AI Personal Fashion Stylist - Agentic Design

• 

  FashionAI

Inspiration

What it does

Challenges we ran into

Accomplishments that we're proud of

What i learned

What's next for AI Personal Fashion Stylist - Agentic Design

The AI Fashion Stylist Pro: From Chaos to Clarity

The Inspiration We’ve all been there: standing in front of a closet full of clothes, yet having "nothing to wear." The real challenge isn't the lack of clothes; it’s the cognitive load of matching those clothes to a changing world—the sudden rainstorm, back-to-back Zoom calls, or a formal dinner. I wanted to build an agent that doesn't just "chat," but actually sees and reasons like a professional stylist.

How I Built It I architected the project as a Multimodal Agentic Loop Decision Making. Using FastAPI as the backbone and Streamlit for the interface, the app works in three stages:

Vision: Gemini 3 Flash analyzes a webcam feed to identify fabrics and styles.

Intelligence: The agent uses Function Calling to fetch real-time data from OpenWeather and Google Calendar.

Reasoning: It processes this context to find the "Winner Outfit."

The Challenges & Learning The biggest hurdle was the 429 Quota Limit. Facing a Limit: 0 error on Gemini 3 Pro for three days taught me the value of graceful degradation. I pivoted to a "future-proof" architecture: building a dynamic model-switcher that runs on Gemini 2.5 Flash Lite by default but unlocks Gemini 3’s "Thinking Mode" as soon as a Pro key is supplied. This taught me that elite software isn't just about the newest model—it’s about robust, flexible architecture.

Built With Core AI: Gemini 3 Flash / Gemini 2.5 Flash Lite (Google GenAI SDK)

Orchestration: Agentic Workflows & Function Calling

Backend: FastAPI (Python 3.12)

Frontend: Streamlit

Tools: OpenWeatherMap API, Google Calendar API, gTTS (Voice Summary)

DevOps: Docker, Python-dotenv

Built With

• api
• fastapi
• gemini3
• python
• streamlit