ml-food-recommendation

Food Recommendation System using TensorFlow Recommenders (TFRS) and deployed using TensorFlow Serving.

Notebook: MamMates Food Recommendation

Dataset: Food Recommendation Dataset (Dummy)

Features 💡

Using MamMates Food Recommendation, you can get food recommendation based on the given id_user.

Prerequisites 📋

• TensorFlow 2.14.0 or higher
• TensorFlow Recommenders 0.7.3 or higher
• Docker 24.0.7 or higher

Usage ✨

If you already have Docker installed, you only need to run the following command:

• Pull the image from Docker Hub:

docker pull putuwaw/mammates-food-recommendation

• Run the image:

docker run -p 8504:8504 --name ml-rec putuwaw/mammates-food-recommendation

• You can check that the model is already running by opening the browser and go to http://localhost:8504/v1/models/food_rec

• To do prediction, you can use the following command:

curl -s https://raw.githubusercontent.com/MamMates/ml-food-recommendation/main/example.json | curl -X POST -d @- http://localhost:8504/v1/models/food_rec:predict

• You will get the following response:

{
  "predictions": [
    {
      "output_1": [
        1.59945917, 1.14119792, 0.741919041, 0.635785818, 0.532811046,
        0.467606097, 0.457192838, 0.0975963473, 0.017279733, -0.0865440145
      ],
      "output_2": ["13", "14", "12", "2", "18", "20", "11", "10", "7", "9"]
    }
  ]
}

Development 💻

If you want to develop this model, you can follow the steps below:

• Clone this repository:

git clone https://github.com/MamMates/ml-food-recommendation.git

• Update the model by changing the saved model in the model folder.

• Build the Docker image:

docker build -t mammates-food-recommendation .

• Run the image:

docker run -p 8504:8504 --name ml-rec mammates-food-recommendation

• You can check that the model is already running by opening browser and go to http://localhost:8504/v1/models/food_rec

• To do prediction, you can use the following command:

curl -d @example.json -X POST http://localhost:8504/v1/models/food_rec:predict

• To stop the container:

docker stop ml-rec

Note

If you want to learn more about TensorFlow Serving, you can read the REST API documentation here.

License 📝

This project is licensed under the MIT License. See the LICENSE file for details.