| Name | flask-reqcheck JSON |
| Version |
0.0.10
JSON |
| download |
| home_page | None |
| Summary | Request validation for Flask applications using Pydantic. |
| upload_time | 2024-09-09 19:03:52 |
| maintainer | None |
| docs_url | None |
| author | Lewis Dunne |
| requires_python | >=3.11 |
| license | None |
| keywords |
python
flask
validation
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
No coveralls.
|
# Flask-Reqcheck
**Flask-Reqcheck** lets you validate requests in your Flask applications. With a simple
decorator and some [Pydantic](https://docs.pydantic.dev/latest/) models, you can quickly
validate incoming request bodies, query parameters, and url path parameters, reducing
boilerplate code and minimizing errors.
## Installation
Run the following (preferably inside a virtual environment):
```sh
pip install flask-reqcheck
```
## Usage
Flask-Reqcheck is very straightforward to use. The main two objects of interest are the `@validate` decorator and the `get_valid_request` function.
The `validate` decorator is for annotating flask route functions. When you do this, you provide a Pydantic model for the components of the HTTP
request that you would like to validate, such as `body`, `query`, `path`, etc. If the request inputs fail to match the corresponding model then
a HTTP error is raised.
Aside from `@validate`, you can use the more specific decorators - `@validate_body`, `@validate_form`, `@validate_path`,
`@validate_query`, etc (see the API reference).
The `get_valid_request` is a helper function for use *within* the Flask route function. When using `@validate`, a new instance of the `ValidRequest` class
will be created and stored for the current request context. We can use `get_valid_request` to retrieve that object and access its attributes, which correspond
to the different HTTP components that were validated.
For example:
```python
from flask_reqcheck import validate, get_valid_request
from pydantic import BaseModel
# Write a class (with Pydantic) to represent the expected data
class BodyModel(BaseModel):
a: str
b: int
c: float
d: uuid.UUID
arr: list[int]
@app.post("/body")
@validate(body=BodyModel) # Decorate the view function
def request_with_body():
vreq = get_valid_request() # Access the validated data
return vreq.to_dict()
```
First, we import `validate` and `get_valid_request` from Flask-Reqcheck. Then we create a custom model using Pydantic’s `BaseClass` - in this example, it is a simple model for the expected request body. Then you annotate the Flask route function with `@validate`, providing our model of the request body. Finally, within our route function’s logic, we access the instance of the `ValidRequest` class and assign it to a variable using `vreq = get_valid_request()`. We could then call `print(vreq.body)` to obtain the instance of our request body model.
More specific decorators can also be used:
- `@validate_body`
- `@validate_form`
- `@validate_path`
- `@validate_query`
More to come.
For a full example, see the [examples directory in the Flask-Reqcheck repository](/example/).
## Contributing
Clone the repo, pip install locally, then make the changes. Please do the following:
- Branch off of the `develop` branch to work on changes
- Use the `/feature/{feature name}` or `/bugfix/{bugfix name}` format for branch names
- PR from your branch into `develop`
- Use the [Black](https://black.readthedocs.io/en/stable/) formatter along with [isort](https://pycqa.github.io/isort/) to keep the codebase clean. Before making a PR:
- `python -m black .`
- `python -m isort .`
- Update the docs where necessary - the `make html` command from the `/docs` directory might be enough for minor changes. Otherwise, see how they are structured and make changes accordingly. Given that the focus is on just a few functions, the rest of the API is only documented in the code itself so it might not be necessary to include that in the main docs html.
- Use `numpy` style docstrings
- Write tests - PRs will fail if the tests fail
## License
MIT
Raw data
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"description": "# Flask-Reqcheck\n\n**Flask-Reqcheck** lets you validate requests in your Flask applications. With a simple \ndecorator and some [Pydantic](https://docs.pydantic.dev/latest/) models, you can quickly \nvalidate incoming request bodies, query parameters, and url path parameters, reducing \nboilerplate code and minimizing errors.\n\n## Installation\n\nRun the following (preferably inside a virtual environment):\n\n```sh\npip install flask-reqcheck\n```\n\n## Usage\n\nFlask-Reqcheck is very straightforward to use. The main two objects of interest are the `@validate` decorator and the `get_valid_request` function.\n\nThe `validate` decorator is for annotating flask route functions. When you do this, you provide a Pydantic model for the components of the HTTP \nrequest that you would like to validate, such as `body`, `query`, `path`, etc. If the request inputs fail to match the corresponding model then \na HTTP error is raised. \n\nAside from `@validate`, you can use the more specific decorators - `@validate_body`, `@validate_form`, `@validate_path`, \n`@validate_query`, etc (see the API reference).\n\nThe `get_valid_request` is a helper function for use *within* the Flask route function. When using `@validate`, a new instance of the `ValidRequest` class \nwill be created and stored for the current request context. We can use `get_valid_request` to retrieve that object and access its attributes, which correspond \nto the different HTTP components that were validated.\n\nFor example:\n\n```python\nfrom flask_reqcheck import validate, get_valid_request\nfrom pydantic import BaseModel\n\n# Write a class (with Pydantic) to represent the expected data\nclass BodyModel(BaseModel):\n a: str\n b: int\n c: float\n d: uuid.UUID\n arr: list[int]\n\n@app.post(\"/body\")\n@validate(body=BodyModel) # Decorate the view function\ndef request_with_body():\n vreq = get_valid_request() # Access the validated data\n return vreq.to_dict()\n```\n\nFirst, we import `validate` and `get_valid_request` from Flask-Reqcheck. Then we create a custom model using Pydantic\u2019s `BaseClass` - in this example, it is a simple model for the expected request body. Then you annotate the Flask route function with `@validate`, providing our model of the request body. Finally, within our route function\u2019s logic, we access the instance of the `ValidRequest` class and assign it to a variable using `vreq = get_valid_request()`. We could then call `print(vreq.body)` to obtain the instance of our request body model.\n\nMore specific decorators can also be used:\n- `@validate_body`\n- `@validate_form`\n- `@validate_path`\n- `@validate_query`\n\nMore to come.\n\nFor a full example, see the [examples directory in the Flask-Reqcheck repository](/example/).\n\n## Contributing\n\nClone the repo, pip install locally, then make the changes. Please do the following:\n\n- Branch off of the `develop` branch to work on changes\n- Use the `/feature/{feature name}` or `/bugfix/{bugfix name}` format for branch names\n- PR from your branch into `develop`\n- Use the [Black](https://black.readthedocs.io/en/stable/) formatter along with [isort](https://pycqa.github.io/isort/) to keep the codebase clean. Before making a PR:\n - `python -m black .`\n - `python -m isort .`\n- Update the docs where necessary - the `make html` command from the `/docs` directory might be enough for minor changes. Otherwise, see how they are structured and make changes accordingly. Given that the focus is on just a few functions, the rest of the API is only documented in the code itself so it might not be necessary to include that in the main docs html.\n- Use `numpy` style docstrings\n- Write tests - PRs will fail if the tests fail\n\n\n## License\n\nMIT\n",
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