双十二智能识图推荐

import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
import numpy as np
import requests
from PIL import Image

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

def preprocess_image(image_url):
    response = requests.get(image_url)
    img = Image.open(io.BytesIO(response.content))
    img = img.resize((224, 224))  # 调整图像大小以适应模型输入
    img_array = np.array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = preprocess_input(img_array)
    return img_array

def get_recommendations(image_url):
    img_array = preprocess_image(image_url)
    predictions = model.predict(img_array)
    decoded_predictions = decode_predictions(predictions, top=3)[0]
    
    # 这里可以根据预测结果进行商品推荐
    recommendations = []
    for pred in decoded_predictions:
        recommendations.append({
            'label': pred[1],
            'confidence': float(pred[2])
        })
    return recommendations

# 示例使用
image_url = 'https://example.com/path/to/image.jpg'
recommendations = get_recommendations(image_url)
print(recommendations)