use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    TOKENIZER_REPO,
    device_map="auto",
    local_files_only=False,
    torch_dtype=torch.bfloat16
)



def generate(text):
    chat_data = []
    text = text.strip()
    print("text===="+text)
    if text:
       chat_data.append({"role": "system", "content": text})
    print(chat_data)
    achat=tokenizer.apply_chat_template(chat_data,return_tensors="pt")
    print(achat)
    outputs = model.generate(achat,
                         max_new_tokens=128,
                         top_p=0.01,
                         top_k=85,
                         repetition_penalty=1.1,
                         temperature=0.01)

    theResult=tokenizer.decode(outputs[0])
    print(theResult)
    splitOutput=theResult.splitlines()
	theReturn="";
    for i in range(0,len(splitOutput)):
	   print("i={},out={}".format(i, splitOutput[i]))
	   if(i>0 and splitOutput[i].strip()):
	     theReturn+=splitOutput[i].strip()
    print("result={}".format(theReturn))
    return tokenizer.decode(outputs[0])

gradio_app = gr.Interface(
    generate,
    inputs=gr.Text(),
    outputs=gr.Text(),
    title="test",
)

if __name__ == "__main__":
    gradio_app.launch()