/ml-food-recommendation

Food Recommendation System using TFRS

Primary LanguageJupyter NotebookMIT LicenseMIT

ml-food-recommendation

TensorFlow Docker Google Colab LICENSE Docker Version Docker Pulls

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 📋

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
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
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.