feat(algo): implements an optimized readable breadth first search app…

whatTasteIsItLike.py

@@ -1,10 +1,38 @@
+# def describe_items(food_items, color):
+#     # Write your code here.
+#     apple_taste = food_items['fruits']['temperate']['apple']['taste']
+#     beet_taste = food_items['vegetables']['root']['beet']['taste']
+#     return [f"The apple is {apple_taste}.", f"The beet is {beet_taste}."] 
+
+from collections import deque
+# iterative approach (most-effiecient O(N)- Breadth first Search)
 def describe_items(food_items, color):
-    # Write your code here.
-    apple_taste = food_items['fruits']['temperate']['apple']['taste']
-    beet_taste = food_items['vegetables']['root']['beet']['taste']
-    return [f"The apple is {apple_taste}.", f"The beet is {beet_taste}."] 
-
+  result = []
+  queue = deque(food_items.items())
+
+  while queue:
+    item_name , val = queue.popleft()
+    # print("\nCurrent Queue Contents:") for debugging 
+    # for item in queue:
+    #     print(item)
+    if isinstance(val, dict): # check if the current value is a dict 
+      # then we have 2 possibilites 
+      if val.get('color') == color:
+        result.append(f"The {item_name} is {val['taste']}.")
+      else: # Add child items to the queue for further processing
+        queue.extend(val.items()) # we add unprocessed items to the right using extend and we pop from the left "BFS Under the hood to completely process the dict as a tree of nodes"
+
+  return result
+
+
+
+"""
+levels traversed in BFS :  TIME COMPLEXITY IS O(N)
 
+Level 1: 2 nodes (fruits, vegetables)
+Level 2: 4 nodes (tropical, temperate, leafy, root)
+Level 3: 8 nodes (mango, pineapple, apple, pear, spinach, kale, carrot, beet)
+"""
 
 
 # This is how your code will be called.
@@ -65,3 +93,5 @@ def describe_items(food_items, color):
     }
 }
 #* ['The apple is sweet.', 'The beet is earthy.'] is the expected output.
+# print(food_items.items())
+print(describe_items(food_items, 'red'))