app.py

import gradio as gr
import openvino_genai as ov_genai
import queue
import threading
import time
from optimum.intel import OVModelForCausalLM, OVWeightQuantizationConfig
import nncf

import huggingface_hub as hf_hub
# 初始化 OpenVINO 模型
model_id = "OpenVINO/Qwen2.5-7B-Instruct-int4-ov"
model_path = "ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)

config = ov_genai.GenerationConfig()
config.max_new_tokens = 4096
config.top_p = 0.9;
config.top_k = 30;


pipe = ov_genai.LLMPipeline(model_path, "CPU")

#pipe.start_chat()

def start_chat():
    pipe.start_chat()
    return "✅ 開始對話！"

def finish_chat():
    pipe.finish_chat()
    return "🛑 結束對話！"


	
# 建立推論函式：使用 streamer 並回傳 generator 結果
def generate_stream(prompt):
    prompt = prompt #+ " /no_think" + " 答案短且明瞭"

    q = queue.Queue()
    tps_result = ""
    def streamer(subword):
        print(subword, end='', flush=True)
        q.put(subword)
        return ov_genai.StreamingStatus.RUNNING

    def worker():
        # 在背景 thread 中做推論
        nonlocal tps_result
        gen_result = pipe.generate([prompt], streamer=streamer, config=config)
        tps = gen_result.perf_metrics.get_throughput().mean
        tps_result = f"{tps:.2f} tokens/s"        
        q.put(None)  # 結束符號

    threading.Thread(target=worker).start()

    result = ""
    while True:
        token = q.get()
        if token is None:
            break
        result += token
        yield result,""  # 把逐步結果傳給 output textbox
    yield result, tps_result

with gr.Blocks() as demo:
    gr.Markdown("## 🧠 OpenVINO Streaming Demo with Gradio Textbox")
    
    with gr.Row():
        start_btn = gr.Button("開始對話")
        end_btn = gr.Button("結束對話")
        status_box = gr.Textbox(label="狀態", interactive=False)
        TPS_box = gr.Textbox(label="TPS", interactive=False)
    
    start_btn.click(fn=start_chat, outputs=status_box)
    end_btn.click(fn=finish_chat, outputs=status_box)
	
    textbox_input = gr.Textbox(label="Prompt", lines=1, placeholder="Enter prompt here...")
    #textbox_output = gr.Textbox(label="Output", elem_id="scrollbox", lines=10)
    textbox_output = gr.Markdown(min_height=30, label="robot answer:")
    
    # 按鈕控制觸發推論
    button = gr.Button("Submit")

    # 當按鈕被按下時，呼叫 generate_stream 並更新 textbox_output
    button.click(fn=generate_stream, inputs=textbox_input, outputs=[textbox_output, TPS_box])

demo.launch()