✨ start fleshing out model running function

callbacks/segmentation.py

@@ -1,13 +1,45 @@
 from dash import callback, Input, Output, State, no_update
 from utils.annotations import Annotations
+from utils.data_utils import data
+import numpy as np
 
 
+# NEXT STEPS:
+# - this function returns a job ID, which would be associated with the workflow run on vaughan
+# - then we need another callback to pick up this ID and start polling for successful output
 @callback(
     Output("output-placeholder", "children"),
     Input("run-model", "n_clicks"),
     State("annotation-store", "data"),
+    State("project-name-src", "value"),
 )
-def run_model(n_clicks, annotation_store):
+def run_job(n_clicks, annotation_store, project_name):
+    # TODO: Check if someone needs annotations as inputs
     if n_clicks:
+
+        annotations = Annotations(annotation_store)
+        annotations.create_annotation_metadata()
+        annotations.create_annotation_mask(sparse=False) # TODO: Would sparse need to be true?
+
+        # Get metadata and annotation data
+        metadata = annotations.get_annotations()
+        mask = annotations.get_annotation_mask()
+
+        # Get raw images associated with each annotated slice
+        # Actually we can just pass the indices and have the job point to Tiled directly
+        img_idx = list(metadata.keys())
+        img = data[project_name]
+        raw = []
+        for idx in img_idx:
+            ar = img[int(idx)]
+            raw.append(ar)
+        raw = np.stack(raw)
+        mask = np.stack(mask)
+
+        # Some checks to validate that things are in the format we'd expect
+        print(metadata)
+        print(mask.shape)
+        print(raw.shape)
+
         return "Running the model..."
     return no_update

components/control_bar.py

@@ -340,13 +340,13 @@ def layout():
                                 ],
                             ),
                             dmc.Space(h=20),
-                            dmc.Center(
-                                # dmc.Button(
-                                #     "Save annotation",
-                                #     variant="light",
-                                #     style={"width": "160px", "margin": "5px"},
-                                # )
-                            ),
+                            # dmc.Center(
+                            #     # dmc.Button(
+                            #     #     "Save annotation",
+                            #     #     variant="light",
+                            #     #     style={"width": "160px", "margin": "5px"},
+                            #     # )
+                            # ),
                             dmc.Center(
                                 dmc.Button(
                                     "Export annotation",
@@ -359,13 +359,13 @@ def layout():
                         ],
                     ),
                     _accordion_item(
-                        "Model configuration",
-                        "carbon:ibm-watson-machine-learning",
-                        "run-model",
+                        title="Model configuration",
+                        icon="carbon:ibm-watson-machine-learning",
+                        value="run-model",
                         children=[
                             dmc.Center(
                                 dmc.Button(
-                                    "Run model",
+                                    children="Run model",
                                     id="run-model",
                                     variant="light",
                                     style={"width": "160px", "margin": "5px"},

utils/annotations.py

@@ -14,6 +14,9 @@ def __init__(self, annotation_store):
     def get_annotations(self):
         return self.annotations
 
+    def get_annotation_mask(self):
+        return self.annotation_mask
+
     def get_annotation_mask_as_bytes(self):
         buffer = io.BytesIO()
         np.save(buffer, self.annotation_mask)