/icevision-streamlit

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

IceVision Streamlit App

Pre-requisites

You need to have the IceVision package already installed in order to run the IceVision Streamlit App. You can either install the [inference] or [all]packages. The [inference] option is recommended if we are only interested in getting the predictions (inference) as opposed to training models.

For [inference] the packages option:

pip install icevision[inference] icedata

For [all] the packages option:

pip install icevision[all] icedata

Streamlit Local Installation

From your the project root directory , run the following command:

pip install streamlit

In your termilal, press Ctrl+Click on the URL to open the app in your browser, or open the following URL in your browser: http://localhost:8501/.

Drag & Drop an image in the grey zone. The predicted image will appear after a few seconds. Use the controls on the left to adjust the thresholds as well the masks attributes (label, bounding boxes, masks)


Running the Streamlit App from the Githup Repo

Install the streamlit package if it has not be done yet by running the following command:

streamlit run https://raw.githubusercontent.com/airctic/icevision-streamlit/master/app.py

In your termilal, press Ctrl+Click on the URL to open the app in your browser, or open the following URL in your browser: http://localhost:8501/

Drag & Drop an image in the grey zone. The predicted image will appear after a few seconds. Use the controls on the left to adjust the thresholds as well the masks attributes (label, bounding boxes, masks)

Running the Streamlit App on a Local Machine

streamlit run app.py

Press Ctrl+Click on the http://192.168.2.11:8501 URL to open the app in your local browser


Docker

Building Docker Image

You might choose your own tag name instead of app:latest

docker build -f Dockerfile -t ice-st:latest .

Running the Docker Image

docker run -p 8501:8501 ice-st:latest

Important: Your container will be available on http://localhost:8501/

Note: Make sure no other app is running on port 8501

Drag & Drop an image in the grey zone. The predicted image will appear after a few seconds. Use the controls on the left to adjust the thresholds as well the masks attributes (label, bounding boxes, masks)