Support for different response content types

Now it is possible to define an special argument "accept" for the
predict method, allowing the user to specify what response type is
expected. This is passed to the predict enpoints, therefore we are able
to return something different than JSON documents.

Author: alvarolopez

deepaas/api/v2/predict.py

@@ -34,7 +34,16 @@ def _get_model_response(model_name, model_obj):
 
 
 def _get_handler(model_name, model_obj):
-    handler_args = webargs.core.dict2schema(model_obj.get_predict_args())
+    aux = model_obj.get_predict_args()
+    accept = aux.get("accept", None)
+    if accept and "application/json" not in accept.validate.choices:
+        accept.validate.choices.insert(0, "application/json")
+        accept.validate.choices.append("*/*")
+        accept.default = "application/json"
+        accept.missing = "application/json"
+        accept.location = "headers"
+
+    handler_args = webargs.core.dict2schema(aux)
     handler_args.opts.ordered = True
 
     response = _get_model_response(model_name, model_obj)
@@ -49,7 +58,8 @@ def __init__(self, model_name, model_obj):
 
         @aiohttp_apispec.docs(
             tags=["models"],
-            summary="Make a prediction given the input data"
+            summary="Make a prediction given the input data",
+            produces=accept.validate.choices if accept else None,
         )
         @aiohttp_apispec.querystring_schema(handler_args)
         @aiohttp_apispec.response_schema(response(), 200)
@@ -63,6 +73,13 @@ async def post(self, request, wsk_args=None):
 
             ret = task.result()
 
+            accept = args.get("accept", "application/json")
+            if accept != "application/json":
+                response = web.Response(
+                    body=ret,
+                    content_type=accept,
+                )
+                return response
             if self.model_obj.has_schema:
                 self.model_obj.validate_response(ret)
                 return web.json_response(ret)

deepaas/model/v2/test.py

@@ -47,15 +47,50 @@ class TestModel(base.BaseModel):
         ),
     }
 
+    _logo = (
+        b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x1e\x00\x00\x00"
+        b"\r\x08\x04\x00\x00\x00\x14{\x16\x1c\x00\x00\x02\x13IDAT(\xcf\x8d"
+        b"\xd2?h\x94w\x00\xc6\xf1\xcf\xef\xbd\xfc9\xe3\x19\xacb\x8c\xa2C"
+        b"\x13-\x81\xe8 (\x11\xa4\x831 \xb4v\xb0B\xc0B\xa5\x10\x1c\x1c\xc4"
+        b"\xd2RQ\x14T\xecRE\xcd\xd0\x16\x11$\x8a\x82E\xba(\xddl\xc1:J\xa8CC"
+        b"\xd2\x98\xd8\xd2\x10EI\xcc\xdd\xe9\xdd\xbdw^\xee=\x07\x15\x17"
+        b"\x07\xbf\xd33<\xdf\xe1\x81\x87\xb7l\x95\xd5\xf6*\xb6\t\xcd\xb6"
+        b"\x19\xb0!\x8a\xbc\x17[\xe5\xde\xc8\x9a]7\xaf\xa8l\x7f\xca\xb7"
+        b"\xf6\xbdSh\xa0\xd7\x9d0\x9f\x8e*\tX,\xb7F\x8f5v\xfa\xdaM\x07u"
+        b"\xd5>>\xb3E\xc5\xad\xcc\xc8G\x9ez\x18\xe6;\xb4\x9b\xecx\xf2\x1f!"
+        b"\xd2o\xd8\xac\x11Cr\xda}\xe3\xb9X\xcds+\xd3B\xb3S\xca\xe6<S"
+        b"\xf4\x9d\xd5\xae\xb9\xa9$\x96u44\xb2[\xc9y\xfdN+\xc9\xfbB\xc5e;"
+        b"\xcc\xb9gQ\x94\xf2\xbd\xaa\x1f,u\\\xa2l@\xc5#\xbbu;\xach/\xe3~"
+        b"\nQ\x9f\xa6\xe0\xa8\xbc?<\xd0m\xd4\x84\xce\x86\xe0\x88\x17NE\x8d"
+        b"\xbeRv\xd1S\xbf\x89\xf5\x0f \n\xce\x19\xa7f\xd7\xeb\xfd\xbd\xf2"
+        b"\xc6\x0c\x1b6e}\x149\xa0\xe2\xe7\xd0d\x97\x82+ZM\xb9\xadh\xed"
+        b"\xeb\xf6\xe7\xaaL\x1a\x8c\xa2Ni\x0e\xc9\xb9\xa2n\xc6\xa6\x86`@"
+        b"\xc5\xa5\xd0\xa4O\xde\x8d\xd0\xa2\xcf\x0bg\xc5\xb6\x7f\x824\xc7L"
+        b"\xb1G\xd1\xa0\xcf\x9cT\x90\xb7\xd1\xbf\xb2\x86\xfc\xa2\xec\xba"
+        b"\x16\xc1\xaf\x12W\r\x9auO\x8f\x8a1=\x96\xe8\x97uXH\xf9\xd2\xdfr"
+        b"\xee\xbb\xe0\xae\xa5:\x9c7f\xd4\t\x0b\xdb\xf4\xb2\xdc\x8f\xfe7m"
+        b"\xc8j]b\xa3b9%\x17\xb5\x04\xed\x92\xf0\xac\xbeB!\xca'\x8b\xc5"
+        b"\xd2\x16\xc9\x84I\xea\x0b\x94\xb4j\x93\x0b9\xd5\xa8Z\xcbX\xe7"
+        b"\x96MVXe$\xf5W\x924\xd8\xac\xb5\xfe\xd02IRU\x901%\xa3P\xef\xf5"
+        b"\x81'>4\x1b\xddH>\xad7*\xd7\xb2V\n\xa8\xf8\x1djH)z\xa0\xd30\xfe1#"
+        b"\x98V\xd5d\xce\x8c\xa2\x92\x89\xfa*\xd3xl^lZ\xde\x9f\xe27\xf7|"
+        b"\tz\x97\xc9?\xa6\x13[\xb9\x00\x00\x00\x00IEND\xaeB`\x82"
+    )
+
     def warm(self):
         LOG.debug("Test model is warming...")
 
     def predict(self, **kwargs):
         LOG.debug("Got the following kw arguments: %s", kwargs)
-        return {
+        d = {
             "date": "2019-01-1",
             "labels": [{"label": "foo", "probability": 1.0}]
         }
+        if kwargs.get("accept") == "text/plain":
+            return str(d)
+        elif kwargs.get("accept") == "image/png":
+            return self._logo
+        return d
 
     def train(self, *args, **kwargs):
         sleep = kwargs.get("sleep", 1)
@@ -81,6 +116,12 @@ def get_predict_args(self):
                              "be one of the choices declared in 'enum'"),
                 enum=["foo", "bar"],
                 validate=validate.OneOf(["foo", "bar"]),
+            ),
+            "accept": fields.Str(
+                description=("Media type(s) that is/are acceptable for the "
+                             "response."),
+                validate=validate.OneOf(["text/plain", "image/png"]),
+                location="headers",
             )
         }
 

deepaas/tests/test_v2_api.py

@@ -92,6 +92,7 @@ async def test_model_not_found(self):
     async def test_predict_no_parameters(self):
         ret = await self.client.post("/v2/models/deepaas-test/predict/")
         json = await ret.json()
+        print(json)
         self.assertDictEqual(
             {
                 'parameter': ['Missing data for required field.'],

doc/source/user/v2-api.rst

@@ -241,6 +241,30 @@ In order to define a custom response, the ``response`` attribute is used:
 
         schema = Response
 
+Returning different content types
+*********************************
+
+Sometimes it is useful to return something different than a JSON file. For such
+cases, you can define an additional argument ``accept`` defining the content
+types that you are able to return as follows::
+
+   def get_predict_args():
+        return {
+            "accept": fields.Str(
+                description="Media type(s) that is/are acceptable for the response.",
+                validate=validate.OneOf(["text/plain"]),
+         }
+
+Consequantly, the predict calls will receive an ``accept`` argument containing
+the content type requested by the user.
+
+.. important:: Please be aware the the JSON content type (i.e.
+   ``application/json``) must be always supported and it will be defined and
+   presented to the user even if you do not define it in your ``accept``
+   argument. This means that if you do not receive an accept argument, or the
+   accept argument is empty you **must** return content that can be redered as
+   a JSON following the schema definition described above.
+
 Using classes
 -------------