Switch to using pyproject for dependencies

[mrchrisadams]

This pull request includes several changes across different files to improve code quality, functionality, and configuration. The most important changes include the removal of an unused fixture, modifications to the fake_evidence function, updates to import statements, the addition of a justfile for task automation, and the introduction of a pyproject.toml for project configuration.

Codebase simplification:

• Removed the mock_open fixture from apps/accounts/tests/test_provider_request.py as it was not being used.
• Updated the fake_evidence function in apps/accounts/tests/test_provider_request.py to use io.BytesIO for file creation.
• Removed the mock_open parameter from the test_evidence_validation_fails function in apps/accounts/tests/test_provider_request.py.

Code improvements:

• Consolidated import statements in apps/greencheck/api/views.py to improve readability.
• Replaced the deprecated length_is filter with length in several HTML templates to ensure compatibility with newer Django versions. [1] [2] [3]

Configuration and dependencies:

• Added a justfile to automate common development tasks, such as creating a virtual environment, running tests, and deploying releases.
• Introduced a pyproject.toml file for managing project dependencies and configurations, replacing requirements/requirements.in and requirements/requirements.dev.in. [1] [2] [3]

[github-actions]

Old Energy Estimation

Eco-CI Output:

Label	🖥 avg. CPU utilization [%]	🔋 Total Energy [Joules]	🔌 avg. Power [Watts]	Duration [Seconds]
Total Run (incl. overhead)	20.0104	556.571	3.41	163
checkout	7.25	3.53279	1.77	2
pip install uv wheel	21.975	15.9429	3.99	4
pip install requirements	66.212	32.0013	6.40	5
pytest	18.3221	505.094	3.41	148

🌳 CO2 Data:
City: San Jose, Lat: 37.1835, Lon: -121.7714
IP: 13.83.1.210
CO₂ from energy is: 0.155839880 g
CO₂ from manufacturing (embodied carbon) is: 0.046506151 g
Carbon Intensity for this location: 280 gCO₂eq/kWh
SCI: 0.202346 gCO₂eq / pipeline run emitted

[github-actions]

Old Energy Estimation

Eco-CI Output:

Label	🖥 avg. CPU utilization [%]	🔋 Total Energy [Joules]	🔌 avg. Power [Watts]	Duration [Seconds]
Total Run (incl. overhead)	16.0354	655.879	3.20	205
checkout	10.93	3.66295	3.66	1
pip install uv wheel	11.03	3.66521	1.83	2
pip install requirements	67.906	32.5452	5.42	6
pytest	14.64	616.006	3.18	194

🌳 CO2 Data:
City: Boydton, Lat: 36.6676, Lon: -78.3875
IP: 20.161.78.56
CO₂ from energy is: 0.218407707 g
CO₂ from manufacturing (embodied carbon) is: 0.058489331 g
Carbon Intensity for this location: 333 gCO₂eq/kWh
SCI: 0.276897 gCO₂eq / pipeline run emitted

[github-actions]

Old Energy Estimation

Eco-CI Output:

Label	🖥 avg. CPU utilization [%]	🔋 Total Energy [Joules]	🔌 avg. Power [Watts]	Duration [Seconds]
Total Run (incl. overhead)	22.7717	482.319	3.57	135
checkout	15.34	3.81398	3.81	1
pip install uv wheel	15.39	3.81313	3.81	1
pip install requirements	64.74	25.2792	5.06	5
pytest	20.9575	449.413	3.62	124

🌳 CO2 Data:
City: Boydton, Lat: 36.6676, Lon: -78.3875
IP: 20.161.77.192
CO₂ from energy is: 0.160129908 g
CO₂ from manufacturing (embodied carbon) is: 0.038517364 g
Carbon Intensity for this location: 332 gCO₂eq/kWh
SCI: 0.198647 gCO₂eq / pipeline run emitted

[github-actions]

Old Energy Estimation

Eco-CI Output:

Label	🖥 avg. CPU utilization [%]	🔋 Total Energy [Joules]	🔌 avg. Power [Watts]	Duration [Seconds]
Total Run (incl. overhead)	20.5854	526.042	3.51	150
checkout	6.42	3.50829	1.75	2
pip install uv wheel	6.51	3.50668	1.75	2
pip install requirements	66.204	31.8792	5.31	6
pytest	19.1266	487.147	3.50	139

🌳 CO2 Data:
City: Washington, Lat: 38.7095, Lon: -78.1539
IP: 20.84.127.29
CO₂ from energy is: 0.174645944 g
CO₂ from manufacturing (embodied carbon) is: 0.042797071 g
Carbon Intensity for this location: 332 gCO₂eq/kWh
SCI: 0.217443 gCO₂eq / pipeline run emitted

[github-actions]

Old Energy Estimation

Eco-CI Output:

Label	🖥 avg. CPU utilization [%]	🔋 Total Energy [Joules]	🔌 avg. Power [Watts]	Duration [Seconds]
Total Run (incl. overhead)	18.7933	575.521	3.35	172
checkout	13.5	3.75786	3.76	1
pip install uv wheel	13.58	3.75579	1.88	2
pip install requirements	65.035	25.5319	4.26	6
pytest	17.1841	542.475	3.37	161

🌳 CO2 Data:
City: Boydton, Lat: 36.6676, Lon: -78.3875
IP: 20.161.78.238
CO₂ from energy is: 0.191072972 g
CO₂ from manufacturing (embodied carbon) is: 0.049073975 g
Carbon Intensity for this location: 332 gCO₂eq/kWh
SCI: 0.240147 gCO₂eq / pipeline run emitted

[github-actions]

Old Energy Estimation

Eco-CI Output:

Label	🖥 avg. CPU utilization [%]	🔋 Total Energy [Joules]	🔌 avg. Power [Watts]	Duration [Seconds]
Total Run (incl. overhead)	18.5105	602.723	3.35	180
checkout	7.54	3.54493	3.54	1
pip install uv wheel	7.64	3.54705	1.77	2
pip install requirements	68.242	33.0471	5.51	6
pytest	17.0392	562.583	3.33	169

🌳 CO2 Data:
City: Boydton, Lat: 36.6676, Lon: -78.3875
IP: 20.109.36.208
CO₂ from energy is: 0.200104036 g
CO₂ from manufacturing (embodied carbon) is: 0.051356485 g
Carbon Intensity for this location: 332 gCO₂eq/kWh
SCI: 0.251461 gCO₂eq / pipeline run emitted

[github-actions]

Eco-CI Output:

Label	🖥 avg. CPU utilization [%]	🔋 Total Energy [Joules]	🔌 avg. Power [Watts]	Duration [Seconds]
Total Run (incl. overhead)	17.0872	629.756	3.26	193
checkout	7.52	3.54328	1.77	2
pip install uv wheel	7.61	3.54394	3.54	1
pip install requirements	67.026	32.3173	6.46	5
pytest	15.7285	590.351	3.26	181

🌳 CO2 Data:
City: Washington, Lat: 38.7095, Lon: -78.1539
IP: 40.76.117.196
CO₂ from energy is: 0.209078992 g
CO₂ from manufacturing (embodied carbon) is: 0.055065565 g
Carbon Intensity for this location: 332 gCO₂eq/kWh
SCI: 0.264145 gCO₂eq / pipeline run emitted