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

MODEL_ID = "HODACHI/EZO-Common-9B-gemma-2-it"
DTYPE = torch.bfloat16

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="cuda",
    torch_dtype=DTYPE,
)

def respond(
    message,
    history: list[tuple[str, str]],
    max_tokens,
    temperature,
    top_p,
):
    chat = []
    for user, assistant in history:
        chat.append({"role": "user", "content": user})
        chat.append({"role": "assistant", "content": assistant})
    chat.append({"role": "user", "content": message})

    prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    generation_kwargs = dict(
        input_ids=inputs,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
        streamer=streamer,
    )
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    response = ""
    for new_text in streamer:
        response += new_text
        yield response

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Slider(minimum=1, maximum=2048, value=150, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

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