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swarms/examples/demos/nutrition/nutrition_example.py

140 lines
3.9 KiB

import base64
import os
import requests
from dotenv import load_dotenv
from swarm_models import OpenAIChat
from swarms.structs import Agent
# 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}, and the"
f" following items in the fridge: {fridge_contents},"
" considering dietary preferences as vegetarian with a"
" preference for Italian and Indian cuisines, 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 = Agent(
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")