双十二汽车相关识别推荐

import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions
import numpy as np

# 加载预训练的MobileNetV2模型
model = MobileNetV2(weights='imagenet')

# 读取并预处理图像
img_path = 'path_to_your_image.jpg'
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)

# 进行预测
preds = model.predict(x)
print('Predicted:', decode_predictions(preds, top=3)[0])

def recommend_car(user_history):
    # 假设user_history是一个包含用户过去购买汽车类型的列表
    car_types = ['SUV', 'Sedan', 'Hatchback']
    recommended_cars = []

    for car_type in car_types:
        if car_type in user_history:
            recommended_cars.extend(get_similar_cars(car_type))

    return recommended_cars

def get_similar_cars(car_type):
    # 这里应该有一个数据库查询或者其他逻辑来获取相似的汽车
    similar_cars = {
        'SUV': ['Toyota Highlander', 'Honda CR-V'],
        'Sedan': ['Toyota Camry', 'Honda Accord'],
        'Hatchback': ['Toyota Corolla Hatchback', 'Honda Civic Hatchback']
    }
    return similar_cars.get(car_type, [])

# 示例使用
user_history = ['SUV', 'Sedan']
print(recommend_car(user_history))