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---
title: Natsar Demo
emoji: "🚀"
colorFrom: red
colorTo: red
sdk: docker
pinned: false
hardware: gpu-t4-small
---
# Introduction
Computer Vision object detection for National Search and Rescue (NATSAR)
## Prerequisites
1. Install conda (environment management) using terminal on VScode
- for mac user [https://www.anaconda.com/docs/getting-started/miniconda/main]
- for window user [https://www.anaconda.com/docs/getting-started/miniconda/main]
2. Create env from conda by using the following command
`conda create --name natsar python=3.11`
it will create `natsar` (can be diffent name) environtment for this project.
and it is also a good practice to create separate environtment for specific project.
3. Activate the environment
`conda activate natsar`
4. Install PDM (package and dependency manager) to avoid conflict dependency
`pip install pdm`
sometimes `conda` doesn't support some libraries, then `pip` will be allowed to do. BUT use pip within the `natsar` env.
5. Intstall packages and dependencies
hello
`pdm install`
## Running the project locally
after install dependencies, make sure to activate the environment
1. go to folder src using `cd src` on terminal
2. run `app.py` file using `pdm run streamlit run app.py`
\*\*if cloning from huggingface it might need to mount large file with git lfs
use `pip install git-lfs` then `git lfs install`
then `git lfs pull` to pull the files to local and `pdm run streamlit run app.py` to run
## Build and Test
- Main app.py file to be placed at root of NATSAR-DEMO repo.
- The app to point to different models that sit within the nominated sub-folders
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