Feature/pre-commit hook and pdm integration

backend/.gitignore

@@ -0,0 +1,162 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm-project.org/#use-with-ide
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

backend/.pre-commit-config.yaml

@@ -0,0 +1,23 @@
+repos:
+
+  # Versioning: Commit messages & changelog
+  - repo: https://github.com/commitizen-tools/commitizen
+    rev: v3.27.0
+    hooks:
+      - id: commitizen
+        stages: [commit-msg]
+
+  # Lint / autoformat: Python code
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: "v0.4.8"
+    hooks:
+      - id: ruff
+      - id: ruff-format
+
+  # Trailing whitespace
+  -   repo: https://github.com/pre-commit/pre-commit-hooks
+      rev: v2.3.0
+      hooks:
+      -   id: check-yaml
+      -   id: end-of-file-fixer
+      -   id: trailing-whitespace

backend/README.md

@@ -3,7 +3,7 @@
 ## Backend is created with [Django](https://www.djangoproject.com/)
 This project was bootstrapped with  [Geodjango Template](https://github.com/itskshitiz321/geodjangotemplate.git)
 #### For Quickly Getting Started
-**Note:** Depending upon your OS and Env installation will vary, This project tightly depends on [Tensorflow](https://www.tensorflow.org/install/pip) with GPU support so accordingly build your development environment 
+**Note:** Depending upon your OS and Env installation will vary, This project tightly depends on [Tensorflow](https://www.tensorflow.org/install/pip) with GPU support so accordingly build your development environment
 ### Install Python3, pip and virtualenv first
 ##### Skip this, step if you already have one
     sudo apt-get install python3
@@ -19,13 +19,13 @@ This project was bootstrapped with  [Geodjango Template](https://github.com/itsk
 sudo apt-get install git-lfs
 ```
 
-- Clone Ramp Basemodel 
+- Clone Ramp Basemodel
 ```
 git clone https://github.com/radiantearth/model_ramp_baseline.git
 ```
 
-- Clone Ramp - Code 
-Note: This clone location will be your RAMP_HOME 
+- Clone Ramp - Code
+Note: This clone location will be your RAMP_HOME
 ```
 git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code
 ```
@@ -36,19 +36,19 @@ cp -r model_ramp_baseline/data/input/checkpoint.tf ramp-code/ramp/checkpoint.tf
 ```
 
 
-- Remove basemodel repo we don't need it anymore 
+- Remove basemodel repo we don't need it anymore
 ```
 rm -rf model_ramp_baseline
 ```
-- Install numpy 
-Numpy needs to be installed before gdal 
+- Install numpy
+Numpy needs to be installed before gdal
 ```
 pip install numpy==1.23.5
 ```
 
-- Install gdal and rasetrio 
-Based on your env : You can either use conda / setup manually on your os 
-for eg on ubuntu : 
+- Install gdal and rasetrio
+Based on your env : You can either use conda / setup manually on your os
+for eg on ubuntu :
 ```
 sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
 sudo apt-get install gdal-bin
@@ -58,12 +58,12 @@ export C_INCLUDE_PATH=/usr/include/gdal
 pip install --global-option=build_ext --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version`
 ```
 
-- Install Ramp - Dependecies 
+- Install Ramp - Dependecies
 ```
 cd ramp-code && cd colab && make install
 ```
 
-- For Conda users : You may need to install rtree, gdal , rasterio & imagecodecs separately 
+- For Conda users : You may need to install rtree, gdal , rasterio & imagecodecs separately
 
 ```
 conda install -c conda-forge rtree
@@ -82,14 +82,14 @@ conda install -c conda-forge imagecodecs
 pip install --upgrade setuptools
 ```
 
-- Install fAIr Utilities 
+- Install fAIr Utilities
 ```
 pip install hot-fair-utilities==1.0.41
 ```
 
-**Remember In order to run fAIr , You need to configure your PC with tensorflow - GPU Support** 
+**Remember In order to run fAIr , You need to configure your PC with tensorflow - GPU Support**
 
-You can check your GPU by : 
+You can check your GPU by :
 
 ```
 import tensorflow as tf
@@ -98,82 +98,88 @@ print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('
 
 
 - Install psycopg2
-Again based on your os/env you can do manual installation 
-for eg : on ubuntu : 
+Again based on your os/env you can do manual installation
+for eg : on ubuntu :
 ```
 sudo apt-get install python3-psycopg2
 ```
 
-- Install redis server on your pc 
+- Install redis server on your pc
 
 ```
 sudo apt install redis
 ```
 
-- Finally installl pip dependencies 
+- Install pdm for dependency management
 
 ```
-pip install -r requirements.txt
+pip install pdm
+```
+
+- Finally install project dependencies
+
+```
+pdm install
 ```
 
 ### Make sure you have postgresql installed with postgis extension enabled
 
 
-#### Configure .env: 
+#### Configure .env:
     Create .env in the root backend project , and add the credentials as provided on .env_sample , Export your secret key and database url to your env
 
-    Export your database url 
+    Export your database url
     ```
     export DATABASE_URL=postgis://postgres:postgres@localhost:5432/ai
     ```
-    
-    You will need more env variables (Such as Ramp home, Training Home) that can be found on ```.sample_env```  
+
+    You will need more env variables (Such as Ramp home, Training Home) that can be found on ```.sample_env```
 
 #### Now change your username, password and db name in settings.py accordingly to your database
     python manage.py makemigrations login core
     python manage.py migrate
     python manage.py runserver
-### Now server will be available in your 8000 port on web, you can check out your localhost:8000/admin for admin panel 
+### Now server will be available in your 8000 port on web, you can check out your localhost:8000/admin for admin panel
 To login on admin panel, create your superuser and login with your credentials restarting the server
 
     python manage.py createsuperuser
 
-## Authentication 
+## Authentication
 fAIr uses oauth2.0 Authentication using [osm-login-python](https://github.com/kshitijrajsharma/osm-login-python)
 1. Get your login Url
     Hit ```/api/v1/auth/login/ ```
-    - URL will give you login URL which you can use to provide your osm credentials and authorize fAIr 
+    - URL will give you login URL which you can use to provide your osm credentials and authorize fAIr
     - After successful login  you will get access-token that you can use across all osm login required endpoints in fAIr
-2. Check authentication by getting back your data 
+2. Check authentication by getting back your data
     Hit ```/api/v1/auth/me/```
-    - URL requires access-token as header and in return you will see your osm username, id and image url 
+    - URL requires access-token as header and in return you will see your osm username, id and image url
 
 
-## Start celery workers 
+## Start celery workers
 
--  Start celery workers 
+-  Start celery workers
 
 ```
 celery -A aiproject worker --loglevel=debug -n my_worker
 ```
 
-- Monitor using flower 
-if  you are using redis as result backend, api supports both options django / redis 
+- Monitor using flower
+if  you are using redis as result backend, api supports both options django / redis
 You can start flower to start monitoring your tasks
 ```
-celery -A aiproject  --broker=redis://127.0.0.1:6379/0 flower 
+celery -A aiproject  --broker=redis://127.0.0.1:6379/0 flower
 ```
 
-## Run Tests 
+## Run Tests
 
 ```
 python manage.py test
 ```
 
 
-# Build fAIr with Docker for Development 
-- Install all the required drivers for your graphics to access it from containers, and check your graphics and drivers with ```nvidia-smi``` . Up to now only nvidia is Supported 
-- Follow docker_sample_env to create ```.env``` file in your dir 
+# Build fAIr with Docker for Development
+- Install all the required drivers for your graphics to access it from containers, and check your graphics and drivers with ```nvidia-smi``` . Up to now only nvidia is Supported
+- Follow docker_sample_env to create ```.env``` file in your dir
 - Build the Image
 
 ```