Context-Aware Outfit Suggestion Agent
A multi-modal morning assistant that determines your location, checks the weather, and generates funny, art-inspired outfit advice sent via Telegram and Email.
The Challenge
Decision fatigue in the mornings regarding what to wear based on fluctuating weather conditions, leading to wasted time.
The Solution
Built a location-aware pipeline that chains three different APIs (IP, Location, Weather) to provide context to an AI agent. The agent generates a 'Subject' and 'Body' for email, and a concise summary for Telegram, ensuring the user gets the info regardless of their preferred platform.
Implementation Details
This workflow acts as a personal stylist agent. It dynamically resolves the user's location using IP geolocation APIs, fetches granular weather data from wttr.in, and uses an LLM to generate a humorous, personality-driven outfit recommendation. Uniquely, it also queries the Art Institute of Chicago API to find an artwork matching the weather mood to include in the email.
Technologies Used
Skills Applied
Results Achieved
Saved approximately 10 minutes per day by automating the 'check weather -> decide outfit' cognitive loop.
Project Walkthrough
Challenges Faced
- ▸Ensuring accurate weather data without hardcoding user location
- ▸Reliably parsing JSON from the LLM to split content between Email (HTML) and Telegram (Text)
- ▸Finding relevant visual assets dynamically based on weather conditions
Solutions Implemented
- Chained `api.ipify.org` and `ip-api.com` to auto-detect location on every run
- Implemented a custom Tool within the workflow to query the Art Institute API for weather-matching imagery
- Added a robust `If` node and loop logic to handle validation errors and retry processing if the LLM output is malformed
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