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Lobe is a free, easy to use app that has everything you need to bring your machine learning ideas to life. This Flask starter project creates a REST API to get predictions from a TensorFlow model on your projects or apps. To start using it, follow the instructions below:

1. Clone or download the project on your computer to get started. You'll need Python 3.6, 3.7, 3.8, or 3.9 to run this starter project as well.

2. Export a TensorFlow model from Lobe

3. Move thesaved_model.pb file,variables folder, andsignature.json file exported from Lobe to the/model folder

3. Create and activate a virtual environment

python-mvenv .venv.venv\Scripts\activate

4. Install dependencies

python-mpipinstall--upgradepip&&pipinstall-rrequirements.txt

5. Run the server locally

pythonapp.py

3. Create and activate a virtual environment

python-mvenv .venvsource .venv/bin/activate

4. Install dependencies

python-mpipinstall--upgradepip&&pipinstall-rrequirements.txt

5. Run the server

pythonapp.py# orexportFLASK_APP=app.pyflaskrun

1. Have version 2.0.80 or higher ofAzure CLI installed.
   az --version
2. Login by running this command and following prompts
   az login
3. Deploy to the cloud!
   az webapp up --sku B1 --name <your unique app name>

Azure documentation is available if you run into issues.Thisquick start is a good starting point.

1. Perform a post request to the target url/predict with your base64 image. Refer totesting.py for getting started sending requests to the server.

{"image":"<base64 image>"}

2. Successful requests return JSON with the confidences of your predictions.

{"predictions": [    {"predicted_label":0.9105    },    {"another_label":0.0895    }  ]}

The Flask starter project is optimized for models exported from Lobe but could be used with any TensorFlow models with some small updates.

Lobe has an endpoint built in called Lobe Connect that can be used while running the app and this starter project works the same way. If your app works with Lobe Connect, it will work with this starter project just by updating the URL.

We are using TensorFlow 2.7.0 to run the tf_example.py file. If you see any GPU errors or want to run the script on GPU please refer tohttps://www.tensorflow.org/install/gpu

The code takes in abase64 image and returns an array of predictions and confidences. The server code that defines endpoints is inapp.py. And the code for using your model including image pre-processing and output formatting for a prediction is intf_model_helper.py. For reference, the Swagger definition file lives inswagger/.

GitHub Issues are for reporting bugs, discussing features and general feedback on the Flask starter project. Be sure to check our documentation, FAQ and past issues before opening any new ones.

To share your project, get feedback on it, and learn more about Lobe, please visit our community onReddit. We look forward to seeing the amazing projects that can be built, when machine learning is made accessible to you.