商品智能识别限时秒杀

import cv2
import numpy as np
from tensorflow.keras.models import load_model

# 加载预训练的商品识别模型
model = load_model('product_recognition_model.h5')

def recognize_product(image_path):
    img = cv2.imread(image_path)
    img = cv2.resize(img, (224, 224))  # 调整图像大小以适应模型输入
    img = np.expand_dims(img, axis=0)  # 增加批次维度
    predictions = model.predict(img)  # 进行预测
    product_id = np.argmax(predictions, axis=1)[0]  # 获取预测结果中概率最高的商品ID
    return product_id

def handle_seckill_request(user_id, product_id, quantity):
    if check_inventory(product_id, quantity):  # 检查库存是否充足
        lock.acquire()  # 获取分布式锁
        try:
            if update_inventory(product_id, -quantity):  # 更新库存
                create_order(user_id, product_id, quantity)  # 创建订单
                return True
        finally:
            lock.release()  # 释放分布式锁
    return False

# 示例调用
image_path = 'example_product.jpg'
product_id = recognize_product(image_path)
if handle_seckill_request(user_id=123, product_id=product_id, quantity=1):
    print("秒杀成功！")
else:
    print("秒杀失败，商品已售罄或库存不足。")