app.py

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

model_name = "scb10x/typhoon2.5-qwen3-4b"
token = os.getenv("HF_TOKEN")

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
)

model.to("cuda" if torch.cuda.is_available() else "cpu")

def generate_chat(message):
    messages = [{"role": "user", "content": message}]

    input_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(model.device)

    output_ids = model.generate(
        input_ids,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id,
    )

    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    response_text = response.split(message)[-1].strip()

    return response_text

@spaces.GPU
def predict(message, history=None):  # history ไม่ถูกใช้
    response = generate_chat(message)
    return response

chat_ui = gr.ChatInterface(
    fn=predict,
    title="Typhoon 2.5 ZeroGPU",
)

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