From ebe5ca905e1a2ba603ca9ac65a9bb8b56efe09d8 Mon Sep 17 00:00:00 2001
From: Gabriel Guarisa <gabrielguarisa@gmail.com>
Date: Sat, 2 Mar 2024 20:29:44 -0300
Subject: [PATCH 1/4] Create PayloadManager class

---
 Makefile                         |  4 +++
 example.py                       | 26 +++++++++-----
 modelib/core/endpoint_factory.py | 19 +++++++----
 modelib/runners/base.py          | 52 +++++++++++++---------------
 modelib/runners/sklearn.py       | 58 ++++++++++++++++----------------
 tests/test_example.py            | 27 ++++++++++++---
 6 files changed, 108 insertions(+), 78 deletions(-)

diff --git a/Makefile b/Makefile
index 76db7ef..267e10e 100644
--- a/Makefile
+++ b/Makefile
@@ -10,3 +10,7 @@ tests:
 formatting:
 	poetry run ruff format .
 	poetry run ruff check .
+
+.PHONY: example
+example:
+	poetry run python example.py
\ No newline at end of file
diff --git a/example.py b/example.py
index 57ee2f6..e953132 100644
--- a/example.py
+++ b/example.py
@@ -1,5 +1,5 @@
 import fastapi
-
+import pydantic
 import modelib as ml
 
 
@@ -28,12 +28,20 @@ def create_model():
     return model
 
 
-FEATURES = [
-    {"name": "sepal length (cm)", "dtype": "float64"},
-    {"name": "sepal width (cm)", "dtype": "float64"},
-    {"name": "petal length (cm)", "dtype": "float64"},
-    {"name": "petal width (cm)", "dtype": "float64"},
-]
+# InputData = [
+#     {"name": "sepal length (cm)", "dtype": "float64"},
+#     {"name": "sepal width (cm)", "dtype": "float64"},
+#     {"name": "petal length (cm)", "dtype": "float64"},
+#     {"name": "petal width (cm)", "dtype": "float64"},
+# ]
+
+
+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)")
+
 
 MODEL = create_model()
 
@@ -41,14 +49,14 @@ def create_model():
     name="my simple model",
     predictor=MODEL,
     method_name="predict",
-    features=FEATURES,
+    request_model=InputData,
 )
 
 pipeline_runner = ml.SklearnPipelineRunner(
     "Pipeline Model",
     predictor=MODEL,
     method_names=["transform", "predict"],
-    features=FEATURES,
+    request_model=InputData,
 )
 
 app = fastapi.FastAPI()
diff --git a/modelib/core/endpoint_factory.py b/modelib/core/endpoint_factory.py
index 129dcac..d5ac629 100644
--- a/modelib/core/endpoint_factory.py
+++ b/modelib/core/endpoint_factory.py
@@ -4,26 +4,27 @@
 from slugify import slugify
 
 from modelib.core import schemas
-from modelib.runners.base import BaseRunner
+from modelib.runners.base import PayloadManager, BaseRunner
 
 
 def create_runner_endpoint(
     app: fastapi.FastAPI,
-    runner: BaseRunner,
+    runner_func: typing.Callable,
+    payload_manager: PayloadManager,
     **kwargs,
 ) -> fastapi.FastAPI:
-    path = f"/{slugify(runner.name)}"
+    path = f"/{slugify(payload_manager.name)}"
 
     route_kwargs = {
-        "name": runner.name,
+        "name": payload_manager.name,
         "methods": ["POST"],
-        "response_model": runner.response_model,
+        "response_model": payload_manager.response_model,
     }
     route_kwargs.update(kwargs)
 
     app.add_api_route(
         path,
-        runner.get_runner_func(),
+        runner_func,
         **route_kwargs,
     )
 
@@ -42,6 +43,10 @@ def create_runners_router(runners: typing.List[BaseRunner]) -> fastapi.APIRouter
     )
 
     for runner in runners:
-        router = create_runner_endpoint(router, runner)
+        router = create_runner_endpoint(
+            router,
+            runner_func=runner.get_runner_func(),
+            payload_manager=runner.payload_manager,
+        )
 
     return router
diff --git a/modelib/runners/base.py b/modelib/runners/base.py
index 72a3e41..6a6fcdd 100644
--- a/modelib/runners/base.py
+++ b/modelib/runners/base.py
@@ -12,34 +12,33 @@ def remove_unset_features(features: typing.List[dict]) -> typing.List[dict]:
     ]
 
 
-class BaseRunner:
+class PayloadManager:
     def __init__(
         self,
         name: str,
-        predictor: typing.Any,
-        features: typing.List[dict] = None,
+        request_model: typing.Union[typing.Type[pydantic.BaseModel], typing.List[dict]],
+        response_model: typing.Type[pydantic.BaseModel] = schemas.ResultResponseModel,
         **kwargs,
     ):
         self._name = name
-        self._predictor = predictor
-        self._features = remove_unset_features(features) if features else None
-        self._request_model = (
-            schemas.pydantic_model_from_list_of_dicts(self.name, self.features)
-            if self.features
-            else None
-        )
+        if isinstance(request_model, list):
+            request_model = remove_unset_features(request_model)
+            self._request_model = schemas.pydantic_model_from_list_of_dicts(
+                name, request_model
+            )
+        elif issubclass(request_model, pydantic.BaseModel):
+            self._request_model = request_model
+        else:
+            raise ValueError("request_model must be a pydantic.BaseModel subclass")
 
-    @property
-    def name(self) -> str:
-        return self._name
+        if not issubclass(response_model, pydantic.BaseModel):
+            raise ValueError("response_model must be a pydantic.BaseModel subclass")
 
-    @property
-    def predictor(self) -> typing.Any:
-        return self._predictor
+        self._response_model = response_model
 
     @property
-    def features(self) -> typing.List[str]:
-        return self._features
+    def name(self) -> str:
+        return self._name
 
     @property
     def request_model(self) -> typing.Type[pydantic.BaseModel]:
@@ -47,16 +46,13 @@ def request_model(self) -> typing.Type[pydantic.BaseModel]:
 
     @property
     def response_model(self) -> typing.Type[pydantic.BaseModel]:
-        return schemas.ResultResponseModel
+        return self._response_model
 
-    def get_runner_func(self) -> typing.Callable:
-        raise NotImplementedError()
 
-    def to_dict(self) -> dict:
-        return {
-            "name": self.name,
-            "features": self.features,
-        }
+class BaseRunner:
+    @property
+    def payload_manager(self) -> PayloadManager:
+        raise NotImplementedError
 
-    def __dict__(self) -> dict:
-        return self.to_dict()
+    def get_runner_func(self) -> typing.Callable:
+        raise NotImplementedError
diff --git a/modelib/runners/sklearn.py b/modelib/runners/sklearn.py
index d74ab3d..4eb6cfc 100644
--- a/modelib/runners/sklearn.py
+++ b/modelib/runners/sklearn.py
@@ -8,7 +8,7 @@
 
 from modelib.core import exceptions, schemas
 
-from .base import BaseRunner
+from .base import PayloadManager, BaseRunner
 from sklearn.pipeline import Pipeline
 from sklearn.base import BaseEstimator
 
@@ -24,26 +24,25 @@ def __init__(
                 f"Predictor must be an instance of sklearn.base.BaseEstimator, got {type(predictor)}"
             )
 
-        predictor = predictor.set_output(transform="pandas")
+        self._predictor = predictor.set_output(transform="pandas")
+        self._payload_manager = PayloadManager(**kwargs)
 
-        features = kwargs.get("features")
-        feature_names = [f["name"] for f in features]
-
-        if hasattr(predictor, "feature_names_in_"):
-            for f in predictor.feature_names_in_:
-                if f not in feature_names:
-                    raise ValueError(f"Feature {f} is not in features: {features}")
+    @property
+    def predictor(self) -> BaseEstimator:
+        return self._predictor
 
-        super().__init__(predictor=predictor, **kwargs)
+    @property
+    def payload_manager(self) -> PayloadManager:
+        return self._payload_manager
 
     def execute(self, input_df: pd.DataFrame) -> typing.Any:
         raise NotImplementedError
 
     def get_runner_func(self) -> typing.Callable:
-        def runner_func(data: self.request_model):
+        def runner_func(data: self.payload_manager.request_model):
             try:
                 input_df = (
-                    pd.DataFrame(data.model_dump(by_alias=False), index=[0])
+                    pd.DataFrame(data.model_dump(by_alias=True), index=[0])
                     if isinstance(data, pydantic.BaseModel)
                     else data
                 )
@@ -58,11 +57,11 @@ def runner_func(data: self.request_model):
                     detail={
                         "error": str(ex),
                         "traceback": str(traceback.format_exc()),
-                        "runner": self.name,
+                        "runner": self.payload_manager.name,
                     },
                 ) from ex
 
-        runner_func.__name__ = self.name
+        runner_func.__name__ = self.payload_manager.name
         return runner_func
 
 
@@ -72,10 +71,12 @@ def __init__(
         name: str,
         predictor: BaseEstimator,
         method_name: str,
-        features: typing.List[dict],
+        request_model: typing.Union[typing.Type[pydantic.BaseModel], typing.List[dict]],
         **kwargs,
     ):
-        super().__init__(name=name, predictor=predictor, features=features, **kwargs)
+        super().__init__(
+            predictor=predictor, name=name, request_model=request_model, **kwargs
+        )
 
         if not hasattr(predictor, method_name):
             raise ValueError(f"Predictor does not have method {method_name}")
@@ -90,9 +91,6 @@ def method_name(self) -> str:
     def method(self) -> typing.Callable:
         return getattr(self.predictor, self.method_name)
 
-    def to_dict(self) -> dict:
-        return {**super().to_dict(), "method_name": self.method_name}
-
     def execute(self, input_df: pd.DataFrame) -> typing.Any:
         return {"result": self.method(input_df).tolist()[0]}
 
@@ -103,10 +101,19 @@ def __init__(
         name: str,
         predictor: Pipeline,
         method_names: typing.List[str],
-        features: typing.List[dict],
+        request_model: typing.Union[typing.Type[pydantic.BaseModel], typing.List[dict]],
         **kwargs,
     ):
-        super().__init__(name=name, predictor=predictor, features=features, **kwargs)
+        kwargs["response_model"] = kwargs.get(
+            "response_model", schemas.ResultResponseWithStepsModel
+        )
+
+        super().__init__(
+            name=name,
+            predictor=predictor,
+            request_model=request_model,
+            **kwargs,
+        )
 
         method_names = [method_names] if isinstance(method_names, str) else method_names
 
@@ -130,13 +137,6 @@ def __init__(
     def method_names(self) -> typing.List[str]:
         return self._method_names
 
-    @property
-    def response_model(self) -> typing.Type[pydantic.BaseModel]:
-        return schemas.ResultResponseWithStepsModel
-
-    def to_dict(self) -> dict:
-        return {**super().to_dict(), "method_names": self.method_names}
-
     def execute(self, input_df: pd.DataFrame) -> typing.Any:
         step_outputs = {}
         previous_step_output = input_df.copy()
@@ -153,7 +153,7 @@ def execute(self, input_df: pd.DataFrame) -> typing.Any:
                     detail={
                         "error": str(ex),
                         "traceback": str(traceback.format_exc()),
-                        "runner": self.name,
+                        "runner": self.payload_manager.name,
                         "step": step_name,
                         "method": method_name,
                         "step_outputs": step_outputs,
diff --git a/tests/test_example.py b/tests/test_example.py
index a32e598..330619d 100644
--- a/tests/test_example.py
+++ b/tests/test_example.py
@@ -2,6 +2,8 @@
 from fastapi.testclient import TestClient
 
 import modelib as ml
+import pytest
+import pydantic
 
 
 def create_model():
@@ -34,22 +36,37 @@ def create_model():
     {"name": "petal width (cm)", "dtype": "float64"},
 ]
 
+
+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)")
+
+
 MODEL = create_model()
 
 
-def test_example():
+@pytest.mark.parametrize(
+    "request_model,model",
+    [
+        (FEATURES, MODEL),
+        (InputData, MODEL),
+    ],
+)
+def test_example(request_model, model):
     simple_runner = ml.SklearnRunner(
         name="my simple model",
-        predictor=MODEL,
+        predictor=model,
         method_name="predict",
-        features=FEATURES,
+        request_model=request_model,
     )
 
     pipeline_runner = ml.SklearnPipelineRunner(
         "Pipeline Model",
-        predictor=MODEL,
+        predictor=model,
         method_names=["transform", "predict"],
-        features=FEATURES,
+        request_model=request_model,
     )
 
     app = fastapi.FastAPI()

From 90f0faa1aeb22d736610d00ec5c691ea0e53a8ad Mon Sep 17 00:00:00 2001
From: Gabriel Guarisa <gabrielguarisa@gmail.com>
Date: Sat, 2 Mar 2024 20:36:47 -0300
Subject: [PATCH 2/4] Refactor endpoint creation and add slugify
 functionality***

This commit refactors the `create_runner_endpoint` function in `endpoint_factory.py` to use the `EndpointMetadataManager` class instead of the deprecated `PayloadManager` class. It also adds the `slugify` functionality to generate the path slug for the endpoint. Additionally, the `create_runners_router` function in `endpoint_factory.py` now accepts additional keyword arguments for configuring the API router. This commit improves code organization and readability.
---
 modelib/core/endpoint_factory.py | 39 ++++++++++++++++++--------------
 modelib/runners/base.py          | 10 +++++---
 modelib/runners/sklearn.py       | 16 ++++++-------
 modelib/server/app.py            |  5 +++-
 4 files changed, 41 insertions(+), 29 deletions(-)

diff --git a/modelib/core/endpoint_factory.py b/modelib/core/endpoint_factory.py
index d5ac629..de8dafa 100644
--- a/modelib/core/endpoint_factory.py
+++ b/modelib/core/endpoint_factory.py
@@ -1,24 +1,24 @@
 import typing
 
 import fastapi
-from slugify import slugify
+
 
 from modelib.core import schemas
-from modelib.runners.base import PayloadManager, BaseRunner
+from modelib.runners.base import EndpointMetadataManager, BaseRunner
 
 
 def create_runner_endpoint(
     app: fastapi.FastAPI,
     runner_func: typing.Callable,
-    payload_manager: PayloadManager,
+    endpoint_metadata_manager: EndpointMetadataManager,
     **kwargs,
 ) -> fastapi.FastAPI:
-    path = f"/{slugify(payload_manager.name)}"
+    path = f"/{endpoint_metadata_manager.slug}"
 
     route_kwargs = {
-        "name": payload_manager.name,
+        "name": endpoint_metadata_manager.name,
         "methods": ["POST"],
-        "response_model": payload_manager.response_model,
+        "response_model": endpoint_metadata_manager.response_model,
     }
     route_kwargs.update(kwargs)
 
@@ -31,22 +31,27 @@ def create_runner_endpoint(
     return app
 
 
-def create_runners_router(runners: typing.List[BaseRunner]) -> fastapi.APIRouter:
-    router = fastapi.APIRouter(
-        tags=["runners"],
-        responses={
-            500: {
-                "model": schemas.JsonApiErrorModel,
-                "description": "Inference Internal Server Error",
-            }
-        },
-    )
+def create_runners_router(
+    runners: typing.List[BaseRunner], **runners_router_kwargs
+) -> fastapi.APIRouter:
+    responses = runners_router_kwargs.pop("responses", {})
+    if 500 not in responses:
+        responses[500] = {
+            "model": schemas.JsonApiErrorModel,
+            "description": "Inference Internal Server Error",
+        }
+
+    runners_router_kwargs["responses"] = responses
+
+    runners_router_kwargs["tags"] = runners_router_kwargs.get("tags", ["runners"])
+
+    router = fastapi.APIRouter(**runners_router_kwargs)
 
     for runner in runners:
         router = create_runner_endpoint(
             router,
             runner_func=runner.get_runner_func(),
-            payload_manager=runner.payload_manager,
+            endpoint_metadata_manager=runner.endpoint_metadata_manager,
         )
 
     return router
diff --git a/modelib/runners/base.py b/modelib/runners/base.py
index 6a6fcdd..62ff4e1 100644
--- a/modelib/runners/base.py
+++ b/modelib/runners/base.py
@@ -1,7 +1,7 @@
 import typing
 
 import pydantic
-
+from slugify import slugify
 from modelib.core import schemas
 
 
@@ -12,7 +12,7 @@ def remove_unset_features(features: typing.List[dict]) -> typing.List[dict]:
     ]
 
 
-class PayloadManager:
+class EndpointMetadataManager:
     def __init__(
         self,
         name: str,
@@ -40,6 +40,10 @@ def __init__(
     def name(self) -> str:
         return self._name
 
+    @property
+    def slug(self) -> str:
+        return slugify(self.name)
+
     @property
     def request_model(self) -> typing.Type[pydantic.BaseModel]:
         return self._request_model
@@ -51,7 +55,7 @@ def response_model(self) -> typing.Type[pydantic.BaseModel]:
 
 class BaseRunner:
     @property
-    def payload_manager(self) -> PayloadManager:
+    def endpoint_metadata_manager(self) -> EndpointMetadataManager:
         raise NotImplementedError
 
     def get_runner_func(self) -> typing.Callable:
diff --git a/modelib/runners/sklearn.py b/modelib/runners/sklearn.py
index 4eb6cfc..220eeab 100644
--- a/modelib/runners/sklearn.py
+++ b/modelib/runners/sklearn.py
@@ -8,7 +8,7 @@
 
 from modelib.core import exceptions, schemas
 
-from .base import PayloadManager, BaseRunner
+from .base import EndpointMetadataManager, BaseRunner
 from sklearn.pipeline import Pipeline
 from sklearn.base import BaseEstimator
 
@@ -25,21 +25,21 @@ def __init__(
             )
 
         self._predictor = predictor.set_output(transform="pandas")
-        self._payload_manager = PayloadManager(**kwargs)
+        self._endpoint_metadata_manager = EndpointMetadataManager(**kwargs)
 
     @property
     def predictor(self) -> BaseEstimator:
         return self._predictor
 
     @property
-    def payload_manager(self) -> PayloadManager:
-        return self._payload_manager
+    def endpoint_metadata_manager(self) -> EndpointMetadataManager:
+        return self._endpoint_metadata_manager
 
     def execute(self, input_df: pd.DataFrame) -> typing.Any:
         raise NotImplementedError
 
     def get_runner_func(self) -> typing.Callable:
-        def runner_func(data: self.payload_manager.request_model):
+        def runner_func(data: self.endpoint_metadata_manager.request_model):
             try:
                 input_df = (
                     pd.DataFrame(data.model_dump(by_alias=True), index=[0])
@@ -57,11 +57,11 @@ def runner_func(data: self.payload_manager.request_model):
                     detail={
                         "error": str(ex),
                         "traceback": str(traceback.format_exc()),
-                        "runner": self.payload_manager.name,
+                        "runner": self.endpoint_metadata_manager.name,
                     },
                 ) from ex
 
-        runner_func.__name__ = self.payload_manager.name
+        runner_func.__name__ = self.endpoint_metadata_manager.name
         return runner_func
 
 
@@ -153,7 +153,7 @@ def execute(self, input_df: pd.DataFrame) -> typing.Any:
                     detail={
                         "error": str(ex),
                         "traceback": str(traceback.format_exc()),
-                        "runner": self.payload_manager.name,
+                        "runner": self.endpoint_metadata_manager.name,
                         "step": step_name,
                         "method": method_name,
                         "step_outputs": step_outputs,
diff --git a/modelib/server/app.py b/modelib/server/app.py
index 97992e7..d6b0824 100644
--- a/modelib/server/app.py
+++ b/modelib/server/app.py
@@ -12,10 +12,13 @@ def init_app(
     app: fastapi.FastAPI,
     runners: typing.List[BaseRunner],
     include_infrastructure: bool = True,
+    **runners_router_kwargs,
 ) -> fastapi.FastAPI:
     exceptions.init_app(app)
 
-    app.include_router(endpoint_factory.create_runners_router(runners))
+    app.include_router(
+        endpoint_factory.create_runners_router(runners, **runners_router_kwargs)
+    )
 
     if include_infrastructure:
         infrastructure.init_app(app)

From 2f269b71b1909b5b1ef16de8909862d238c594d0 Mon Sep 17 00:00:00 2001
From: Gabriel Guarisa <gabrielguarisa@gmail.com>
Date: Sat, 2 Mar 2024 20:36:59 -0300
Subject: [PATCH 3/4] Update version and add alternative request model
 definition

---
 README.md      |  18 ++-
 poetry.lock    | 338 ++++++++++++++++++++++++-------------------------
 pyproject.toml |   3 +-
 3 files changed, 185 insertions(+), 174 deletions(-)

diff --git a/README.md b/README.md
index b7d0155..94fd0c2 100644
--- a/README.md
+++ b/README.md
@@ -39,13 +39,25 @@ MODEL = Pipeline(
 Let's assume that you have a dataset with the following columns:
 
 ```python
-FEATURES = [
+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 variable name used 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
@@ -55,7 +67,7 @@ simple_runner = ml.SklearnRunner(
     name="my simple model",
     predictor=MODEL,
     method_name="predict",
-    features=FEATURES,
+    request_model=request_model,
 )
 ```
 
@@ -66,7 +78,7 @@ pipeline_runner = ml.SklearnPipelineRunner(
     "Pipeline Model",
     predictor=MODEL,
     method_names=["transform", "predict"],
-    features=FEATURES,
+    request_model=request_model,
 )
 ```
 
diff --git a/poetry.lock b/poetry.lock
index bef261c..78a2751 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -16,13 +16,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
 
 [[package]]
 name = "anyio"
-version = "4.2.0"
+version = "4.3.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
-    {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
 ]
 
 [package.dependencies]
@@ -74,63 +74,63 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.4.1"
+version = "7.4.3"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -185,13 +185,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.2"
+version = "1.0.4"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -202,7 +202,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.23.0)"]
+trio = ["trio (>=0.22.0,<0.25.0)"]
 
 [[package]]
 name = "httptools"
@@ -475,13 +475,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.19.0"
+version = "0.20.0"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
 files = [
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@@ -504,18 +504,18 @@ prometheus-client = ">=0.8.0,<1.0.0"
 
 [[package]]
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-version = "2.6.1"
+version = "2.6.3"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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+pydantic-core = "2.16.3"
 typing-extensions = ">=4.6.1"
 
 [package.extras]
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+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:a425479ee40ff021f8216c9d07a6a3b54b31c8267c6e17aa88b70d7ebd0e5e5b"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:5c5cbc703168d1b7a838668998308018a2718c2130595e8e190220238addc96f"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:99b6add4c0b39a513d323d3b93bc173dac663c27b99860dd5bf491b240d26137"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75f76ee558751746d6a38f89d60b6228fa174e5172d143886af0f85aa306fd89"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:00ee1c97b5364b84cb0bd82e9bbf645d5e2871fb8c58059d158412fee2d33d8a"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:287073c66748f624be4cef893ef9174e3eb88fe0b8a78dc22e88eca4bc357ca6"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ed25e1835c00a332cb10c683cd39da96a719ab1dfc08427d476bce41b92531fc"},
+    {file = "pydantic_core-2.16.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:86b3d0033580bd6bbe07590152007275bd7af95f98eaa5bd36f3da219dcd93da"},
+    {file = "pydantic_core-2.16.3.tar.gz", hash = "sha256:1cac689f80a3abab2d3c0048b29eea5751114054f032a941a32de4c852c59cad"},
 ]
 
 [package.dependencies]
@@ -614,13 +614,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pytest"
-version = "8.0.0"
+version = "8.0.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.0.0-py3-none-any.whl", hash = "sha256:50fb9cbe836c3f20f0dfa99c565201fb75dc54c8d76373cd1bde06b06657bdb6"},
-    {file = "pytest-8.0.0.tar.gz", hash = "sha256:249b1b0864530ba251b7438274c4d251c58d868edaaec8762893ad4a0d71c36c"},
+    {file = "pytest-8.0.2-py3-none-any.whl", hash = "sha256:edfaaef32ce5172d5466b5127b42e0d6d35ebbe4453f0e3505d96afd93f6b096"},
+    {file = "pytest-8.0.2.tar.gz", hash = "sha256:d4051d623a2e0b7e51960ba963193b09ce6daeb9759a451844a21e4ddedfc1bd"},
 ]
 
 [package.dependencies]
@@ -654,13 +654,13 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 
 [[package]]
 name = "python-dateutil"
-version = "2.8.2"
+version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
-    {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
-    {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
+    {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
 ]
 
 [package.dependencies]
@@ -693,13 +693,13 @@ files = [
 
 [[package]]
 name = "python-slugify"
-version = "8.0.3"
+version = "8.0.4"
 description = "A Python slugify application that also handles Unicode"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "python-slugify-8.0.3.tar.gz", hash = "sha256:e04cba5f1c562502a1175c84a8bc23890c54cdaf23fccaaf0bf78511508cabed"},
-    {file = "python_slugify-8.0.3-py2.py3-none-any.whl", hash = "sha256:c71189c161e8c671f1b141034d9a56308a8a5978cd13d40446c879569212fdd1"},
+    {file = "python-slugify-8.0.4.tar.gz", hash = "sha256:59202371d1d05b54a9e7720c5e038f928f45daaffe41dd10822f3907b937c856"},
+    {file = "python_slugify-8.0.4-py2.py3-none-any.whl", hash = "sha256:276540b79961052b66b7d116620b36518847f52d5fd9e3a70164fc8c50faa6b8"},
 ]
 
 [package.dependencies]
@@ -903,13 +903,13 @@ files = [
 
 [[package]]
 name = "sniffio"
-version = "1.3.0"
+version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
-    {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
 [[package]]
@@ -943,13 +943,13 @@ files = [
 
 [[package]]
 name = "threadpoolctl"
-version = "3.2.0"
+version = "3.3.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
-    {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
+    {file = "threadpoolctl-3.3.0-py3-none-any.whl", hash = "sha256:6155be1f4a39f31a18ea70f94a77e0ccd57dced08122ea61109e7da89883781e"},
+    {file = "threadpoolctl-3.3.0.tar.gz", hash = "sha256:5dac632b4fa2d43f42130267929af3ba01399ef4bd1882918e92dbc30365d30c"},
 ]
 
 [[package]]
@@ -965,24 +965,24 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.10.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
 
 [[package]]
 name = "tzdata"
-version = "2023.4"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
-    {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [[package]]
@@ -1226,4 +1226,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "81571e6ab7349111fec6567698bd5b19fe866afa9563eede1a591c5eb7e5d829"
+content-hash = "31762022f9c6c8f1720248de6ce76ed4912f66dbc8d5359043fecae5fa950e86"
diff --git a/pyproject.toml b/pyproject.toml
index f73e951..bde45c5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "modelib"
-version = "0.1.1"
+version = "0.2.0"
 description = "A minimalist framework for online deployment of sklearn-like models"
 authors = ["Gabriel Guarisa <gabrielguarisa@gmail.com>"]
 readme = "README.md"
@@ -11,7 +11,6 @@ keywords = ["machine learning", "fastapi", "sklearn", "online deployment"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
-joblib = "^1.3.2"
 uvicorn = {extras = ["standard"], version = "^0.26.0"}
 fastapi = "^0.109.1"
 prometheus-fastapi-instrumentator = "^6.1.0"

From 41f992f6b513088d45530156c0eca1e338812a92 Mon Sep 17 00:00:00 2001
From: Gabriel Guarisa <gabrielguarisa@gmail.com>
Date: Sat, 2 Mar 2024 21:01:34 -0300
Subject: [PATCH 4/4] Refactor init_app function and add documentation

---
 README.md             |  12 ++-
 example.py            |  20 +++--
 modelib/server/app.py |  14 +++-
 tests/test_example.py | 167 ++++++++++++++++++++++++------------------
 4 files changed, 127 insertions(+), 86 deletions(-)

diff --git a/README.md b/README.md
index 94fd0c2..10e71fb 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ request_model = [
     {"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 variable name used in the training dataset:
+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):
@@ -82,14 +82,20 @@ pipeline_runner = ml.SklearnPipelineRunner(
 )
 ```
 
-Now you can extend a FastAPI app with the runners:
+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, [simple_runner, pipeline_runner])
+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:
diff --git a/example.py b/example.py
index e953132..910ed27 100644
--- a/example.py
+++ b/example.py
@@ -12,9 +12,7 @@ def create_model():
 
     X, y = load_iris(return_X_y=True, as_frame=True)
 
-    X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=0.2, random_state=42
-    )
+    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
 
     model = Pipeline(
         [
@@ -28,12 +26,12 @@ def create_model():
     return model
 
 
-# InputData = [
-#     {"name": "sepal length (cm)", "dtype": "float64"},
-#     {"name": "sepal width (cm)", "dtype": "float64"},
-#     {"name": "petal length (cm)", "dtype": "float64"},
-#     {"name": "petal width (cm)", "dtype": "float64"},
-# ]
+features_metadata = [
+    {"name": "sepal length (cm)", "dtype": "float64"},
+    {"name": "sepal width (cm)", "dtype": "float64"},
+    {"name": "petal length (cm)", "dtype": "float64"},
+    {"name": "petal width (cm)", "dtype": "float64"},
+]
 
 
 class InputData(pydantic.BaseModel):
@@ -49,7 +47,7 @@ class InputData(pydantic.BaseModel):
     name="my simple model",
     predictor=MODEL,
     method_name="predict",
-    request_model=InputData,
+    request_model=InputData,  # OR request_model=features_metadata
 )
 
 pipeline_runner = ml.SklearnPipelineRunner(
@@ -61,7 +59,7 @@ class InputData(pydantic.BaseModel):
 
 app = fastapi.FastAPI()
 
-app = ml.init_app(app, [simple_runner, pipeline_runner])
+app = ml.init_app(app=app, runners=[simple_runner, pipeline_runner])
 
 if __name__ == "__main__":
     import uvicorn
diff --git a/modelib/server/app.py b/modelib/server/app.py
index d6b0824..a25e260 100644
--- a/modelib/server/app.py
+++ b/modelib/server/app.py
@@ -9,11 +9,23 @@
 
 
 def init_app(
-    app: fastapi.FastAPI,
+    *,
     runners: typing.List[BaseRunner],
+    app: fastapi.FastAPI = fastapi.FastAPI(),
     include_infrastructure: bool = True,
     **runners_router_kwargs,
 ) -> fastapi.FastAPI:
+    """Initialize FastAPI application with Modelib runners.
+
+    Args:
+        runners: List of runners to be included in the application.
+        app: FastAPI application to be initialized. If not provided, a new application will be created.
+        include_infrastructure: Whether to include infrastructure endpoints.
+        **runners_router_kwargs: Additional keyword arguments to be passed to the runners router.
+
+    Returns:
+        FastAPI application with Modelib runners.
+    """
     exceptions.init_app(app)
 
     app.include_router(
diff --git a/tests/test_example.py b/tests/test_example.py
index 330619d..1f29f3e 100644
--- a/tests/test_example.py
+++ b/tests/test_example.py
@@ -1,79 +1,12 @@
 import fastapi
 from fastapi.testclient import TestClient
-
-import modelib as ml
+import example
 import pytest
-import pydantic
-
-
-def create_model():
-    from sklearn.datasets import load_iris
-    from sklearn.ensemble import RandomForestClassifier
-    from sklearn.pipeline import Pipeline
-    from sklearn.preprocessing import StandardScaler
-    from sklearn.model_selection import train_test_split
-
-    X, y = load_iris(return_X_y=True, as_frame=True)
-
-    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    model = Pipeline(
-        [
-            ("scaler", StandardScaler()),
-            ("clf", RandomForestClassifier(random_state=42)),
-        ]
-    ).set_output(transform="pandas")
-
-    model.fit(X_train, y_train)
-
-    return model
-
-
-FEATURES = [
-    {"name": "sepal length (cm)", "dtype": "float64"},
-    {"name": "sepal width (cm)", "dtype": "float64"},
-    {"name": "petal length (cm)", "dtype": "float64"},
-    {"name": "petal width (cm)", "dtype": "float64"},
-]
-
-
-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)")
-
-
-MODEL = create_model()
-
-
-@pytest.mark.parametrize(
-    "request_model,model",
-    [
-        (FEATURES, MODEL),
-        (InputData, MODEL),
-    ],
-)
-def test_example(request_model, model):
-    simple_runner = ml.SklearnRunner(
-        name="my simple model",
-        predictor=model,
-        method_name="predict",
-        request_model=request_model,
-    )
-
-    pipeline_runner = ml.SklearnPipelineRunner(
-        "Pipeline Model",
-        predictor=model,
-        method_names=["transform", "predict"],
-        request_model=request_model,
-    )
-
-    app = fastapi.FastAPI()
+import modelib as ml
 
-    app = ml.init_app(app, [simple_runner, pipeline_runner])
 
-    client = TestClient(app)
+def test_example():
+    client = TestClient(example.app)
 
     response = client.get("/docs")
 
@@ -118,3 +51,95 @@ def test_example(request_model, model):
             "clf": [0],
         },
     }
+
+
+@pytest.mark.parametrize(
+    "runner,endpoint_path,expected_response",
+    [
+        (
+            ml.SklearnRunner(
+                name="my simple model",
+                predictor=example.MODEL,
+                method_name="predict",
+                request_model=example.InputData,
+            ),
+            "/my-simple-model",
+            {"result": 0},
+        ),
+        (
+            ml.SklearnRunner(
+                name="my 232, simple model",
+                predictor=example.MODEL,
+                method_name="predict",
+                request_model=example.features_metadata,
+            ),
+            "/my-232-simple-model",
+            {"result": 0},
+        ),
+        (
+            ml.SklearnPipelineRunner(
+                "Pipeline Model",
+                predictor=example.MODEL,
+                method_names=["transform", "predict"],
+                request_model=example.InputData,
+            ),
+            "/pipeline-model",
+            {
+                "result": 0,
+                "steps": {
+                    "scaler": [
+                        {
+                            "sepal length (cm)": -7.081194586015879,
+                            "sepal width (cm)": -6.845571885453045,
+                            "petal length (cm)": -2.135591504400147,
+                            "petal width (cm)": -1.5795728805764124,
+                        }
+                    ],
+                    "clf": [0],
+                },
+            },
+        ),
+        (
+            ml.SklearnPipelineRunner(
+                "My SUPER    Pipeline Model",
+                predictor=example.MODEL,
+                method_names=["transform", "predict"],
+                request_model=example.features_metadata,
+            ),
+            "/my-super-pipeline-model",
+            {
+                "result": 0,
+                "steps": {
+                    "scaler": [
+                        {
+                            "sepal length (cm)": -7.081194586015879,
+                            "sepal width (cm)": -6.845571885453045,
+                            "petal length (cm)": -2.135591504400147,
+                            "petal width (cm)": -1.5795728805764124,
+                        }
+                    ],
+                    "clf": [0],
+                },
+            },
+        ),
+    ],
+)
+def test_runners(runner, endpoint_path, expected_response):
+    app = fastapi.FastAPI()
+
+    app = ml.init_app(app=app, runners=[runner])
+
+    client = TestClient(app)
+
+    response = client.post(
+        endpoint_path,
+        json={
+            "sepal length (cm)": 0,
+            "sepal width (cm)": 0,
+            "petal length (cm)": 0,
+            "petal width (cm)": 0,
+        },
+    )
+
+    assert response.status_code == 200
+    assert response.json() == expected_response