WebChic.ai 🎀
An AI-powered fashion advisor that provides personalized outfit analysis and recommendations. Built with Streamlit and powered by Google's Gemini AI, this web application helps users make informed decisions about their outfit choices by analyzing uploaded images and providing detailed styling advice.
Features
Outfit Analysis
Upload any outfit photo and receive detailed feedback on style, fit, and overall appearance.
Seasonal Recommendations
Get advice on the best time of year to wear your outfit based on style and fabric analysis.
Occasion Matching
Learn which events and settings best suit your ensemble for optimal style impact.
Accessory Suggestions
Receive personalized recommendations for complementary accessories to enhance your look.
Time-of-Day Guidance
Understand whether your outfit is better suited for day or night occasions.
Style Categorization
Get insights into your outfit's style category and fashion classification.
Prerequisites
Before running the application, make sure you have:
Python 3.7+
Python version 3.7 or higher for compatibility
Google API Key
A Google API key for Gemini AI integration
Required Packages
All Python packages listed in the requirements section
Requirements
python-dotenv
Environment variable management
streamlit
Web framework for the user interface
google.generativeai
Google's Gemini AI integration
Pillow
Image processing and manipulation
Setup
Clone the Repository
Create Environment File
Create a .env file in the root directory and add your Google API key:
Install Dependencies
Run the Application
Usage
Launch Application
Use the command above to start the Streamlit app
Upload Image
Upload an image of an outfit using the file uploader
Analyze Outfit
Click the "Analyze Outfit" button to start the AI analysis
Wait for Processing
Wait for the AI to process your image and generate insights
Review Results
Review the detailed analysis and recommendations provided
Features in Detail
Image Analysis
Supported Formats
Supports JPG, JPEG, and PNG image formats
Auto Resizing
Automatically resizes images while maintaining aspect ratio
Optimal Performance
Maximum height of 500 pixels for optimal processing performance
AI Analysis Includes
Seasonal Timing
Best months to wear the outfit based on style and fabric
Time Suitability
Suitable times of day for the outfit
Occasion Matching
Appropriate occasions and events
Style Classification
Style categorization and fashion classification
Accessory Tips
Complementary accessory suggestions
UI Features
Responsive Design
Modern gradient theme with responsive layout
Intuitive Interface
Clear and user-friendly interface design
Loading Feedback
Loading spinner during analysis for better UX
Organized Layout
Two-column layout with visual hierarchy
Customization
The application includes custom CSS styling that can be modified in the st.markdown section of the code. Key customizable elements include:
Color Schemes
Customize primary and secondary color palettes
Typography
Adjust font sizes and typography hierarchy
Button Styles
Modify button appearance and hover effects
Layout Spacing
Adjust margins, padding, and component spacing
Technical Details
Streamlit Frontend
Built with Streamlit for rapid web app development
Gemini 2.0 Flash
Uses Google's Gemini 2.0 Flash model for image analysis
Environment Management
Implements secure environment variable management
Responsive Design
Features responsive design with custom CSS styling
Error Handling
Includes comprehensive error handling for file uploads
Performance Optimized
Optimized for fast processing and user experience
Contributing
Open Source
Contributions are welcome! Please feel free to submit a Pull Request for improvements, bug fixes, or new features.
Support
Contact
For support, please contact me at tripathilakshya9@gmail.com
Acknowledgments
Google Generative AI
For powering the intelligent image analysis capabilities
Streamlit
For the excellent web framework that makes building data apps simple
Fashion Community
For inspiration and feedback on fashion AI applications
Open Source
For the amazing tools and libraries that make innovation possible