Uses a Multimodal Language model with vision capability to estimate the nutrition content of food in given image.

• HuggingFace
• Gradio
• Torch
• fastapi
• uvicorn
• ffmpeg

I used Bunny as the Vision Language Model (VLM) or Multimodal Large Language Model (MLLM) because it is lightweight but still powerful.

Still there are some hallucinations. So using a better model might improve the usability of this application.

Compared to the typical LLMs, bunny is small and is based on phi-2 with 'only' 2.7B params. It uses SigLIP vision encoder with 0.4B params.

Bunny Repo

Scripts

• main
• bunny
• analyzer
• retriever
• extractor
• database
• knowledge_scraper

Knowledge base

• Scraped Articles
• Nutrition tables
• Food Images with captions

Diet Journal

• DietDB
• Journal Files (summary)

• Analyzer: takes the initial description prompt text and image to figure out what the meal is.
• Retriever: RAG to get the nutrition information from the nutrition knowledge base (KB)
• Extractor: Takes analyzer output and returns meal nutrition info
• Chronicler: takes extractor output and summarizes the meals and saves it into the diet journal

• Get image and text prompt from user.
• Does the image contain food?
• What food items are in the image?
• Retrieve KB to find nutrition info about each food item.
• What are the portion sizes of each item in the image?
• Based on this estimate the calories and amount of macronutrients
• Extract discrete diet data (Calories, Protein, Carbs, Fat, Fiber, list of Vitamins and Minerals were present)
• Save this as meal info in diet journal.

• each day is divided into meals
• each meal is divided into food items
• each food item has properties:
• • calories (number)
• • protein (n)
• • carbs (n)
• • fat (n)
• • fiber (n)
• • other (list of strings)
• the Total nutrition is calculated and updated for each day when a meal is added
• the information will be stored and summarized in human readable format so the user can view the journal independent of the application if they want to ** main database will have the daily diet objects and is linked to the summaries ** corresponding journal summary file as text/md file.

Analyses the diet journal to give insights about your nutrition based on your age, height, weight, activity, and in future possibly health conditions and other factors.

• Collect user data (height, weight, activity, age are the main ones to start with)
• Select time period (last week, last month, all/max)
• Retrieve the journals for the time period using RAG
• Prompt from user asking questions about their nutrition journey.

• Do finetuning on food image and caption dataset

• For assistant

Proof of concept for now.

• https://cloud.google.com/blog/products/ai-machine-learning/rapidly-build-an-application-in-gradio-power-by-a-generative-ai-agent
• https://medium.com/@drjulija/how-i-built-a-basic-rag-for-pdf-qa-in-a-few-lines-of-python-code-9849c32e59f0
• https://docs.trychroma.com/guides