# Data annotator app
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/data-annotator.html

> Leverage the data annotator app to both label new data and label predicted data within an active learning situation after training a model with DataRobot.

# Data annotator app

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/data_annotator_app/data_annotator_app.ipynb)

High-quality training data is necessary for a top-notch machine learning model. But how you can quickly and easily collect labels from a team of human reviewers? One way is to stand up a Flask app for quick labeling review. This notebook will show you how to leverage the data-annotator app to both (1) label new data and (2) label predicted data within an active learning situation after training a model with DataRobot.

The data-annotator app requires two inputs:

- img_path : The app is currently configured for labeling images (jpg and png are both supported formats). You need to place these images within a directory and specify that path to the app.
- data_path : You need to tell the app all possible labels for your images.
- If you are classifying images that have not yet been labeled, you can provide a csv file with at least one column named label that contains all potential classes. See Scenario 1 below for more details.
- If you are classifying images that have already been assigned labels, you can provide a csv file with at least two columns named img_path (filename of the image) and label (assigned class for the image).
- If you are classifying images that have already been scored within DataRobot, please refer to Scenario 2 below for more details on how to configure the dataset.

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