When you\u2019re done, you can see Cloudinary\u2019s Python SDK documentation<\/a> for more information.<\/p>\n<\/div>\n\n\n This is the logic your code will follow:<\/p>\n\n\n\n View<\/a> the completed code. <\/p>\n\n\n\n To perform the steps in this demo, you’ll need:<\/p>\n\n\n\n In a terminal in your Python3 environment, run the following code:<\/p>\n\n\n In your project, create a new file called When writing your own applications, follow your organization\u2019s policy on storing secrets and don\u2019t expose your API secret.<\/p>\n<\/div>\n\n\n First, create a folder called The Copy the following code to Call this function only the first time you run the program so that the images are uploaded just once.\u00a0<\/p>\n<\/div>\n\n\nCoding Your Product Recommendation Solution<\/h2>\n\n\n\n
![\"Offer](\"https:\/\/cloudinary-res.cloudinary.com\/image\/upload\/v1670982288\/blog\/product_recommendations.png\")
<\/figure>\n\n\n\nPrerequisite<\/h2>\n\n\n\n
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Step 1: Set Up and Configuration<\/h2>\n\n\n\n
pip3 install cloudinary<\/code><\/span><\/pre>\n\n\nmy_products.py<\/code>. Next, copy and paste the following into this file, and replace the <cloud_name><\/code>, <api_key><\/code>, and <api_secret><\/code> with your product environment credentials:<\/p>\n\n\nmy_products.py<\/code>:<\/p>\n\n\n# Set your Cloudinary credentials<\/span>\n# ==============================<\/span>\ncloudinary.config(\n cloud_name = \"<cloud_name>\"<\/span>,\n api_key = \"<api_key>\"<\/span>,\n api_secret = \"<api_secret>\"<\/span>,\n api_proxy = \"\"<\/span>\n)\n\n# import Cloudinary libraries<\/span>\nimport cloudinary\nfrom cloudinary import uploader\nimport cloudinary.api\nfrom cloudinary.utils import cloudinary_url\n\n# import other libraries<\/span>\nimport os\nimport threading\nfrom collections import Counter\nfrom urllib.request import urlopen\nimport ssl\nimport json\nfrom flask import Flask, render_template, request\n\napp = Flask(__name__)<\/code><\/span>Code language:<\/span> PHP<\/span> (<\/span>php<\/span>)<\/span><\/small><\/pre>\n\n\nStep 2: Upload images to Cloudinary with auto-tagging<\/h2>\n\n\n\n
Images<\/code> in the same directory as my_products.py<\/code>. Then, fill that folder with the images you want to use for this project.<\/p>\n\n\nImages<\/code> folder is all set up with sample images when you download the project from GitHub<\/a>.<\/p>\n<\/div>\n\n\nmy_products.py<\/code> to upload all the images from the Images<\/code> folder and automatically tag those images using Amazon Rekognition’s automatic image tagging<\/a> capabilities. Only tags that meet or exceed the specified confidence level of 0.7 are assigned to the image.<\/p>\n\n\n\nmy_products.py<\/code> (con’t):<\/p>\n\n\ndef img_upload():\n # Get your images stored in a local directory.<\/span>\n listOfImgs = os.listdir(os.path.join(os.getcwd()+'\/Images'<\/span>))\n\n # Upload all those images to Cloudinary with auto-tagging.<\/span>\n for<\/span> key,row in enumerate(listOfImgs):\n resp=cloudinary.uploader.upload('Images\/'<\/span>+row,\n categorization = \"aws_rek_tagging\"<\/span>, \n auto_tagging = 0.8<\/span>,\n use_filename = True<\/span>,\n unique_filename = False<\/span>,\n overwrite = True<\/span>,\n folder = \"tagged_images\"<\/span>,\n transformation = \"\/c_fill,g_auto,w_250,h_250\/f_auto\/q_auto\"<\/span>,\n # You\u2019ll use the my_products tag to retrieve all the images.<\/span>\n tag=\"my_products\"<\/span>\n )<\/code><\/span>Code language:<\/span> PHP<\/span> (<\/span>php<\/span>)<\/span><\/small><\/pre>\n\n