Foodlg-Flask-Docker

Foodlg Flask backend app to host object detection and classification models, as temporary replacement for Rafiki

Build application (make sure Git LFS is installed)

$ git clone 
$ docker build -t foodlg_flask_101 .

Run the container

Create a container from the image.

$ docker run --name foodlg-flask-container-101 -d -p 5001:5001 foodlg_flask_101

Now visit http://localhost:5001

 Flask app has started successfully! 

Experiment with the API (Optional)

Use the model/predict endpoint to load a test image and get predicted labels for the image from the API. The coordinates of the bounding box are returned in the detection_box field, and contain the array of normalized coordinates (ranging from 0 to 1) in the form [ymin, xmin, ymax, xmax].

You can also test it on the command line, for example:

$ curl -F "[email protected]" -XPOST http://localhost:5001/model/predict

You should see a JSON response like that below:

{
  "status": "ok",
  "predictions": [
      {
          "label_id": "1",
          "label": "banana",
          "probability": 0.944034993648529,
          "detection_box": [
              0.1242099404335022,
              0.12507188320159912,
              0.8423267006874084,
              0.5974075794219971
          ]
      },
      {
          "label_id": "18",
          "label": "duck rice",
          "probability": 0.8645511865615845,
          "detection_box": [
              0.10447660088539124,
              0.17799153923988342,
              0.8422801494598389,
              0.732001781463623
          ]
      }
  ]
}