intent.md

Product Requirements Document: WardrobeWizard

1. Executive Summary

Product Name: WardrobeWizard

Version: 1.0

Document Owner: Product Team

Last Updated: January 26, 2026

Purpose: WardrobeWizard is a Django-based web application that empowers users to digitize their physical wardrobes through photo uploads, leveraging machine learning to automatically categorize clothing items and generate personalized outfit recommendations.

---

2. Product Overview

2.1 Vision

To simplify wardrobe management and outfit planning by creating an intelligent digital closet that understands what users own and helps them make better styling decisions.

2.2 Target Audience

• Fashion-conscious individuals seeking better outfit coordination
• People with large wardrobes who struggle with organization
• Users interested in maximizing their existing wardrobe potential
• Individuals looking to track and plan their clothing purchases

2.3 Key Value Propositions

• Automated Organization: ML-powered classification eliminates manual tagging
• Smart Recommendations: AI-driven outfit suggestions based on wardrobe inventory
• Personal Style Archive: Visual history of all clothing items
• Time Savings: Quick outfit planning without physical closet searching

---

3. Goals and Success Metrics

3.1 Product Goals

1. Enable users to build a comprehensive digital wardrobe within their first week
2. Provide accurate clothing classification (>85% accuracy target)
3. Generate relevant outfit recommendations that users actually wear
4. Create an intuitive, mobile-friendly user experience

3.2 Key Performance Indicators (KPIs)

• User Engagement: Average items uploaded per user (target: 20+ items/month)
• Classification Accuracy: ML model accuracy rate (target: ≥85%)
• Retention: 30-day user retention rate (target: ≥40%)
• Recommendation Usage: Percentage of users who view outfit recommendations weekly (target: ≥50%)
• Upload Success Rate: Percentage of successful photo uploads (target: ≥95%)

---

4. User Stories and Use Cases

4.1 Core User Stories

As a new user, I want to:

• Create an account securely so I can access my wardrobe from any device
• Upload photos of my clothing items quickly
• See my items automatically categorized without manual tagging
• View my complete digital wardrobe in an organized layout

As a returning user, I want to:

• Get outfit recommendations based on what I already own
• Filter my wardrobe by category, color, or season
• Update or delete items I no longer own
• Track when I last wore specific items

As a style-conscious user, I want to:

• Receive outfit combinations I haven't considered before
• Save favorite outfit combinations for future reference
• Get recommendations for specific occasions (work, casual, formal)

4.2 Primary Use Cases

Use Case 1: First-Time Wardrobe Upload

• User registers and logs in
• Uploads 10-30 clothing photos in bulk
• System classifies each item automatically
• User reviews and confirms their digital wardrobe

Use Case 2: Daily Outfit Planning

• User logs in on mobile device
• Navigates to "Outfit Recommendations"
• Views 3-5 suggested outfits based on wardrobe
• Selects an outfit and marks it as "worn today"

Use Case 3: Wardrobe Maintenance

• User uploads new purchase
• System adds and classifies new item
• User removes items they've donated/discarded
• Profile reflects current wardrobe state

---

5. Functional Requirements

5.1 User Authentication and Profile Management

FR-1.1: User Registration

• Users must register with email and password
• Password requirements: minimum 8 characters, at least one uppercase, one number
• Email verification required before full access
• Success/error messaging for registration flow

FR-1.2: User Login/Logout

• Secure login with email and password
• Session management with configurable timeout (default: 14 days)
• "Remember me" option for persistent login
• Password reset functionality via email

FR-1.3: User Profile

• Display user's name, email, join date
• Show total wardrobe item count
• Profile photo upload (optional)
• Account settings: password change, email preferences

5.2 Image Upload and Storage

FR-2.1: Photo Upload

• Support for JPEG, PNG, WebP formats
• Maximum file size: 10MB per image
• Drag-and-drop upload interface
• Multi-file upload capability (up to 20 images simultaneously)
• Mobile camera integration for direct photo capture
• Upload progress indicator

FR-2.2: Image Processing

• Automatic image resizing/optimization for storage
• Thumbnail generation (200x200px) for gallery views
• Full-size image retention for detail views
• Background removal option (optional enhancement)

FR-2.3: Image Storage

• Secure cloud/local storage with user-specific directories
• Unique filename generation to prevent conflicts
• Image URL generation for frontend display
• Storage quota per user (default: 500 images or 2GB)

5.3 Machine Learning Classification

FR-3.1: Clothing Category Classification

• Automatic classification into predefined categories:
  • Tops: T-shirts, shirts, blouses, sweaters, hoodies
  • Bottoms: Jeans, pants, shorts, skirts
  • Dresses: Casual dresses, formal dresses
  • Outerwear: Jackets, coats, blazers
  • Footwear: Sneakers, boots, sandals, heels
  • Accessories: Hats, bags, scarves, belts
• Confidence score display for each classification
• Manual override capability for incorrect classifications

FR-3.2: Model Integration

• TensorFlow/Keras model served via Django backend
• Real-time inference on image upload (< 3 seconds per image)
• Batch processing for bulk uploads
• Model versioning and update capability without downtime

FR-3.3: Classification Metadata

• Store classification results in database
• Track classification confidence scores
• Log classification history for model improvement
• Allow user feedback on classification accuracy

5.4 Wardrobe Management

FR-4.1: Wardrobe Gallery View

• Grid layout displaying all user's clothing items
• Filter options: category, color, season, occasion
• Search functionality by item name or attributes
• Sort options: date added, most worn, least worn, category

FR-4.2: Item Detail View

• Full-size image display
• Classification details and confidence score
• Metadata: upload date, wear count, last worn date
• Edit options: recategorize, add tags, add notes
• Delete functionality with confirmation

FR-4.3: Item Editing

• Update category manually
• Add custom tags (colors, brands, seasons)
• Add personal notes
• Mark items as favorites
• Set item status (active, storage, donated)

5.5 Outfit Recommendations

FR-5.1: Recommendation Generation

• Algorithm considers:
  • Category compatibility (top + bottom + shoes)
  • Color harmony principles
  • Seasonal appropriateness
  • Occasion suitability
  • Wear frequency (suggest less-worn items)
• Generate 5-10 outfit suggestions daily
• Refresh recommendations on user request

FR-5.2: Recommendation Display

• Visual outfit cards showing 3-4 items combined
• "Like" and "Dislike" feedback for learning preferences
• "Save outfit" functionality
• "Wear this outfit" button to log usage

FR-5.3: Saved Outfits

• Personal outfit collection
• Edit saved outfits
• Share outfits (future enhancement)
• Outfit history/calendar view

FR-5.4: Recommendation Filters

• Filter by occasion: casual, work, formal, athletic
• Filter by season: spring, summer, fall, winter
• Weather-based suggestions (future enhancement)

5.6 Analytics and Insights

FR-6.1: Wardrobe Statistics

• Total items by category
• Most/least worn items
• Outfit diversity score
• Upload timeline graph

FR-6.2: Wear Tracking

• Log when outfits are worn
• Identify underutilized items
• Cost-per-wear calculations (if purchase price added)

---

6. Non-Functional Requirements

6.1 Performance

• Page load time: < 2 seconds for wardrobe gallery
• Image upload processing: < 5 seconds per image
• ML classification inference: < 3 seconds per image
• Concurrent user support: 100+ simultaneous users
• API response time: < 500ms for non-ML endpoints

6.2 Security

• HTTPS encryption for all communications
• Secure password hashing (bcrypt or Argon2)
• CSRF protection on all forms
• SQL injection prevention via Django ORM
• Image file validation to prevent malicious uploads
• User data isolation (users can only access their own data)
• Regular security audits and dependency updates

6.3 Scalability

• Horizontal scaling capability for Django application servers
• Database optimization for 10,000+ users
• CDN integration for image delivery
• Caching layer (Redis) for frequently accessed data
• Asynchronous task processing (Celery) for ML inference

6.4 Reliability

• 99.5% uptime target
• Automated database backups (daily)
• Error logging and monitoring (Sentry or similar)
• Graceful error handling with user-friendly messages
• Data redundancy for image storage

6.5 Usability

• Mobile-responsive design (Bootstrap or Tailwind CSS)
• Intuitive navigation with < 3 clicks to any feature
• Accessibility compliance (WCAG 2.1 AA)
• Progressive web app capabilities
• Cross-browser compatibility (Chrome, Firefox, Safari, Edge)

6.6 Maintainability

• Clean, documented codebase following PEP 8
• Comprehensive unit and integration tests (>80% coverage)
• Modular architecture for easy feature additions
• Version control with Git
• CI/CD pipeline for automated testing and deployment

---

7. Technical Architecture

7.1 Technology Stack

Backend:

• Framework: Django 4.x (Python 3.10+)
• Database: PostgreSQL 14+
• ORM: Django ORM
• Task Queue: Celery with Redis broker
• ML Framework: TensorFlow 2.x / Keras

Frontend:

• Template Engine: Django Templates or React integration
• CSS Framework: Bootstrap 5 or Tailwind CSS
• JavaScript: Vanilla JS or Alpine.js for interactivity
• Image Upload: Dropzone.js or similar

Infrastructure:

• Web Server: Gunicorn + Nginx
• Storage: AWS S3 or local filesystem with backup
• Caching: Redis
• Hosting: AWS EC2/Heroku/DigitalOcean
• CDN: CloudFlare or AWS CloudFront

7.2 Data Models

User Model (extends Django User)

• id (PrimaryKey)
• email (unique)
• password (hashed)
• profile_photo (optional)
• created_at
• last_login

ClothingItem Model

• id (PrimaryKey)
• user (ForeignKey to User)
• image_url
• thumbnail_url
• category (CharField with choices)
• classification_confidence (FloatField)
• custom_tags (JSONField or ManyToMany to Tag)
• notes (TextField)
• wear_count (IntegerField, default=0)
• last_worn_date (DateField, nullable)
• is_active (BooleanField, default=True)
• created_at
• updated_at

Outfit Model

• id (PrimaryKey)
• user (ForeignKey to User)
• name (CharField)
• items (ManyToMany to ClothingItem)
• occasion (CharField with choices)
• season (CharField with choices)
• is_saved (BooleanField)
• wear_count (IntegerField, default=0)
• created_at
• last_worn_date (DateField, nullable)

WearLog Model

• id (PrimaryKey)
• user (ForeignKey to User)
• outfit (ForeignKey to Outfit, nullable)
• items (ManyToMany to ClothingItem)
• date_worn (DateField)
• notes (TextField, optional)

7.3 API Endpoints

Authentication:

• POST /api/auth/register
• POST /api/auth/login
• POST /api/auth/logout
• POST /api/auth/password-reset

Wardrobe:

• GET /api/wardrobe/ (list all items)
• POST /api/wardrobe/upload (upload new item)
• GET /api/wardrobe/{id} (item detail)
• PUT /api/wardrobe/{id} (update item)
• DELETE /api/wardrobe/{id} (delete item)

Recommendations:

• GET /api/recommendations/ (get outfit suggestions)
• POST /api/recommendations/{id}/feedback (like/dislike)
• POST /api/recommendations/save (save outfit)
• GET /api/outfits/saved (list saved outfits)

Analytics:

• GET /api/analytics/stats (wardrobe statistics)
• GET /api/analytics/wear-history (wear log)

7.4 ML Model Specifications

Model Type: Convolutional Neural Network (CNN)

Input: 224x224 RGB images

Output: Softmax probability distribution across clothing categories

Training Data Requirements:

• Minimum 1,000 images per category
• Diverse backgrounds, lighting, angles
• Labeled dataset from Fashion-MNIST, DeepFashion, or custom collection

Model Performance Targets:

• Accuracy: ≥85%
• Precision/Recall: ≥80% per category
• Inference time: < 3 seconds on CPU

Model Deployment:

• Saved as .h5 or SavedModel format
• Loaded once at Django startup
• Cached in memory for fast inference
• Periodic retraining with user feedback data

---

8. User Interface Design

8.1 Key Pages/Views

Landing Page (Unauthenticated)

• Hero section with value proposition
• Feature highlights (3-4 key benefits)
• Call-to-action: "Get Started" button
• Login link

Registration/Login Page

• Clean form layout
• Social login options (future enhancement)
• Password strength indicator
• Clear error messaging

Dashboard (Post-Login)

• Quick stats: total items, outfits saved, items worn this week
• Recent uploads gallery
• Today's outfit recommendations preview
• Quick upload button

Wardrobe Gallery

• Grid layout (4-6 items per row, responsive)
• Category filter sidebar/dropdown
• Search bar at top
• Item thumbnails with category badges
• Hover effects for interactivity

Item Detail Modal/Page

• Large image display
• Classification info and confidence score
• Edit buttons: recategorize, add tags, delete
• Wear history
• "Add to outfit" button

Outfit Recommendations Page

• Carousel or card layout for outfit suggestions
• Each outfit shows 3-4 items arranged visually
• Like/dislike buttons
• "Wear this" and "Save" buttons
• Filters for occasion and season

User Profile

• Profile photo and basic info
• Wardrobe statistics dashboard
• Settings link
• Logout button

8.2 Mobile Considerations

• Bottom navigation bar for key features
• Camera integration for quick uploads
• Swipe gestures for outfit browsing
• Touch-optimized buttons and interactions

---

9. Development Phases

Phase 1: MVP (Weeks 1-6)

Core Features:

• User authentication (register, login, logout)
• Single image upload with basic validation
• ML model integration for classification
• Simple wardrobe gallery view
• Basic item detail page
• Responsive design foundation

Deliverables:

• Functional Django application
• Trained ML model integrated
• Basic UI with essential features
• User authentication working
• Image storage configured

Phase 2: Enhanced Wardrobe Management (Weeks 7-10)

Features:

• Multi-image batch upload
• Advanced filtering and search
• Item editing capabilities
• Image optimization and thumbnails
• Improved gallery UI/UX

Deliverables:

• Polished wardrobe interface
• Complete CRUD operations for items
• Optimized image pipeline

Phase 3: Outfit Recommendations (Weeks 11-14)

Features:

• Recommendation algorithm implementation
• Outfit generation based on wardrobe
• Saved outfits functionality
• Outfit feedback mechanism
• Occasion and season filters

Deliverables:

• Working recommendation engine
• Outfit management system
• User preference learning

Phase 4: Analytics and Polish (Weeks 15-16)

Features:

• Wardrobe analytics dashboard
• Wear tracking and logging
• Performance optimization
• UI/UX refinements
• Comprehensive testing

Deliverables:

• Production-ready application
• Analytics dashboard
• Full test coverage
• Deployment documentation

---

10. Testing Requirements

10.1 Unit Testing

• Model tests for all Django models
• View tests for all endpoints
• ML model prediction accuracy tests
• Utility function tests
• Target coverage: >80%

10.2 Integration Testing

• End-to-end user registration and login flow
• Image upload and classification pipeline
• Outfit generation and saving workflow
• Database transaction integrity

10.3 User Acceptance Testing

• Beta testing with 10-20 users
• Usability testing sessions
• Feedback collection and iteration
• Performance testing under load

10.4 Security Testing

• Penetration testing for authentication
• File upload vulnerability scanning
• SQL injection and XSS testing
• CSRF protection validation

---

11. Deployment and Operations

11.1 Deployment Strategy

• Staging environment for pre-production testing
• Blue-green deployment for zero-downtime updates
• Automated deployment via CI/CD pipeline
• Database migration strategy
• Rollback procedures

11.2 Monitoring

• Application performance monitoring (New Relic, DataDog)
• Error tracking (Sentry)
• User analytics (Google Analytics, Mixpanel)
• Server health monitoring
• ML model performance tracking

11.3 Maintenance

• Weekly dependency updates
• Monthly security patches
• Quarterly model retraining with new data
• Regular database optimization
• Backup verification and disaster recovery drills

---

12. Risks and Mitigations

Risk	Impact	Probability	Mitigation Strategy
Low ML accuracy affects user trust	High	Medium	Extensive model training, user feedback loop, manual override option
Image storage costs escalate	Medium	High	Implement storage quotas, image compression, archival policies
Slow classification inference	High	Medium	Model optimization, batch processing, async task queue
User adoption below target	High	Medium	Strong onboarding, marketing campaign, referral incentives
Security breach/data leak	Critical	Low	Security audits, encryption, access controls, regular updates
Scalability issues at 1,000+ users	Medium	Medium	Load testing, horizontal scaling setup, database optimization

---

13. Future Enhancements

Post-MVP Features (v2.0+)

• Social sharing of outfits
• Virtual try-on with AR
• Shopping suggestions based on wardrobe gaps
• Weather-based recommendations
• Calendar integration for outfit planning
• Collaborative wardrobes (couples, roommates)
• Laundry tracking and reminders
• Sustainability metrics (cost-per-wear, carbon footprint)
• Integration with fashion retailers
• Mobile native apps (iOS, Android)

---

14. Appendices

Appendix A: Glossary

• Wardrobe: Collection of all clothing items uploaded by a user
• Outfit: Combination of 2+ clothing items styled together
• Classification: ML-powered categorization of clothing items
• Wear Log: Historical record of when items/outfits were worn
• Confidence Score: ML model's certainty in classification (0-100%)

Appendix B: References

• Django Documentation: https://docs.djangoproject.com
• TensorFlow Guide: https://www.tensorflow.org/guide
• Fashion-MNIST Dataset: https://github.com/zalandoresearch/fashion-mnist
• DeepFashion Dataset: http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html

Appendix C: Change Log

• v1.0 (January 26, 2026): Initial PRD created

---

Document Status: Draft v1.0
Next Review Date: February 15, 2026
Approval Required From: Engineering Lead, Design Lead, Product Manager