Food image analysis for calorie estimation with Vision AI and Telegram

Who’s it for

Teams building health/fitness apps, coaches running check-ins in chat, and anyone who needs quick, structured nutrition insights from food photos—without manual logging.

What it does / How it works

This workflow accepts a food image (URL or Base64), uses a vision-capable LLM to infer likely ingredients and rough gram amounts, estimates per-ingredient calories, and returns a strict JSON summary with total calories and a short nutrition note. It normalizes different payloads (e.g., Telegram/LINE/Webhook) into a common format, handles transient errors with retries, and avoids hardcoded secrets by using credentials/env vars.

Requirements

Vision-capable LLM credentials (e.g., gpt-4o or equivalent) One input channel (Webhook, Telegram, or LINE) Environment variables for model name/temperature and optional request validation

How to set up

1. Connect your input channel and enable the Webhook (copy the test URL). 2. Add LLM credentials and set LLMMODEL and LLMTEMPERATURE (e.g., 0.3). 3. Turn on the workflow, send a sample payload with imageUrl, and confirm the strict JSON output. 4. (Optional) Configure a reply node (Telegram/Slack or HTTP Response) and a logger (Google Sheets/Notion).