Merge branch 'chore/render-method-renaming' into 'master'

chore: rename `render_status_update` -> `render_current_status_as_markdown`

See merge request !2

evaluator.py

@@ -29,8 +29,14 @@ class AIcrowdEvaluator:
             ground_truth_data_path=Constants.GROUND_TRUTH_PATH,
             predictions_file_path=Constants.PREDICTIONS_FILE_PATH,
         )
-
-    def render_status_update(self) -> str:
+    
+    def live_evaluate(self, ground_truth_path: str, predictions_file_path: str) -> dict:
+        # Not compulsory but encouraged to do so.
+        # For long running evaluations, this will help the participants understand the performance
+        # of their models before the completion of the evaluation.
+        return self.evaluator.scoring(ground_truth_path, prediction_file_path, partial_score=True)
+
+    def render_current_status_as_markdown(self) -> str:
         """This method updates the GitLab issue page comment with the current evaluation
         progress. The content returned should be in markdown.
 
@@ -55,10 +61,9 @@ class AIcrowdEvaluator:
             progress_percent = len(predictions) * 100 / total_image_count
 
             # Get live scores for predictions so far
-            scores = self.evaluator.scoring(
+            scores = self.live_evaluate(
                 Constants.GROUND_TRUTH_PATH,
                 Constants.PREDICTIONS_FILE_PATH,
-                partial_score=True,
             )
         except:
             progress_percent = 0

readme.md

@@ -78,8 +78,15 @@ class AIcrowdEvaluator:
   def __init__(self, **kwargs):
     pass
   
-  def render_status_update(self) -> str:
+  def render_current_status_as_markdown(self) -> str:
     return ""
+  
+  def live_evaluate(self) -> str:
+    # Not compulsory but encouraged to do so.
+    # For long running evaluations, this will help the participants understand the performance
+    # of their models before the completion of the evaluation.
+    # Refer example evaluator.py at https://gitlab.aicrowd.com/-/tree/master/evaluator.py
+    return {}
 
   def evaluate(self):
     scores = {