Update app.py

app.py

@@ -7,7 +7,7 @@ import requests
 import os
 import time
 from autogen import AssistantAgent, GroupChat, GroupChatManager
-import openai 
+import openai
 
 # Initialize YOLOv8 for multi-label food detection
 model = YOLO("yolov8n.pt")  # Nano model for speed, fine-tune on food data later
@@ -34,12 +34,12 @@ def recognize_foods(image):
     for result in results:
         for cls in result.boxes.cls:
             label = model.names[int(cls)]
-            if "food" in label.lower() or label in ["pasta", "rice", "tomato", "potato", "bread", "curry"]:  # Expand this list
+            if "food" in label.lower() or label in ["broccoli", "carrot", "green bean", "chicken", "turkey", "pasta", "rice", "tomato", "potato", "bread", "curry"]:  # Expanded list
                 conf = result.boxes.conf[result.boxes.cls == cls].item()
                 foods.append((label, conf))
                 detected = True
     if not detected:
-        print("Warning: No food items detected in the image.")
+        print("Warning: No food items detected in the image. Check YOLOv8 model or image quality.")
     print(f"Recognition took {time.time() - start:.2f}s: Found foods {foods}")
     return list(set(foods))  # Remove duplicates
 
@@ -157,25 +157,25 @@ def get_nutrition_advice(nutrition_data, openai_key):
 # 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. Call recognize_foods with the image provided in the message.",
+    system_message="Identify all food items in the image and return a list of (label, probability) pairs. Parse the message for the image data and call recognize_foods with it.",
     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. Call estimate_sizes with the image and list of foods from the previous message.",
+    system_message="Estimate portion sizes in grams for each recognized food based on the image. Parse the previous message for the list of foods and call estimate_sizes with the image and foods.",
     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. Call fetch_nutrition with the foods and sizes dictionary from the previous message and the Nutritionix key from the initial message.",
+    system_message="Fetch nutritional data from the Nutritionix API using the user's key. Parse the previous message for the foods and sizes dictionary and the initial message for the Nutritionix key, then call fetch_nutrition.",
     function_map={"fetch_nutrition": fetch_nutrition}
 )
 
 advice_agent = AssistantAgent(
     name="NutritionAdvisor",
-    system_message="Provide basic nutrition advice based on the food data using the user's OpenAI key. Call get_nutrition_advice with the nutrition data from the previous message and the OpenAI key from the initial message.",
+    system_message="Provide basic nutrition advice based on the food data using the user's OpenAI key. Parse the previous message for the nutrition data and the initial message for the OpenAI key, then call get_nutrition_advice.",
     function_map={"get_nutrition_advice": get_nutrition_advice}
 )