Update app.py

app.py

@@ -11,46 +11,6 @@ from ultralytics import YOLO
 
 model = YOLO("yolov8n.pt")  # Nano model for speed, fine-tune on food data later
 
-# Multi-label recognition model (placeholder - swap for fine-tuned multi-label later)
-recognizer = pipeline("image-classification", model=model)
-
-# AutoGen Agent Definitions
-food_recognizer = AssistantAgent(
-    name="FoodRecognizer",
-    system_message="Identify all food items in the image and return a list of (label, probability) pairs.",
-    function_map={"recognize_foods": recognize_foods}
-)
-
-size_estimator = AssistantAgent(
-    name="SizeEstimator",
-    system_message="Estimate portion sizes in grams for each recognized food based on the image.",
-    function_map={"estimate_sizes": estimate_sizes}
-)
-
-nutrition_fetcher = AssistantAgent(
-    name="NutritionFetcher",
-    system_message="Fetch nutritional data from the Nutritionix API using the user's key.",
-    function_map={"fetch_nutrition": fetch_nutrition}
-)
-
-##advice_agent = AssistantAgent(
-##    name="NutritionAdvisor",
-##    system_message="Provide basic nutrition advice using the user's OpenAI/Grok key."
-##)
-
-orchestrator = AssistantAgent(
-    name="Orchestrator",
-    system_message="Coordinate the workflow, format the output, and return the final result as text.",
-    function_map={}
-)
-
-group_chat = GroupChat(
-    agents=[food_recognizer, size_estimator, nutrition_fetcher, advice_agent, orchestrator],
-    messages=[],
-    max_round=10
-)
-manager = GroupChatManager(groupchat=group_chat)
-
 # Agent Functions
 def recognize_foods(image):
     start = time.time()
@@ -135,6 +95,45 @@ def fetch_nutrition(foods_with_sizes, nutritionix_key):
 #    except Exception as e:
 #        return f"Error with LLM key: {str(e)}"
 
+
+# AutoGen Agent Definitions
+food_recognizer = AssistantAgent(
+    name="FoodRecognizer",
+    system_message="Identify all food items in the image and return a list of (label, probability) pairs.",
+    function_map={"recognize_foods": recognize_foods}
+)
+
+size_estimator = AssistantAgent(
+    name="SizeEstimator",
+    system_message="Estimate portion sizes in grams for each recognized food based on the image.",
+    function_map={"estimate_sizes": estimate_sizes}
+)
+
+nutrition_fetcher = AssistantAgent(
+    name="NutritionFetcher",
+    system_message="Fetch nutritional data from the Nutritionix API using the user's key.",
+    function_map={"fetch_nutrition": fetch_nutrition}
+)
+
+##advice_agent = AssistantAgent(
+##    name="NutritionAdvisor",
+##    system_message="Provide basic nutrition advice using the user's OpenAI/Grok key."
+##)
+
+orchestrator = AssistantAgent(
+    name="Orchestrator",
+    system_message="Coordinate the workflow, format the output, and return the final result as text.",
+    function_map={}
+)
+
+group_chat = GroupChat(
+    agents=[food_recognizer, size_estimator, nutrition_fetcher, advice_agent, orchestrator],
+    messages=[],
+    max_round=10
+)
+manager = GroupChatManager(groupchat=group_chat)
+
+
 # Orchestrator Logic (via AutoGen chat)
 def orchestrate_workflow(image, nutritionix_key):
     start = time.time()