import gradio as gr
from huggingface_hub import InferenceClient
from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer, pipeline

# 載入模型和標記器
model_id = "HelloSun/Qwen2.5-0.5B-Instruct-openvino"
model = OVModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# 建立生成管道
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

def respond(message, history):
    # 將當前訊息與歷史訊息合併
    input_text = message if not history else history[-1][1] + " " + message
    # 獲取模型的回應
    response = pipe(input_text, max_length=100, num_return_sequences=1)
    return response[0]['generated_text'], history + [(message, response[0]['generated_text'])]

# 設定 Gradio 的聊天界面
demo = gr.ChatInterface(fn=respond)

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