proper fast api suage
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This commit is contained in:
Kilian Scheidecker 2024-05-29 18:14:40 +02:00
parent 99dc8b5e67
commit ae9860cc8e
6 changed files with 1774 additions and 1006 deletions

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backend/.gitignore vendored Normal file
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# osm-cache
cache/
# 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.fming.dev/latest/usage/project/#working-with-version-control
.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/

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[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
numpy = "*"
scipy = "*"
fastapi = "*"
[dev-packages]
[requires]
python_version = "3.12"
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
numpy = "*"
scipy = "*"
fastapi = "*"
osmpythontools = "*"
[dev-packages]

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backend/Pipfile.lock generated

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from src.optimizer import solve_optimization
from src.landmarks_manager import Landmark
from fastapi import FastAPI
app = FastAPI()
@app.get("/optimize/{max_steps}/{print_details}")
def main(max_steps: int, print_details: bool):
# CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
#max_steps = 16
# Initialize all landmarks (+ start and goal). Order matters here
landmarks = []
landmarks.append(Landmark("départ", -1, (0, 0)))
landmarks.append(Landmark("tour eiffel", 99, (0,2))) # PUT IN JSON
landmarks.append(Landmark("arc de triomphe", 99, (0,4)))
landmarks.append(Landmark("louvre", 99, (0,6)))
landmarks.append(Landmark("montmartre", 99, (0,10)))
landmarks.append(Landmark("concorde", 99, (0,8)))
landmarks.append(Landmark("arrivée", -1, (0, 0)))
visiting_order = solve_optimization(landmarks, max_steps, print_details)
#return visiting_order
return("max steps :", max_steps, "\n", visiting_order)
if __name__ == "__main__":
main()
from .src.optimizer import solve_optimization
from .src.landmarks_manager import Landmark
from fastapi import FastAPI
app = FastAPI()
@app.get("/optimize/{max_steps}/{print_details}")
def main(max_steps: int, print_details: bool):
# CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
#max_steps = 16
# Initialize all landmarks (+ start and goal). Order matters here
landmarks = []
landmarks.append(Landmark("départ", -1, (0, 0)))
landmarks.append(Landmark("tour eiffel", 99, (0,2))) # PUT IN JSON
landmarks.append(Landmark("arc de triomphe", 99, (0,4)))
landmarks.append(Landmark("louvre", 99, (0,6)))
landmarks.append(Landmark("montmartre", 99, (0,10)))
landmarks.append(Landmark("concorde", 99, (0,8)))
landmarks.append(Landmark("arrivée", -1, (0, 0)))
visiting_order = solve_optimization(landmarks, max_steps, print_details)
#return visiting_order
return("max steps :", max_steps, "\n", visiting_order)
"""if __name__ == "__main__":
main()"""

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from OSMPythonTools.api import Api
from OSMPythonTools.overpass import Overpass
from dataclasses import dataclass
# Defines the landmark class (aka some place there is to visit)
@dataclass
class Landmark :
name : str
attractiveness : int
id : int
# Converts a OSM id to a landmark
def add_from_id(id: int, score: int) :
try :
s = 'way/' + str(id) # prepare string for query
obj = api.query(s) # object to add
except :
s = 'relation/' + str(id) # prepare string for query
obj = api.query(s) # object to add
return Landmark(obj.tag('name:fr'), score, id) # create Landmark out of it
# take a lsit of tuples (id, score) to generate a list of landmarks
def generate_landmarks(ids_and_scores: list) :
L = []
for tup in ids_and_scores :
L.append(add_from_id(tup[0], tup[1]))
return L
api = Api()
l = (7515426, 70)
t = (5013364, 100)
n = (201611261, 99)
a = (226413508, 50)
m = (23762981, 30)
ids_and_scores = [t, l, n, a, m]
landmarks = generate_landmarks(ids_and_scores)
for obj in landmarks :
from OSMPythonTools.api import Api
from OSMPythonTools.overpass import Overpass
from dataclasses import dataclass
# Defines the landmark class (aka some place there is to visit)
@dataclass
class Landmarkkkk :
name : str
attractiveness : int
id : int
@dataclass
class Landmark :
name : str
attractiveness : int
loc : tuple
# Converts a OSM id to a landmark
def add_from_id(id: int, score: int) :
try :
s = 'way/' + str(id) # prepare string for query
obj = api.query(s) # object to add
except :
s = 'relation/' + str(id) # prepare string for query
obj = api.query(s) # object to add
return Landmarkkkk(obj.tag('name:fr'), score, id) # create Landmark out of it
# take a lsit of tuples (id, score) to generate a list of landmarks
def generate_landmarks(ids_and_scores: list) :
L = []
for tup in ids_and_scores :
L.append(add_from_id(tup[0], tup[1]))
return L
api = Api()
l = (7515426, 70)
t = (5013364, 100)
n = (201611261, 99)
a = (226413508, 50)
m = (23762981, 30)
ids_and_scores = [t, l, n, a, m]
landmarks = generate_landmarks(ids_and_scores)
for obj in landmarks :
print(obj)

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import fastapi
from dataclasses import dataclass
@dataclass
class Destination:
name: str
location: tuple
attractiveness: int
d = Destination()
def get_route() -> list[Destination]:
return {"route": "Hello World"}
endpoint = ("/get_route", get_route)
end
if __name__ == "__main__":
fastapi.run()
import fastapi
from dataclasses import dataclass
@dataclass
class Destination:
name: str
location: tuple
attractiveness: int
d = Destination()
def get_route() -> list[Destination]:
return {"route": "Hello World"}
endpoint = ("/get_route", get_route)
end
if __name__ == "__main__":
fastapi.run()