Merge pull request #176 from elder-plinius/master

Nutrition

playground/demos/nutrition/nutrition.py

@@ -0,0 +1,99 @@
+import os
+import base64
+import requests
+from dotenv import load_dotenv
+from swarms.models import Anthropic, OpenAIChat
+from swarms.structs import Flow
+
+# Load environment variables
+load_dotenv()
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+# Define prompts for various tasks
+MEAL_PLAN_PROMPT = "Based on the following user preferences: dietary restrictions as vegetarian, preferred cuisines as Italian and Indian, a total caloric intake of around 2000 calories per day, and an exclusion of legumes, create a detailed weekly meal plan. Include a variety of meals for breakfast, lunch, dinner, and optional snacks."
+IMAGE_ANALYSIS_PROMPT = "Identify the items in this fridge, including their quantities and condition."
+
+# Function to encode image to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Initialize Language Model (LLM)
+llm = OpenAIChat(
+    openai_api_key=openai_api_key,
+    max_tokens=3000,
+)
+
+# Function to handle vision tasks
+def create_vision_agent(image_path):
+    base64_image = encode_image(image_path)
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {openai_api_key}"
+    }
+    payload = {
+        "model": "gpt-4-vision-preview",
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": IMAGE_ANALYSIS_PROMPT},
+                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
+                ]
+            }
+        ],
+        "max_tokens": 300
+    }
+    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
+    return response.json()
+
+# Function to generate an integrated shopping list considering meal plan and fridge contents
+def generate_integrated_shopping_list(meal_plan_output, image_analysis, user_preferences):
+    # Prepare the prompt for the LLM
+    fridge_contents = image_analysis['choices'][0]['message']['content']
+    prompt = (f"Based on this meal plan: {meal_plan_output}, "
+              f"and the following items in the fridge: {fridge_contents}, "
+              f"considering dietary preferences as vegetarian with a preference for Italian and Indian cuisines, "
+              f"generate a comprehensive shopping list that includes only the items needed.")
+
+    # Send the prompt to the LLM and return the response
+    response = llm(prompt)
+    return response  # assuming the response is a string
+
+# Define agent for meal planning
+meal_plan_agent = Flow(
+    llm=llm,
+    sop=MEAL_PLAN_PROMPT,
+    max_loops=1,
+    autosave=True,
+    saved_state_path="meal_plan_agent.json",
+)
+
+# User preferences for meal planning
+user_preferences = {
+    "dietary_restrictions": "vegetarian",
+    "preferred_cuisines": ["Italian", "Indian"],
+    "caloric_intake": 2000,
+    "other notes": "Doesn't eat legumes"
+}
+
+# Generate Meal Plan
+meal_plan_output = meal_plan_agent.run(
+    f"Generate a meal plan: {user_preferences}"
+)
+
+# Vision Agent - Analyze an Image
+image_analysis_output = create_vision_agent("full_fridge.jpg")
+
+# Generate Integrated Shopping List
+integrated_shopping_list = generate_integrated_shopping_list(meal_plan_output, image_analysis_output, user_preferences)
+
+# Print and save the outputs
+print("Meal Plan:", meal_plan_output)
+print("Integrated Shopping List:", integrated_shopping_list)
+
+with open("nutrition_output.txt", "w") as file:
+    file.write("Meal Plan:\n" + meal_plan_output + "\n\n")
+    file.write("Integrated Shopping List:\n" + integrated_shopping_list + "\n")
+
+print("Outputs have been saved to nutrition_output.txt")