ml-food-rating

Food Rating Classification using Convolutional Neural Network (CNN) and deployed using TensorFlow Serving.

Notebook: MamMates Food Rating

Dataset: Food Rating Dataset

Features 💡

Using Mammates Food Rating, you can categorize food images into 3 classes.

Class	Label
0	Rating 1 (MamRates ⭐)
1	Rating 2 (MamRates ⭐⭐)
2	Rating 3 (MamRates 🌟🌟🌟)

Prerequisites 📋

• TensorFlow 2.14.0 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-rating

• Run the image:

docker run -p 8502:8502 --name ml-rating putuwaw/mammates-food-rating

• You can check that the model is already running by opening the browser and go to http://localhost:8502/v1/models/food_rating

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

curl -s https://raw.githubusercontent.com/MamMates/ml-food-rating/main/example.json | curl -X POST -d @- http://localhost:8502/v1/models/food_rating:predict

• You will get the following response:

{
  "predictions": [[0.00170704955, 0.46321556, 0.535077393]]
}

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-rating.git

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

• Build the Docker image:

docker build -t mammates-food-rating .

• Run the image:

docker run -p 8502:8502 --name ml-rating mammates-food-rating

• You can check that the model is already running by opening browser and go to http://localhost:8502/v1/models/food_rating

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

curl -d @example.json -X POST http://localhost:8502/v1/models/food_rating:predict

• To stop the container:

docker stop ml-rating

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.