12.12商品识别推荐

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.preprocessing import image
import numpy as np

# 加载预训练的ResNet50模型
model = ResNet50(weights='imagenet')

def recognize_product(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    preds = model.predict(x)
    return decode_predictions(preds, top=1)[0][0]

def recommend_products(user_history):
    # 简单的协同过滤推荐逻辑
    # 这里假设user_history是一个包含用户历史购买记录的字典
    recommended_products = []
    for product_id in user_history:
        similar_products = find_similar_products(product_id)
        recommended_products.extend(similar_products)
    return recommended_products

def find_similar_products(product_id):
    # 这里可以实现更复杂的相似度计算逻辑
    # 简单起见，返回一些固定的相似商品ID
    return [product_id + "_similar1", product_id + "_similar2"]

# 示例使用
img_path = 'path_to_image.jpg'
recognized_product = recognize_product(img_path)
print(f"Recognized Product: {recognized_product}")

user_history = {'product1': 5, 'product2': 3}
recommendations = recommend_products(user_history)
print(f"Recommended Products: {recommendations}")