# Freak
Control.
Control your application state with a single line of code.
Freak is using `pydantic` to define the state, supports nested models, partial updates, data validation, and uses `FastAPI` to serve the state over HTTP.
## Installation
```shell
pip install freak
```
## Usage
Define a `pydantic` model and pass it to the `control` function.
```python
from freak import control
from pydantic import BaseModel
class State(BaseModel):
foo: str = "bar"
state = State()
control(state)
```
The `state` object will now be automatically served over HTTP.
Freak generates `/get/<field>` and `/set/<field>` endpoints for each field in the model, as well as the following endpoints for the root state object:
- `/get` (`GET`)
- `/set` (`PATCH`)
- `/reset` (`DELETE`)
- `/get_from_path` (`GET`) - which allows to get a value from the state using dot-notation (like `my.inner.field.`)
The `foo` field can now be modified externally by sending a PUT request to the Freak server, which has been automatically started in the background:
```shell
curl -X PUT localhost:4444/set/foo?value=baz
```
At the same time, the `state` object cat be used in the program. Freak will always modify it in place. This can be helpful for long-running programs that need to be controlled externally, like:
- training a neural network
- running a bot
- etc.
Freak supports nested models and partial updates. Consider the following model:
```python
from pydantic import BaseModel
class Bar(BaseModel):
foo: str = "bar"
baz: str = "qux"
class State(BaseModel):
bar: Bar = Bar()
```
Freak will generate `put` endpoints for the `foo` and `baz` fields, and a `patch` endpoint for the `bar` field (as it's a `pydantic` model itself). This `patch` endpoint supports partial updates:
```shell
curl -X PATCH localhost:4444/set/bar -d '{"foo": "baz"}'
```
Because Freak is using `FastAPI`, it's possible to use auto-generated documentation to interact with the Freak server. The interactive documentation can be accessed at Freak's main endpoint, which by default is `localhost:4444`.
The following screenshot shows the generated endpoints for the DL [example](https://github.com/danielgafni/freak/blob/master/examples/dl_example.py):
## Development
### Installation
```shell
poetry install
poetry run pre-commit install
```
### Testing
```shell
poetry run pytest
```
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"description": "# Freak\n\nControl.\n\nControl your application state with a single line of code.\n\nFreak is using `pydantic` to define the state, supports nested models, partial updates, data validation, and uses `FastAPI` to serve the state over HTTP.\n\n## Installation\n```shell\npip install freak\n```\n\n## Usage\n\nDefine a `pydantic` model and pass it to the `control` function.\n\n```python\nfrom freak import control\nfrom pydantic import BaseModel\n\nclass State(BaseModel):\n foo: str = \"bar\"\n\nstate = State()\ncontrol(state)\n```\n\nThe `state` object will now be automatically served over HTTP.\n\nFreak generates `/get/<field>` and `/set/<field>` endpoints for each field in the model, as well as the following endpoints for the root state object:\n - `/get` (`GET`)\n - `/set` (`PATCH`)\n - `/reset` (`DELETE`)\n - `/get_from_path` (`GET`) - which allows to get a value from the state using dot-notation (like `my.inner.field.`)\n\nThe `foo` field can now be modified externally by sending a PUT request to the Freak server, which has been automatically started in the background:\n\n```shell\ncurl -X PUT localhost:4444/set/foo?value=baz\n```\n\nAt the same time, the `state` object cat be used in the program. Freak will always modify it in place. This can be helpful for long-running programs that need to be controlled externally, like:\n - training a neural network\n - running a bot\n - etc.\n\nFreak supports nested models and partial updates. Consider the following model:\n\n```python\nfrom pydantic import BaseModel\n\nclass Bar(BaseModel):\n foo: str = \"bar\"\n baz: str = \"qux\"\n\nclass State(BaseModel):\n bar: Bar = Bar()\n```\n\nFreak will generate `put` endpoints for the `foo` and `baz` fields, and a `patch` endpoint for the `bar` field (as it's a `pydantic` model itself). This `patch` endpoint supports partial updates:\n\n```shell\ncurl -X PATCH localhost:4444/set/bar -d '{\"foo\": \"baz\"}'\n```\n\nBecause Freak is using `FastAPI`, it's possible to use auto-generated documentation to interact with the Freak server. The interactive documentation can be accessed at Freak's main endpoint, which by default is `localhost:4444`.\n\nThe following screenshot shows the generated endpoints for the DL [example](https://github.com/danielgafni/freak/blob/master/examples/dl_example.py):\n\n\n\n## Development\n\n### Installation\n\n```shell\npoetry install\npoetry run pre-commit install\n```\n### Testing\n\n```shell\npoetry run pytest\n```\n",
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