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import gradio as gr
from transformers import AutoTokenizer
from optimum.intel import OVModelForCausalLM
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning, message="__array__ implementation doesn't accept a copy keyword")
# 模型與標記器載入（你的原始代碼）
model_id = "hsuwill000/DeepSeek-R1-Distill-Qwen-1.5B-openvino"
print("Loading model...")
model = OVModelForCausalLM.from_pretrained(model_id, device_map="auto")
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
def respond(prompt, history):
messages = [
{"role": "system", "content": "使用中文。"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=4096,
temperature=0.7,
top_p=0.9,
do_sample=True
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
response = response.replace("<think>", "**THINK**").replace("</think>", "**THINK**").strip()
return response
with gr.Blocks() as demo:
gr.Markdown("# DeepSeek-R1-Distill-Qwen-1.5B-openvino")
with gr.Tabs():
with gr.TabItem("聊天"):
chat_if = gr.Interface(
fn=respond,
inputs=gr.Textbox(label="Prompt", placeholder="請輸入訊息..."),
outputs=gr.Textbox(label="Response", interactive=False),
api_name="hchat",
title="DeepSeek-R1-Distill-Qwen-1.5B-openvino",
description="回傳輸入內容的測試 API",
)
if __name__ == "__main__":
print("Launching Gradio app...")
#demo.queue(api_open=True, max_size=1)
demo.launch(server_name="0.0.0.0", server_port=7860, share=True, api_open=True, max_size=1)
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