iMessage Food Photo Nutritional Analysis with GPT-4 Vision & Memory Storage

iMessage AI-Powered Smart Calorie Tracker

> 📌 What it looks like in use:

> This image shows a visual of the workflow in action. Use it for reference when replicating or customizing the template.

This n8n template transforms a user-submitted food photo into a detailed, friendly, AI-generated nutritional report — sent back seamlessly as a chat message. It combines OpenAI's visual reasoning, Postgres-based memory, and real-time messaging with Blooio to create a hands-free calorie and nutrition tracker.

🧠 Use Cases

Auto-analyze meals based on user-uploaded images. Daily/weekly/monthly diet summaries with no manual input. Virtual food journaling integrated into messaging apps. Nutrition companion for healthcare, fitness, and wellness apps.

📌 Good to Know

⚠️ This uses GPT-4 with image capabilities, which may incur higher usage costs depending on your OpenAI pricing tier. Review OpenAI’s pricing. The model uses visual reasoning and estimation to determine nutritional info — results are estimates and should not replace medical advice. Blooio is used for sending/receiving messages. You will need a valid API key and project set up with webhook delivery. A Postgres database is required for long-term memory (optional but recommended). You can use any memory node with it.

⚙️ How It Works

Webhook Trigger
The workflow begins when a message is received via Blooio. This webhook listens for user-submitted content, including any image attachments.

Image Validation and Extraction
A conditional check verifies the presence of attachments. If images are found, their URLs are extracted using a Code node and prepared for processing.

Image Analysis via AI Agent
Images are passed to an OpenAI-based agent using a custom system prompt that: Identifies the meal, Estimates portion sizes, Calculates calories, macros, fiber, sugar, and sodium, Scores the meal with a health and confidence rating, Responds in a chatty, human-like summary format.

Memory Integration
A Postgres memory node stores user interactions for recall and contextual continuity, allowing day/week/month reports to be generated based on cumulative messages.

Response Aggregation & Summary
Messages are aggregated and summarized by a second AI agent into a single concise message to be sent back to the user via Blooio.

Message Dispatch
The final message is posted back to the originating conversation using the Blooio Send Message API.

🚀 How to Use

The included webhook can be triggered manually or programmatically by linking Blooio to a frontend chat UI. You can test the flow using a manual POST request containing mock Blooio payloads. Want to use a different messages app? Replace the Blooio nodes with your preferred messaging API (e.g., Twilio, Slack, Telegram).

✅ Requirements

OpenAI API access with GPT-4 Vision or equivalent multimodal support. Blooio account with access to incoming and outgoing message APIs. Optional: Postgres DB (e.g., via Neon) for tracking message context over time.

🛠️ Customising This Workflow

Prompt Tuning**
Tailor the system prompt in the AI Agent node to fit specific diets (e.g., keto, diabetic), age groups, or regionally-specific foods.

Analytics Dashboards**
Hook up your Postgres memory to a data visualization tool for nutritional trends over time.

Multilingual Support**
Adjust the response prompt to translate messages into other languages or regional dialects.

Image Preprocessing**
Insert a preprocessing node before sending images to the model to resize, crop, or enhance clarity for better results.