Update app.py

app.py

@@ -14,30 +14,34 @@ 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)
 
-# Agent Definitions
+# 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."
+    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."
+    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."
+    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."
-)
+##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 and format output."
+    system_message="Coordinate the workflow, format the output, and return the final result as text.",
+    function_map={}
 )
 
 group_chat = GroupChat(
@@ -80,89 +84,86 @@ def fetch_nutrition(foods_with_sizes, nutritionix_key):
     if not nutritionix_key:
         return "Please provide a Nutritionix API key for nutrition data."
     
+    start = time.time()
     url = "https://trackapi.nutritionix.com/v2/natural/nutrients"
     headers = {
-        "x-app-id": "your_nutritionix_app_id",  # Your app ID in HF Secrets
-        "x-app-key": nutritionix_key,          # User's key
+        "x-app-id": os.getenv("NUTRITIONIX_APP_ID"),  # From HF Secrets
+        "x-app-key": nutritionix_key,                  # User's key
         "Content-Type": "application/json"
     }
     # Build query from foods and sizes
     query = "\n".join([f"{size}g {food}" for food, size in foods_with_sizes.items()])
     body = {"query": query}
     
-    response = requests.post(url, headers=headers, json=body)
-    if response.status_code != 200:
-        return f"Nutritionix API error: {response.text}"
-    
-    data = response.json().get("foods", [])
-    nutrition_data = {}
-    for item in data:
-        food_name = item["food_name"]
-        nutrition_data[food_name] = {
-            "calories": item.get("nf_calories", 0),
-            "protein": item.get("nf_protein", 0),
-            "fat": item.get("nf_total_fat", 0),
-            "carbs": item.get("nf_total_carbohydrate", 0)
-        }
-    return nutrition_data
-
-def get_nutrition_advice(nutrition_data, llm_key):
-    if not llm_key:
-        return "No OpenAI/Grok key provided—skipping advice."
     try:
-        openai.api_key = llm_key
-        prompt = "Given this nutritional data, suggest a dietary tip:\n"
-        for food, data in nutrition_data.items():
-            prompt += f"- {food}: {data['calories']} cal, {data['protein']}g protein, {data['fat']}g fat, {data['carbs']}g carbs\n"
+        response = requests.post(url, headers=headers, json=body, timeout=10)
+        if response.status_code != 200:
+            return f"Nutritionix API error: {response.text}"
         
-        response = openai.Completion.create(
-            model="text-davinci-003",  # Swap for Grok if xAI API is available
-            prompt=prompt,
-            max_tokens=50
-        )
-        return response.choices[0].text.strip()
+        data = response.json().get("foods", [])
+        nutrition_data = {}
+        for item in data:
+            food_name = item["food_name"]
+            nutrition_data[food_name] = {
+                "calories": item.get("nf_calories", 0),
+                "protein": item.get("nf_protein", 0),
+                "fat": item.get("nf_total_fat", 0),
+                "carbs": item.get("nf_total_carbohydrate", 0)
+            }
+        print(f"Nutrition fetch took {time.time() - start:.2f}s")
+        return nutrition_data
+    except requests.Timeout:
+        return "Nutritionix API timed out."
     except Exception as e:
-        return f"Error with LLM key: {str(e)}"
+        return f"Nutritionix error: {str(e)}"
 
-def orchestrate_workflow(image, nutritionix_key, llm_key):
-    # Step 1: Recognize foods
-    foods = recognize_foods(image)
-    if not foods:
-        return "No foods recognized. Try a clearer image!", ""
-    
-    # Step 2: Estimate sizes
-    sizes = estimate_sizes(image, foods)
-    
-    # Step 3: Fetch nutrition with user's Nutritionix key
-    nutrition = fetch_nutrition({food: size for food, _ in foods}, nutritionix_key)
-    if isinstance(nutrition, str):  # Error message
-        return nutrition, ""
-    
-    # Step 4: Generate advice with user's LLM key
-    #advice = get_nutrition_advice(nutrition, llm_key)
+#def get_nutrition_advice(nutrition_data, llm_key):
+#    if not llm_key:
+#        return "No OpenAI/Grok key provided—skipping advice."
+#    try:
+#        openai.api_key = llm_key
+#        prompt = "Given this nutritional data, suggest a dietary tip:\n"
+#        for food, data in nutrition_data.items():
+#            prompt += f"- {food}: {data['calories']} cal, {data['protein']}g protein, {data['fat']}g fat, {data['carbs']}g carbs\n"
+#        
+#        response = openai.Completion.create(
+#            model="text-davinci-003",  # Swap for Grok if xAI API is available
+#            prompt=prompt,
+#            max_tokens=50
+#        )
+#        return response.choices[0].text.strip()
+#    except Exception as e:
+#        return f"Error with LLM key: {str(e)}"
+
+# Orchestrator Logic (via AutoGen chat)
+def orchestrate_workflow(image, nutritionix_key):
+    start = time.time()
     
-    # Format output
-    result = "Food Analysis:\n"
-    for food, prob in foods:
-        if food in nutrition:
-            data = nutrition[food]
-            result += (f"- {food} ({sizes[food]}g, {prob:.2%} confidence): "
-                       f"{data['calories']} cal, {data['protein']:.1f}g protein, "
-                       f"{data['fat']:.1f}g fat, {data['carbs']:.1f}g carbs\n")
+    # Initiate chat with Orchestrator, passing image and key as message
+    message = f"Process this image: {image} with Nutritionix key: {nutritionix_key}"
+    response = manager.initiate_chat(
+        orchestrator,
+        message=message,
+        max_turns=10
+    )
     
-    return result  #, advice
+    # Extract and format the final response from the chat
+    result = response[-1].get("content", "No output from agents.")
+    print(f"Total time: {time.time() - start:.2f}s")
+    return result
 
+    
 # Gradio Interface
 interface = gr.Interface(
     fn=orchestrate_workflow,
     inputs=[
         gr.Image(type="numpy", label="Upload a Food Photo"),
         gr.Textbox(type="password", label="Your Nutritionix API Key (required)"),
-        gr.Textbox(type="password", label="Your OpenAI/Grok API Key (optional for advice)")
+        #gr.Textbox(type="password", label="Your OpenAI/Grok API Key (optional for advice)")
     ],
     outputs=[
         gr.Textbox(label="Nutrition Breakdown"),
-        gr.Textbox(label="Nutrition Advice")
+        #gr.Textbox(label="Nutrition Advice")
     ],
     title="Food Nutrition Analyzer",
     description="Upload a food photo and provide your Nutritionix API key. Add an OpenAI/Grok key for advice."