<p align="center">
<a href="https://github.com/pier-digital/modelib"><img src="https://raw.githubusercontent.com/pier-digital/modelib/main/logo.png" alt="modelib"></a>
</p>
<p align="center">
<em>A minimalist framework for online deployment of sklearn-like models</em>
</p>
<div align="center">
[](https://pypi.org/project/modelib/)
[](https://github.com/psf/black)
[](https://github.com/pier-digital/modelib/releases)
[](https://github.com/pier-digital/modelib/blob/main/LICENSE)
</div>
## Installation
```bash
pip install modelib
```
## Usage
The modelib package provides a simple interface to deploy and serve models online. The package is designed to be used with the [fastapi](https://fastapi.tiangolo.com/) package, and supports serving models that are compatible with the [sklearn](https://scikit-learn.org/stable/) package.
First, you will need to create a model that is compatible with the sklearn package. For example, let's create a simple RandomForestClassifier model with a StandardScaler preprocessor:
```python
MODEL = Pipeline(
[
("scaler", StandardScaler()),
("clf", RandomForestClassifier(random_state=42)),
]
).set_output(transform="pandas")
```
Let's assume that you have a dataset with the following columns:
```python
request_model = [
{"name": "sepal length (cm)", "dtype": "float64"},
{"name": "sepal width (cm)", "dtype": "float64"},
{"name": "petal length (cm)", "dtype": "float64"},
{"name": "petal width (cm)", "dtype": "float64"},
]
```
Alternatively, you can use a pydantic model to define the request model, where the alias field is used to match the variable names with the column names in the training dataset:
```python
class InputData(pydantic.BaseModel):
sepal_length: float = pydantic.Field(alias="sepal length (cm)")
sepal_width: float = pydantic.Field(alias="sepal width (cm)")
petal_length: float = pydantic.Field(alias="petal length (cm)")
petal_width: float = pydantic.Field(alias="petal width (cm)")
request_model = InputData
```
After the model is created and trained, you can create a modelib runner for this model as follows:
```python
import modelib as ml
simple_runner = ml.SklearnRunner(
name="my simple model",
predictor=MODEL,
method_name="predict",
request_model=request_model,
)
```
Another option is to use the `SklearnPipelineRunner` class which allows you to get all the outputs of the pipeline:
```python
pipeline_runner = ml.SklearnPipelineRunner(
"Pipeline Model",
predictor=MODEL,
method_names=["transform", "predict"],
request_model=request_model,
)
```
Now you can create a FastAPI app with the runners:
```python
app = ml.init_app(runners=[simple_runner, pipeline_runner])
```
You can also pass an existing FastAPI app to the `init_app` function:
```python
import fastapi
app = fastapi.FastAPI()
app = ml.init_app(app=app, runners=[simple_runner, pipeline_runner])
```
The `init_app` function will add the necessary routes to the FastAPI app to serve the models. You can now start the app with:
```bash
uvicorn <replace-with-the-script-filename>:app --reload
```
After the app is running you can check the created routes in the Swagger UI at the `/docs` endpoint.
The created routes expect a JSON payload with the features as keys and the values as the input to the model. For example, to make a prediction with the simple model runner you can send a POST request to the `/my-simple-model` endpoint with the following payload:
```json
{
"sepal length (cm)": 5.1,
"sepal width (cm)": 3.5,
"petal length (cm)": 1.4,
"petal width (cm)": 0.2
}
```
The response will be a JSON with the prediction:
```json
{
"result": 0
}
```
## Contributing
If you want to contribute to the project, please read the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information.
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