README.md

---
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