Convert Modeldata to Dict

README.md

@@ -106,7 +106,7 @@ def load(modelData):
     # during runtime.
 
     # Any variables returned here, will be passed as the secondary argument to your 'algorithm' function
-    modelData.user_data['payload'] = "Loading has been completed."
+    modelData['payload'] = "Loading has been completed."
     return modelData
 
 
@@ -176,9 +176,9 @@ def infer_image(image_url, n, globals):
 
 def load(modelData):
 
-    modelData.user_data["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
-    modelData.user_data["model"] = load_model(modelData.get_model("squeezenet"))
-    modelData.user_data["labels"] = load_labels(modelData.get_model("labels"))
+    modelData["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
+    modelData["model"] = load_model(modelData.get_model("squeezenet"))
+    modelData["labels"] = load_labels(modelData.get_model("labels"))
     return modelData
 
 
@@ -190,10 +190,10 @@ def apply(input, modelData):
             n = 3
         if "data" in input:
             if isinstance(input["data"], str):
-                output = infer_image(input["data"], n, modelData.user_data)
+                output = infer_image(input["data"], n, modelData)
             elif isinstance(input["data"], list):
                 for row in input["data"]:
-                    row["predictions"] = infer_image(row["image_url"], n, modelData.user_data)
+                    row["predictions"] = infer_image(row["image_url"], n, modelData)
                 output = input["data"]
             else:
                 raise Exception("\"data\" must be a image url or a list of image urls (with labels)")
@@ -257,4 +257,4 @@ Verify that it works on pytest, then:
 ```commandline
 python -m twine upload -r pypi dist/*
 ```
-and you're done :)
+and you're done :)

adk/modeldata.py

@@ -10,8 +10,23 @@ def __init__(self, client, model_manifest_path):
         self.manifest_data = get_manifest(self.manifest_freeze_path)
         self.client = client
         self.models = {}
-        self.user_data = {}
-        self.system_data = {}
+        self.usr_key = "__user__"
+
+    def __getitem__(self, key):
+        return getattr(self, self.usr_key + key)
+
+    def __setitem__(self, key, value):
+        setattr(self, self.usr_key + key, value)
+
+    def data(self):
+        __dict = self.__dict__
+        output = {}
+        for key in __dict.keys():
+            if self.usr_key in key:
+                without_usr_key = key.split(self.usr_key)[1]
+                output[without_usr_key] = __dict[key]
+        return output
+
 
     def available(self):
         if self.manifest_data:

examples/loaded_state_hello_world/src/Algorithm.py

@@ -17,7 +17,7 @@ def load(modelData):
     # during runtime.
 
     # Any variables returned here, will be passed as the secondary argument to your 'algorithm' function
-    modelData.user_data['payload'] = "Loading has been completed."
+    modelData['payload'] = "Loading has been completed."
     return modelData
 
 

examples/pytorch_image_classification/src/Algorithm.py

@@ -53,9 +53,9 @@ def infer_image(image_url, n, globals):
 
 def load(modelData):
 
-    modelData.user_data["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
-    modelData.user_data["model"] = load_model(modelData.get_model("squeezenet"))
-    modelData.user_data["labels"] = load_labels(modelData.get_model("labels"))
+    modelData["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
+    modelData["model"] = load_model(modelData.get_model("squeezenet"))
+    modelData["labels"] = load_labels(modelData.get_model("labels"))
     return modelData
 
 
@@ -67,10 +67,10 @@ def apply(input, modelData):
             n = 3
         if "data" in input:
             if isinstance(input["data"], str):
-                output = infer_image(input["data"], n, modelData.user_data)
+                output = infer_image(input["data"], n, modelData)
             elif isinstance(input["data"], list):
                 for row in input["data"]:
-                    row["predictions"] = infer_image(row["image_url"], n, modelData.user_data)
+                    row["predictions"] = infer_image(row["image_url"], n, modelData)
                 output = input["data"]
             else:
                 raise Exception("\"data\" must be a image url or a list of image urls (with labels)")

tests/adk_algorithms.py

@@ -16,7 +16,7 @@ def apply_binary(input):
 
 def apply_input_or_context(input, model_data=None):
     if model_data:
-        return model_data.user_data
+        return model_data.data()
     else:
         return "hello " + input
 
@@ -30,7 +30,7 @@ def apply_successful_manifest_parsing(input, model_data):
 
 # -- Loading functions --- #
 def loading_text(modelData):
-    modelData.user_data['message'] = 'This message was loaded prior to runtime'
+    modelData['message'] = 'This message was loaded prior to runtime'
     return modelData
 
 
@@ -39,14 +39,14 @@ def loading_exception(modelData):
 
 
 def loading_file_from_algorithmia(modelData):
-    modelData.user_data['data_url'] = 'data://demo/collection/somefile.json'
-    modelData.user_data['data'] = modelData.client.file(modelData.user_data['data_url']).getJson()
+    modelData['data_url'] = 'data://demo/collection/somefile.json'
+    modelData['data'] = modelData.client.file(modelData['data_url']).getJson()
     return modelData
 
 
 def loading_with_manifest(modelData):
-    modelData.user_data["squeezenet"] = modelData.get_model("squeezenet")
-    modelData.user_data['labels'] = modelData.get_model("labels")
+    modelData["squeezenet"] = modelData.get_model("squeezenet")
+    modelData['labels'] = modelData.get_model("labels")
     # optional model
-    modelData.user_data['mobilenet'] = modelData.get_model("mobilenet")
+    modelData['mobilenet'] = modelData.get_model("mobilenet")
     return modelData