endpoint_fx docstring as FastAPI description

vetiver/handlers/sklearn.py

@@ -17,7 +17,8 @@ class SKLearnHandler(BaseHandler):
     pip_name = "scikit-learn"
 
     def handler_predict(self, input_data, check_prototype):
-        """Generates method for /predict endpoint in VetiverAPI
+        """
+        Generates method for /predict endpoint in VetiverAPI
 
         The `handler_predict` function executes at each API call. Use this
         function for calling `predict()` and any other tasks that must be executed
@@ -30,7 +31,7 @@ def handler_predict(self, input_data, check_prototype):
 
         Returns
         -------
-        prediction
+        prediction:
             Prediction from model
         """
 

vetiver/handlers/spacy.py

@@ -54,7 +54,8 @@ def construct_prototype(self):
         return prototype
 
     def handler_predict(self, input_data, check_prototype):
-        """Generates method for /predict endpoint in VetiverAPI
+        """
+        Generates method for /predict endpoint in VetiverAPI
 
         The `handler_predict` function executes at each API call. Use this
         function for calling `predict()` and any other tasks that must be executed
@@ -63,7 +64,9 @@ def handler_predict(self, input_data, check_prototype):
         Parameters
         ----------
         input_data:
-            Test data
+            Test data. The SpacyHandler expects an input of a 1 column DataFrame with
+            the same column names as the prototype data, or column name "text" if no
+            prototype was given.
 
         Returns
         -------

vetiver/handlers/statsmodels.py

@@ -23,7 +23,8 @@ class StatsmodelsHandler(BaseHandler):
         pip_name = "statsmodels"
 
     def handler_predict(self, input_data, check_prototype):
-        """Generates method for /predict endpoint in VetiverAPI
+        """
+        Generates method for /predict endpoint in VetiverAPI
 
         The `handler_predict` function executes at each API call. Use this
         function for calling `predict()` and any other tasks that must be executed

vetiver/handlers/torch.py

@@ -23,7 +23,8 @@ class TorchHandler(BaseHandler):
         pip_name = "torch"
 
     def handler_predict(self, input_data, check_prototype):
-        """Generates method for /predict endpoint in VetiverAPI
+        """
+        Generates method for /predict endpoint in VetiverAPI
 
         The `handler_predict` function executes at each API call. Use this
         function for calling `predict()` and any other tasks that must be executed

vetiver/handlers/xgboost.py

@@ -23,7 +23,8 @@ class XGBoostHandler(BaseHandler):
         pip_name = "xgboost"
 
     def handler_predict(self, input_data, check_prototype):
-        """Generates method for /predict endpoint in VetiverAPI
+        """
+        Generates method for /predict endpoint in VetiverAPI
 
         The `handler_predict` function executes at each API call. Use this
         function for calling `predict()` and any other tasks that must be executed

vetiver/server.py

@@ -11,6 +11,7 @@
 from fastapi.openapi.utils import get_openapi
 from fastapi.responses import HTMLResponse, RedirectResponse
 from fastapi.responses import PlainTextResponse
+from textwrap import dedent
 from warnings import warn
 
 from .utils import _jupyter_nb
@@ -105,10 +106,12 @@ def pin_url():
 
         @app.get("/ping", include_in_schema=True)
         async def ping():
+            """Ping endpoint for health check"""
             return {"ping": "pong"}
 
         @app.get("/metadata")
         async def get_metadata():
+            """Get metadata from model"""
             return self.model.metadata.to_dict()
 
         self.vetiver_post(
@@ -183,13 +186,21 @@ def vetiver_post(self, endpoint_fx: Callable, endpoint_name: str = None, **kw):
         if not endpoint_name:
             endpoint_name = endpoint_fx.__name__
 
+        if endpoint_fx.__doc__ is not None:
+            api_desc = dedent(endpoint_fx.__doc__)
+        else:
+            api_desc = None
+
         if self.check_prototype is True:
 
-            @self.app.post(urljoin("/", endpoint_name), name=endpoint_name)
+            @self.app.post(
+                urljoin("/", endpoint_name),
+                name=endpoint_name,
+                description=api_desc,
+            )
             async def custom_endpoint(input_data: List[self.model.prototype]):
                 _to_frame = api_data_to_frame(input_data)
                 predictions = endpoint_fx(_to_frame, **kw)
-
                 if isinstance(predictions, List):
                     return {endpoint_name: predictions}
                 else: