.. _streamlit:

#########
Streamlit
#########

Labeled Frame Diagnostics
=========================

Analyze predictions of one or more networks on the `train/test/val` images.
Run the following command from inside the ``lightning-pose/lightning_pose/apps`` directory
(make sure you have activated your conda environment):

.. code-block:: console

    streamlit run labeled_frame_diagnostics.py -- --model_dir <ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY>

The only argument needed is ``--model_dir``, which tells the app where to find models and their predictions. ``<ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY>`` should contain hydra subfolders of the type ``YYYY-MM-DD/HH-MM-SS``.

.. note:

    The lightning-pose output folder for a single model is typically ``/path/to/lightning-pose/outputs/YYYY-MM-DD/HH-MM-SS``, where the last folder contains prediction csv files.

The app shows:

* plot of a selected metric (e.g. pixel errors, confidences) for each network and each body part, using bar/box/violin/etc plots.
* scatterplot of a selected metric between two networks

Video Diagnostics
=================

Visualizes multiple networks' predictions on a test video.
From within ``lightning-pose/lightning_pose/apps``, run:

.. code-block:: console

    streamlit run video_diagnostics.py -- --model_dir <ABOLUTE_PATH_TO_HYDRA_OUTPUTS_DIRECTORY>

where ``--model_dir`` is explained above.

The app shows:

* timeseries of predictions/confidences/losses of a selected keypoint (x and y coordinate) for each network
* boxplot/histogram of confidences/losses for each network and each body part