nutrition-ai 0.1.3

A Python client for the Passio Nutrition AI API

Nutrition AI

Nutrition AI is a Python module that interfaces with the Passio Nutrition AI API. It provides functionalities for food product information retrieval, food searching, and interaction with an AI nutrition advisor.

Features

• Authentication with the Passio API
• Fetching food product information by UPC, food ID, or reference code
• Searching for foods by name
• Interaction with an AI nutrition advisor, including:
  • Starting conversation threads
  • Sending messages and receiving responses
  • Handling complex queries requiring additional data
  • Utilizing content tool hints for enhanced responses
  • Visual food extraction from images

Installation

You can install the Nutrition AI module using pip:

pip install nutrition-ai

You will need an API key from Passio to use this module. If you do not have one, you can obtain one here

Configuration

Before using the module, you need to set up your Passio API key as an environment variable:

export PASSIO_API_KEY=your_api_key_here

Usage

We've included a Jupyter notebook here which shows how to use the Nutrition AI module.

Here are some examples of how to use the Nutrition AI module:

Authentication

from nutrition_ai import auth

header, expiry = auth.get_header_and_expiry_time()

Fetching Product Information

from nutrition_ai import fetch

# Fetch by UPC
product = fetch.product_by_upc('698241110109', header)

# Fetch by food ID
product = fetch.product_by_food_id('1603211317047', header)

# Fetch by reference code
product = fetch.product_by_refCode('your_ref_code', header)

Searching for Foods

from nutrition_ai import search

results = search.food_matching('pepperoni pizza', header=header, limit=5)

Interacting with the AI Advisor

from nutrition_ai.advisor import conversation, tools

# Start a conversation thread
thread_response = conversation.start_thread(header)

# Send a simple message
message_response = conversation.send_message(
    thread_response.threadId, 
    'what does vitamin C do, briefly?', 
    header
)

# Handle complex queries
complex_response = conversation.send_message(
    thread_response.threadId,
    'what do you think of my food logs this week?',
    header
)

if complex_response.dataRequest:
    log_response = tools.detect_meal_logs_required(
        complex_response.threadId,
        complex_response.messageId,
        complex_response.dataRequest.toolCallId,
        complex_response.dataRequest.runId,
        "21 Pizzas",
        header
    )

# Use content tool hints
if 'SearchIngredientMatches' in response.contentToolHints:
    search_response = tools.search_ingredient_matches(
        response.threadId,
        response.messageId,
        header
    )

# Visual food extraction
from PIL import Image

img = Image.open("path/to/your/image.jpg").resize((512, 512))
response = tools.visual_food_extraction(
    thread_response.threadId,
    img,
    message="what did I have for lunch?",
    header=header
)

Running Tests

To run the unit tests for this module:

1. Ensure you have set the PASSIO_API_KEY environment variable.
2. Run the following command from the root of the project:

python -m unittest tests/test_nutrition_ai.py

License

MIT

Contact

For any queries or support, please contact [e-mail].