Overview
WearCast is an AI-driven clothing recommendation system that provides personalized outfit suggestions based on weather conditions and user preferences. The system integrates Gemini AI for intelligent recommendations, Supabase for data management, and OpenWeatherMap API for real-time weather data.
Problem Statement
Choosing appropriate clothing based on weather conditions can be time-consuming and subjective. Users often struggle to match their personal style with weather-appropriate clothing, leading to discomfort or style compromises. WearCast solves this by combining AI-powered style analysis with real-time weather data to provide intelligent, personalized recommendations.
System Architecture
The system follows a modern full-stack architecture:
- Frontend: HTML, CSS, JavaScript for user interface
- Backend: Python-based API handling AI processing
- AI Engine: Google Gemini API for natural language understanding and recommendations
- Database: Supabase for user preferences and clothing data
- Weather Service: OpenWeatherMap API for location-based weather data
Technologies Used
Key Features
- Real-time weather integration for location-based recommendations
- AI-powered style analysis using Gemini AI
- Personalized outfit suggestions based on user preferences
- User profile management with Supabase
- Responsive web interface
Challenges & Solutions
Challenge 1: API Integration Complexity
Integrating multiple APIs (Gemini, OpenWeatherMap, Supabase) required careful error handling and data synchronization. I implemented a robust error handling system with retry logic and fallback mechanisms to ensure reliability.
Challenge 2: AI Response Quality
Ensuring consistent and relevant AI recommendations required extensive prompt engineering. I developed a structured prompt template system that provides context about weather, user preferences, and style guidelines to Gemini AI.
Challenge 3: Real-time Data Updates
Weather data needed to be fetched and processed efficiently. I implemented caching mechanisms and optimized API calls to reduce latency while maintaining data freshness.
What I Learned
- Advanced prompt engineering techniques for LLM-based applications
- Multi-API integration patterns and best practices
- Real-time data processing and caching strategies
- User experience design for AI-powered applications
- Supabase database management and authentication
Future Improvements
Potential enhancements include machine learning model fine-tuning for better recommendations, integration with e-commerce platforms for direct purchase links, and mobile app development for better accessibility.