proper fast api suage
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backend/.gitignore
vendored
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backend/.gitignore
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# osm-cache
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cache/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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@ -1,14 +1,12 @@
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[[source]]
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url = "https://pypi.org/simple"
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verify_ssl = true
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name = "pypi"
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[packages]
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numpy = "*"
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scipy = "*"
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fastapi = "*"
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[dev-packages]
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[requires]
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python_version = "3.12"
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[[source]]
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url = "https://pypi.org/simple"
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verify_ssl = true
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name = "pypi"
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[packages]
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numpy = "*"
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scipy = "*"
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fastapi = "*"
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osmpythontools = "*"
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[dev-packages]
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2369
backend/Pipfile.lock
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backend/Pipfile.lock
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File diff suppressed because it is too large
Load Diff
@ -1,34 +1,34 @@
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from src.optimizer import solve_optimization
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from src.landmarks_manager import Landmark
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from fastapi import FastAPI
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app = FastAPI()
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@app.get("/optimize/{max_steps}/{print_details}")
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def main(max_steps: int, print_details: bool):
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# CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
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#max_steps = 16
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# Initialize all landmarks (+ start and goal). Order matters here
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landmarks = []
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landmarks.append(Landmark("départ", -1, (0, 0)))
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landmarks.append(Landmark("tour eiffel", 99, (0,2))) # PUT IN JSON
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landmarks.append(Landmark("arc de triomphe", 99, (0,4)))
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landmarks.append(Landmark("louvre", 99, (0,6)))
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landmarks.append(Landmark("montmartre", 99, (0,10)))
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landmarks.append(Landmark("concorde", 99, (0,8)))
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landmarks.append(Landmark("arrivée", -1, (0, 0)))
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visiting_order = solve_optimization(landmarks, max_steps, print_details)
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#return visiting_order
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return("max steps :", max_steps, "\n", visiting_order)
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if __name__ == "__main__":
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main()
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from .src.optimizer import solve_optimization
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from .src.landmarks_manager import Landmark
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from fastapi import FastAPI
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app = FastAPI()
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@app.get("/optimize/{max_steps}/{print_details}")
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def main(max_steps: int, print_details: bool):
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# CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
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#max_steps = 16
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# Initialize all landmarks (+ start and goal). Order matters here
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landmarks = []
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landmarks.append(Landmark("départ", -1, (0, 0)))
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landmarks.append(Landmark("tour eiffel", 99, (0,2))) # PUT IN JSON
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landmarks.append(Landmark("arc de triomphe", 99, (0,4)))
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landmarks.append(Landmark("louvre", 99, (0,6)))
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landmarks.append(Landmark("montmartre", 99, (0,10)))
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landmarks.append(Landmark("concorde", 99, (0,8)))
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landmarks.append(Landmark("arrivée", -1, (0, 0)))
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visiting_order = solve_optimization(landmarks, max_steps, print_details)
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#return visiting_order
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return("max steps :", max_steps, "\n", visiting_order)
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"""if __name__ == "__main__":
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main()"""
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from OSMPythonTools.api import Api
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from OSMPythonTools.overpass import Overpass
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from dataclasses import dataclass
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# Defines the landmark class (aka some place there is to visit)
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@dataclass
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class Landmark :
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name : str
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attractiveness : int
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id : int
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# Converts a OSM id to a landmark
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def add_from_id(id: int, score: int) :
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try :
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s = 'way/' + str(id) # prepare string for query
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obj = api.query(s) # object to add
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except :
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s = 'relation/' + str(id) # prepare string for query
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obj = api.query(s) # object to add
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return Landmark(obj.tag('name:fr'), score, id) # create Landmark out of it
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# take a lsit of tuples (id, score) to generate a list of landmarks
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def generate_landmarks(ids_and_scores: list) :
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L = []
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for tup in ids_and_scores :
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L.append(add_from_id(tup[0], tup[1]))
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return L
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api = Api()
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l = (7515426, 70)
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t = (5013364, 100)
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n = (201611261, 99)
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a = (226413508, 50)
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m = (23762981, 30)
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ids_and_scores = [t, l, n, a, m]
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landmarks = generate_landmarks(ids_and_scores)
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for obj in landmarks :
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from OSMPythonTools.api import Api
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from OSMPythonTools.overpass import Overpass
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from dataclasses import dataclass
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# Defines the landmark class (aka some place there is to visit)
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@dataclass
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class Landmarkkkk :
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name : str
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attractiveness : int
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id : int
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@dataclass
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class Landmark :
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name : str
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attractiveness : int
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loc : tuple
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# Converts a OSM id to a landmark
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def add_from_id(id: int, score: int) :
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try :
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s = 'way/' + str(id) # prepare string for query
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obj = api.query(s) # object to add
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except :
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s = 'relation/' + str(id) # prepare string for query
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obj = api.query(s) # object to add
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return Landmarkkkk(obj.tag('name:fr'), score, id) # create Landmark out of it
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# take a lsit of tuples (id, score) to generate a list of landmarks
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def generate_landmarks(ids_and_scores: list) :
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L = []
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for tup in ids_and_scores :
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L.append(add_from_id(tup[0], tup[1]))
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return L
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api = Api()
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l = (7515426, 70)
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t = (5013364, 100)
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n = (201611261, 99)
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a = (226413508, 50)
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m = (23762981, 30)
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ids_and_scores = [t, l, n, a, m]
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landmarks = generate_landmarks(ids_and_scores)
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for obj in landmarks :
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print(obj)
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import fastapi
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from dataclasses import dataclass
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@dataclass
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class Destination:
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name: str
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location: tuple
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attractiveness: int
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d = Destination()
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def get_route() -> list[Destination]:
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return {"route": "Hello World"}
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endpoint = ("/get_route", get_route)
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end
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if __name__ == "__main__":
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fastapi.run()
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import fastapi
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from dataclasses import dataclass
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@dataclass
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class Destination:
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name: str
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location: tuple
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attractiveness: int
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d = Destination()
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def get_route() -> list[Destination]:
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return {"route": "Hello World"}
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endpoint = ("/get_route", get_route)
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end
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if __name__ == "__main__":
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fastapi.run()
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