updated readme snippets with template

Author: zeryx

README.md

@@ -104,6 +104,7 @@ def load():
     # The return object from this function can be passed directly as input to your apply function.
     # A great example would be any model files that need to be available to this algorithm
     # during runtime.
+
     # Any variables returned here, will be passed as the secondary argument to your 'algorithm' function
     globals = {}
     globals['payload'] = "Loading has been completed."
@@ -127,47 +128,38 @@ from PIL import Image
 import json
 from torchvision import models, transforms
 
-CLIENT = client()
-SMID_ALGO = "algo://util/SmartImageDownloader/0.2.x"
-LABEL_PATH = "data://AlgorithmiaSE/image_cassification_demo/imagenet_class_index.json"
-MODEL_PATHS = {
-    "squeezenet": 'data://AlgorithmiaSE/image_cassification_demo/squeezenet1_1-f364aa15.pth',
-    'alexnet': 'data://AlgorithmiaSE/image_cassification_demo/alexnet-owt-4df8aa71.pth',
-}
-
-
-def load_labels():
-    local_path = CLIENT.file(LABEL_PATH).getFile().name
+def load_labels(label_path, client):
+    local_path = client.file(label_path).getFile().name
     with open(local_path) as f:
         labels = json.load(f)
     labels = [labels[str(k)][1] for k in range(len(labels))]
     return labels
 
 
-def load_model(name):
+def load_model(name, model_paths, client):
     if name == "squeezenet":
         model = models.squeezenet1_1()
         models.densenet121()
-        weights = torch.load(CLIENT.file(MODEL_PATHS['squeezenet']).getFile().name)
+        weights = torch.load(client.file(model_paths["squeezenet"]).getFile().name)
     else:
         model = models.alexnet()
-        weights = torch.load(CLIENT.file(MODEL_PATHS['alexnet']).getFile().name)
+        weights = torch.load(client.file(model_paths["alexnet"]).getFile().name)
     model.load_state_dict(weights)
     return model.float().eval()
 
 
-def get_image(image_url):
-    input = {"image": image_url, "resize": {'width': 224, 'height': 224}}
-    result = CLIENT.algo(SMID_ALGO).pipe(input).result["savePath"][0]
-    local_path = CLIENT.file(result).getFile().name
+def get_image(image_url, smid_algo, client):
+    input = {"image": image_url, "resize": {"width": 224, "height": 224}}
+    result = client.algo(smid_algo).pipe(input).result["savePath"][0]
+    local_path = client.file(result).getFile().name
     img_data = Image.open(local_path)
     return img_data
 
 
 def infer_image(image_url, n, globals):
-    model = globals['model']
-    labels = globals['labels']
-    image_data = get_image(image_url)
+    model = globals["model"]
+    labels = globals["labels"]
+    image_data = get_image(image_url, globals["SMID_ALGO"], globals["CLIENT"])
     transformed = transforms.Compose([
         transforms.ToTensor(),
         transforms.Normalize(mean=[0.485, 0.456, 0.406],
@@ -186,30 +178,39 @@ def infer_image(image_url, n, globals):
 
 
 def load():
-    globals = {'model': load_model("squeezenet"), 'labels': load_labels()}
+    globals = {}
+    globals["MODEL_PATHS"] = {
+        "squeezenet": "data://AlgorithmiaSE/image_cassification_demo/squeezenet1_1-f364aa15.pth",
+        "alexnet": "data://AlgorithmiaSE/image_cassification_demo/alexnet-owt-4df8aa71.pth",
+    }
+    globals["LABEL_PATHS"] = "data://AlgorithmiaSE/image_cassification_demo/imagenet_class_index.json"
+    globals["CLIENT"] = client()
+    globals["SMID_ALGO"] = "algo://util/SmartImageDownloader/0.2.x"
+    globals["model"] = load_model("squeezenet", globals["MODEL_PATHS"], globals["CLIENT"])
+    globals["labels"] = load_labels(globals["LABEL_PATHS"], globals["CLIENT"])
     return globals
 
 
 def apply(input, globals):
     if isinstance(input, dict):
         if "n" in input:
-            n = input['n']
+            n = input["n"]
         else:
             n = 3
         if "data" in input:
-            if isinstance(input['data'], str):
-                output = infer_image(input['data'], n, globals)
-            elif isinstance(input['data'], list):
-                for row in input['data']:
-                    row['predictions'] = infer_image(row['image_url'], n, globals)
-                output = input['data']
+            if isinstance(input["data"], str):
+                output = infer_image(input["data"], n, globals)
+            elif isinstance(input["data"], list):
+                for row in input["data"]:
+                    row["predictions"] = infer_image(row["image_url"], n, globals)
+                output = input["data"]
             else:
-                raise Exception("'data' must be a image url or a list of image urls (with labels)")
+                raise Exception(""data" must be a image url or a list of image urls (with labels)")
             return output
         else:
-            raise Exception("'data' must be defined")
+            raise Exception(""data" must be defined")
     else:
-        raise Exception('input must be a json object')
+        raise Exception("input must be a json object")
 
 
 algorithm = ADK(apply_func=apply, load_func=load)