diff --git a/AIROADMAP.md b/AIROADMAP.md new file mode 100644 index 0000000..3dd2a94 --- /dev/null +++ b/AIROADMAP.md @@ -0,0 +1,197 @@ +# AI Roadmap for Photo Tagging, Classification, and Search + +## Current State +- ✅ **Dual-Model Classification**: ViT (objects) + CLIP (style/artistic concepts) +- ✅ **Image Captioning**: BLIP for natural language descriptions +- ✅ **Batch Processing**: Auto-tag and caption entire photo libraries +- ✅ **Tag Management**: Create, clear, and organize tags with UI +- ✅ **Performance Optimized**: Thumbnail-first processing with fallbacks + +## Phase 1: Enhanced Classification Models (Q1 2024) + +### 1.1 Specialized Domain Models +- **Face Recognition**: Add `Xenova/face-detection` for person identification + - Detect and count faces in photos + - Age/gender estimation capabilities + - Group photos by detected people +- **Scene Classification**: `Xenova/vit-base-patch16-224-scene` + - Indoor vs outdoor scene detection + - Specific location types (kitchen, bedroom, park, etc.) +- **Emotion Detection**: Face-based emotion classification + - Happy, sad, surprised, etc. from facial expressions + +### 1.2 Multi-Modal Understanding +- **OCR Integration**: `Xenova/trocr-base-printed` for text in images + - Extract text from signs, documents, screenshots + - Automatic tagging based on detected text content +- **Color Analysis**: Implement dominant color extraction + - Tag photos by color palette (warm, cool, monochrome) + - Season detection based on color analysis +- **Quality Assessment**: Technical photo quality scoring + - Blur detection, exposure analysis, composition scoring + +### 1.3 Fine-tuned Photography Models +- **Photography-Specific CLIP**: Train on photography datasets + - Better understanding of camera techniques + - Lens types, shooting modes, creative effects +- **Art Style Classification**: Historical and contemporary art styles + - Renaissance, Impressionist, Modern, Street Art, etc. + +## Phase 2: Advanced Search and Discovery (Q2 2024) + +### 2.1 Semantic Search +- **Vector Embeddings**: Store CLIP embeddings for each photo + - Enable "find similar photos" functionality + - Search by natural language descriptions +- **Hybrid Search**: Combine text search with visual similarity + - "Find beach photos that look like this sunset" + - Cross-modal search capabilities + +### 2.2 Intelligent Grouping +- **Event Detection**: Group photos by time/location/people + - Automatic album creation for trips, parties, holidays +- **Duplicate Detection**: Advanced perceptual hashing + - Find near-duplicates and variations + - Suggest best photo from similar shots +- **Series Recognition**: Detect photo sequences/bursts + - Panorama detection, HDR sequences, time-lapses + +### 2.3 Content-Aware Filtering +- **Smart Collections**: AI-generated photo collections + - "Best portraits", "Golden hour photos", "Action shots" +- **Contextual Recommendations**: Suggest photos based on current view + - "More photos like this", "From the same event" +- **Quality Filtering**: Automatically hide blurry/poor quality photos + +## Phase 3: Personalized AI Assistant (Q3 2024) + +### 3.1 Learning User Preferences +- **Favorite Detection**: Learn what makes users favorite photos + - Personalized quality scoring + - Suggest photos to review/favorite +- **Custom Label Training**: User-specific classification + - Train on user's existing tags + - Recognize personal objects, places, people + +### 3.2 Interactive Tagging +- **Tag Suggestions**: AI-powered tag recommendations during manual tagging +- **Batch Validation**: Review and approve AI-generated tags + - Confidence scoring with user feedback loop +- **Active Learning**: Improve models based on user corrections + +### 3.3 Natural Language Interface +- **Query Understanding**: Parse complex natural language searches + - "Show me outdoor photos from last summer with more than 3 people" +- **Photo Descriptions**: Generate detailed alt-text for accessibility +- **Story Generation**: Create narratives from photo sequences + +## Phase 4: Advanced Computer Vision (Q4 2024) + +### 4.1 Object Detection and Segmentation +- **YOLO Integration**: `Xenova/yolov8n` for precise object detection + - Bounding boxes around detected objects + - Count objects in photos (5 people, 3 cars, etc.) +- **Segmentation Models**: `Xenova/sam-vit-base` for object segmentation + - Extract individual objects from photos + - Background removal capabilities + +### 4.2 Spatial Understanding +- **Depth Estimation**: `Xenova/dpt-large` for depth perception + - Understand 3D structure of photos + - Foreground/background classification +- **Pose Estimation**: Human pose detection in photos + - Activity recognition (running, sitting, dancing) + - Sports/exercise classification + +### 4.3 Temporal Analysis +- **Video Frame Analysis**: Extract keyframes from videos + - Apply photo AI models to video content +- **Motion Detection**: Analyze camera movement and subject motion +- **Sequence Understanding**: Understand photo relationships over time + +## Phase 5: Multimodal AI Integration (2025) + +### 5.1 Audio-Visual Analysis +- **Audio Classification**: For photos with associated audio/video + - Environment sounds, music, speech detection +- **Cross-Modal Retrieval**: Search photos using audio descriptions + +### 5.2 3D Understanding +- **Stereo Vision**: Process photo pairs for depth information +- **3D Scene Reconstruction**: Build 3D models from photo sequences +- **AR/VR Integration**: Spatial photo organization in 3D space + +### 5.3 Advanced Generation +- **Style Transfer**: Apply artistic styles to photos locally +- **Photo Enhancement**: AI-powered photo improvement + - Denoising, super-resolution, colorization +- **Creative Variants**: Generate artistic variations of photos + +## Technical Implementation Strategy + +### Model Selection Criteria +1. **Size Constraints**: Prioritize smaller models (<500MB each) +2. **Performance**: Ensure real-time processing on consumer hardware +3. **Accuracy**: Balance model size vs classification quality +4. **Compatibility**: Ensure Transformers.js support + +### Infrastructure Enhancements +- **Model Caching**: Intelligent model loading/unloading +- **Web Workers**: Background processing to maintain UI responsiveness +- **Progressive Loading**: Load models on-demand based on user actions +- **Offline Support**: Full functionality without internet connection + +### Data Management +- **Embedding Storage**: Efficient vector storage for similarity search +- **Incremental Processing**: Process only new/changed photos +- **Backup Integration**: Sync AI-generated metadata across devices + +## Success Metrics + +### User Experience +- **Search Accuracy**: Percentage of successful photo searches +- **Tagging Efficiency**: Reduction in manual tagging time +- **Discovery Rate**: How often users find unexpected relevant photos + +### Performance +- **Processing Speed**: Photos processed per minute +- **Memory Usage**: RAM consumption during batch operations +- **Model Load Time**: Time to initialize AI models + +### Quality +- **Tag Precision**: Accuracy of automatically generated tags +- **User Satisfaction**: Approval rate of AI suggestions +- **Coverage**: Percentage of photos with meaningful tags + +## Resource Requirements + +### Development +- **Model Research**: Evaluate and test new Transformers.js models +- **Performance Optimization**: GPU acceleration, WebGL optimizations +- **UI/UX Design**: Intuitive interfaces for AI-powered features + +### Infrastructure +- **Testing Framework**: Automated testing for AI model accuracy +- **Benchmarking**: Performance testing across different hardware +- **Documentation**: User guides for AI features + +## Risk Mitigation + +### Privacy & Security +- **Local Processing**: All AI models run locally, no data leaves device +- **Data Encryption**: Encrypt AI-generated metadata +- **User Control**: Always allow manual override of AI decisions + +### Performance +- **Graceful Degradation**: Fallback to simpler models on low-end devices +- **Memory Management**: Prevent out-of-memory errors during batch processing +- **User Feedback**: Clear progress indicators and cancellation options + +### Model Updates +- **Backward Compatibility**: Ensure new models work with existing data +- **Migration Tools**: Convert between different model outputs +- **Version Management**: Track which AI models generated which tags + +--- + +This roadmap prioritizes **local-first AI** with no cloud dependencies, ensuring privacy while delivering powerful photo organization capabilities. Each phase builds upon previous work while introducing new capabilities for comprehensive photo understanding and search. \ No newline at end of file diff --git a/API_DOCUMENTATION.md b/API_DOCUMENTATION.md new file mode 100644 index 0000000..34cd791 --- /dev/null +++ b/API_DOCUMENTATION.md @@ -0,0 +1,198 @@ +# API Documentation Setup Guide + +## Overview + +I've set up **automatic API documentation** using `next-swagger-doc` that stays in sync with your code changes. Here's how it works: + +## ✅ What's Implemented + +### 1. **Documentation Generator** (`src/lib/swagger.ts`) +- Automatically scans your API routes in `src/app/api/` +- Generates OpenAPI 3.0 spec from JSDoc comments +- Includes all schemas, examples, and descriptions + +### 2. **Documentation Viewer** (`/api/docs/page`) +- Interactive Swagger UI interface +- Try-it-out functionality for testing endpoints +- Dark/light mode support + +### 3. **API Endpoint** (`/api/docs`) +- Serves the generated OpenAPI spec as JSON +- Can be consumed by external tools + +## 🚀 Usage + +### Access Documentation +Visit `http://localhost:3000/api/docs/page` to see your interactive API documentation. + +### Add Documentation to New Routes + +Add JSDoc comments above your route handlers: + +```typescript +/** + * @swagger + * /api/your-endpoint: + * post: + * summary: Brief description of what this endpoint does + * description: Detailed description with more context + * tags: [YourTag] + * requestBody: + * required: true + * content: + * application/json: + * schema: + * type: object + * properties: + * param1: { type: 'string', description: 'Parameter description' } + * param2: { type: 'number', minimum: 0, maximum: 1 } + * responses: + * 200: + * description: Success response + * content: + * application/json: + * schema: + * type: object + * properties: + * result: { type: 'string' } + * 400: + * description: Bad request + * content: + * application/json: + * schema: + * $ref: '#/components/schemas/Error' + */ +export async function POST(request: NextRequest) { + // Your route handler code +} +``` + +## 📋 Example Documentation + +I've already documented the **batch classification endpoint** as an example: + +```typescript +/** + * @swagger + * /api/classify/batch: + * post: + * summary: Batch classify photos using AI models + * description: Process multiple photos with AI classification using ViT for objects and optionally CLIP for artistic/style concepts. + * tags: [Classification] + * requestBody: + * content: + * application/json: + * schema: + * $ref: '#/components/schemas/BatchClassifyRequest' + * examples: + * basic: + * summary: Basic batch classification + * value: + * limit: 10 + * minConfidence: 0.3 + * onlyUntagged: true + * comprehensive: + * summary: Comprehensive mode with dual models + * value: + * comprehensive: true + * minConfidence: 0.05 + * maxResults: 25 + */ +``` + +## 🎯 Benefits + +### 1. **Always Up-to-Date** +- Documentation is generated from your actual code +- No separate docs to maintain +- Automatically reflects API changes + +### 2. **Interactive Testing** +- Built-in "Try it out" functionality +- Test endpoints directly from documentation +- See real request/response examples + +### 3. **Developer Experience** +- Comprehensive schemas and examples +- Clear parameter descriptions +- Error response documentation + +### 4. **Integration Ready** +- Standard OpenAPI 3.0 format +- Can be imported into Postman, Insomnia +- Works with code generators + +## 🔧 Extending Documentation + +### Add More Route Documentation + +For each API route, add JSDoc comments with: + +1. **Summary**: One-line description +2. **Description**: Detailed explanation +3. **Tags**: Group related endpoints +4. **Parameters**: Query parameters, path parameters +5. **Request Body**: Expected input schema +6. **Responses**: All possible response codes and schemas +7. **Examples**: Real usage examples + +### Custom Schemas + +Define reusable schemas in `src/lib/swagger.ts`: + +```typescript +components: { + schemas: { + YourCustomSchema: { + type: 'object', + properties: { + id: { type: 'string', description: 'Unique identifier' }, + name: { type: 'string', description: 'Display name' } + }, + required: ['id', 'name'] + } + } +} +``` + +### Examples with Multiple Scenarios + +```typescript +examples: + basic_usage: + summary: Basic usage + value: { param: "value" } + advanced_usage: + summary: Advanced with all options + value: { param: "value", advanced: true } +``` + +## 🎨 Customization + +### Styling +- Documentation UI automatically matches your app's theme +- Supports dark/light mode switching + +### Organization +- Use **tags** to group related endpoints +- Order endpoints by adding them to the `tags` array in swagger.ts + +### Authentication +- Add authentication schemas when needed +- Document API keys, bearer tokens, etc. + +## 📝 Next Steps + +1. **Document Remaining Routes**: Add JSDoc comments to all your API endpoints +2. **Add Examples**: Include realistic request/response examples +3. **Test Documentation**: Use the interactive UI to verify all endpoints work +4. **Export for External Use**: Generate OpenAPI spec for Postman/other tools + +## 🚨 Important Notes + +- The documentation page is at `/api/docs/page` (not just `/api/docs`) +- Swagger UI requires client-side rendering, so it's in the `page.tsx` file +- The generator automatically scans all files in `src/app/api/` for JSDoc comments +- Restart the dev server after adding new documentation to see changes + +Your API documentation will automatically stay in sync as you develop new features! 🎉 \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md index e18f627..b4609d9 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -36,4 +36,5 @@ # Audit - When asked if there are libraries to accomblish custom functionality, check npm -- When asked for alternatives give multiple options with pros and cons \ No newline at end of file +- When asked for alternatives give multiple options with pros and cons +- don't change any code unless I confirm \ No newline at end of file diff --git a/package-lock.json b/package-lock.json index 9f1bf2c..25b5f70 100644 --- a/package-lock.json +++ b/package-lock.json @@ -16,10 +16,13 @@ "exif-reader": "^2.0.2", "glob": "^11.0.3", "next": "^15.5.0", + "next-swagger-doc": "^0.4.1", "react": "^19.1.1", "react-dom": "^19.1.1", "react-share": "^5.2.2", - "sharp": "^0.34.3" + "redoc": "^2.5.0", + "sharp": "^0.34.3", + 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index e8bf3aa..48bd2a7 100644 --- a/src/app/api/classify/batch/route.ts +++ b/src/app/api/classify/batch/route.ts @@ -24,6 +24,76 @@ interface BatchClassifyRequest { } } +/** + * @swagger + * /api/classify/batch: + * post: + * summary: Batch classify photos using AI models + * description: Process multiple photos with AI classification using ViT for objects and optionally CLIP for artistic/style concepts. Supports comprehensive mode for maximum tag diversity. + * tags: [Classification] + * requestBody: + * required: true + * content: + * application/json: + * schema: + * $ref: '#/components/schemas/BatchClassifyRequest' + * examples: + * basic: + * summary: Basic batch classification + * value: + * limit: 10 + * minConfidence: 0.3 + * onlyUntagged: true + * comprehensive: + * summary: Comprehensive mode with dual models + * value: + * limit: 5 + * comprehensive: true + * minConfidence: 0.05 + * maxResults: 25 + * onlyUntagged: true + * responses: + * 200: + * description: Classification results with summary statistics + * content: + * application/json: + * schema: + * type: object + * properties: + * summary: + * type: object + * properties: + * processed: { type: 'integer', description: 'Number of photos processed' } + * successful: { type: 'integer', description: 'Number of successful classifications' } + * failed: { type: 'integer', description: 'Number of failed classifications' } + * totalTagsAdded: { type: 'integer', description: 'Total tags added to database' } + * config: { $ref: '#/components/schemas/ClassifierConfig' } + * results: + * type: array + * items: + * type: object + * properties: + * photoId: { type: 'string' } + * filename: { type: 'string' } + * tagsAdded: { type: 'integer' } + * topTags: + * type: array + * items: + * $ref: '#/components/schemas/ClassificationResult' + * hasMore: { type: 'boolean', description: 'Whether more photos are available to process' } + * 400: + * description: Invalid request parameters + * content: + * application/json: + * schema: + * $ref: '#/components/schemas/Error' + * 500: + * description: Server error during classification + * content: + * application/json: + * schema: + * $ref: '#/components/schemas/Error' + */ export async function POST(request: NextRequest) { try { const body: BatchClassifyRequest = await request.json() @@ -220,7 +290,47 @@ export async function POST(request: NextRequest) { } } -// Get classification status +/** + * @swagger + * /api/classify/batch: + * get: + * summary: Get classification status and statistics + * description: Returns statistics about photo classification status including total photos, tagged/untagged counts, and most common tags. + * tags: [Classification] + * parameters: + * - in: query + * name: directory + * schema: + * type: string + * description: Optional directory to filter statistics + * example: /Users/photos/2024 + * responses: + * 200: + * description: Classification status and statistics + * content: + * application/json: + * schema: + * type: object + * properties: + * total: { type: 'integer', description: 'Total number of photos' } + * tagged: { type: 'integer', description: 'Number of photos with tags' } + * untagged: { type: 'integer', description: 'Number of photos without tags' } + * taggedPercentage: { type: 'integer', description: 'Percentage of photos with tags' } + * topTags: + * type: array + * items: + * type: object + * properties: + * name: { type: 'string', description: 'Tag name' } + * count: { type: 'integer', description: 'Number of photos with this tag' } + * classifierReady: { type: 'boolean', description: 'Whether AI classifier is ready' } + * 500: + * description: Server error + * content: + * application/json: + * schema: + * $ref: '#/components/schemas/Error' + */ export async function GET(request: NextRequest) { try { const { searchParams } = new URL(request.url) diff --git a/src/app/api/docs/route.ts b/src/app/api/docs/route.ts new file mode 100644 index 0000000..6e96f87 --- /dev/null +++ b/src/app/api/docs/route.ts @@ -0,0 +1,15 @@ +import { NextResponse } from 'next/server' +import { getApiDocs } from '@/lib/swagger' + +export async function GET() { + try { + const spec = await getApiDocs() + return NextResponse.json(spec) + } catch (error) { + console.error('Error generating API docs:', error) + return NextResponse.json( + { error: 'Failed to generate API documentation' }, + { status: 500 } + ) + } +} \ No newline at end of file diff --git a/src/app/docs/page.tsx b/src/app/docs/page.tsx new file mode 100644 index 0000000..69e98ba --- /dev/null +++ b/src/app/docs/page.tsx @@ -0,0 +1,88 @@ +'use client' + +import { useState, useEffect } from 'react' +import dynamic from 'next/dynamic' + +// Dynamically import Swagger UI to avoid SSR issues +const SwaggerUI = dynamic(() => import('swagger-ui-react'), { + ssr: false, + loading: () =>
Loading Swagger UI...
+}) + +export default function ApiDocsPage() { + const [spec, setSpec] = useState(null) + const [error, setError] = useState(null) + + useEffect(() => { + fetch('/api/docs') + .then(res => { + if (!res.ok) { + throw new Error(`HTTP error! status: ${res.status}`) + } + return res.json() + }) + .then(setSpec) + .catch(error => { + console.error('Failed to load API docs:', error) + setError(error.message) + }) + }, []) + + if (error) { + return ( +
+
+
⚠️
+

+ Failed to Load API Documentation +

+

+ Error: {error} +

+ +
+
+ ) + } + + if (!spec) { + return ( +
+
+
+

Loading API Documentation...

+
+
+ ) + } + + return ( +
+
+
+

+ Photo Gallery AI API Documentation +

+

+ Complete API reference for the AI-powered photo organization system. + All endpoints run locally with no cloud dependencies. +

+
+ +
+ +
+
+
+ ) +} \ No newline at end of file diff --git a/src/lib/swagger.ts b/src/lib/swagger.ts new file mode 100644 index 0000000..b9a2343 --- /dev/null +++ b/src/lib/swagger.ts @@ -0,0 +1,143 @@ +import { createSwaggerSpec } from 'next-swagger-doc' + +export const getApiDocs = async () => { + const spec = createSwaggerSpec({ + apiFolder: 'src/app/api', + definition: { + openapi: '3.0.0', + info: { + title: 'Photo Gallery AI API', + version: '1.0.0', + description: ` +AI-powered photo organization and classification API. + +## Features +- **Dual-Model Classification**: ViT (objects) + CLIP (style/artistic concepts) +- **Image Captioning**: BLIP model for detailed descriptions +- **Batch Processing**: Process entire photo libraries +- **Tag Management**: Create, organize, and clear tags +- **Local AI**: All processing happens locally, no cloud dependencies + +## Authentication +All endpoints are currently open access for local usage. + +## Rate Limits +No rate limits - designed for local usage. + `, + contact: { + name: 'Photo Gallery API', + url: 'https://github.com/yourproject' + } + }, + servers: [ + { url: 'http://localhost:3000', description: 'Development server' }, + { url: 'https://yourdomain.com', description: 'Production server' } + ], + tags: [ + { + name: 'Photos', + description: 'Photo management operations' + }, + { + name: 'Classification', + description: 'AI-powered image classification and tagging' + }, + { + name: 'Captioning', + description: 'AI-powered image captioning and descriptions' + }, + { + name: 'Tags', + description: 'Tag management and organization' + }, + { + name: 'Configuration', + description: 'AI model configuration and settings' + } + ], + components: { + schemas: { + Photo: { + type: 'object', + properties: { + id: { type: 'string', description: 'Unique photo identifier' }, + filename: { type: 'string', description: 'Original filename' }, + filepath: { type: 'string', description: 'Full file path' }, + directory: { type: 'string', description: 'Parent directory' }, + filesize: { type: 'integer', description: 'File size in bytes' }, + width: { type: 'integer', description: 'Image width in pixels' }, + height: { type: 'integer', description: 'Image height in pixels' }, + format: { type: 'string', description: 'Image format (JPEG, PNG, etc.)' }, + favorite: { type: 'boolean', description: 'Is photo marked as favorite' }, + rating: { type: 'integer', minimum: 0, maximum: 5, description: 'Photo rating (0-5 stars)' }, + description: { type: 'string', description: 'AI-generated or user description' }, + created_at: { type: 'string', format: 'date-time', description: 'Photo creation date' }, + modified_at: { type: 'string', format: 'date-time', description: 'Last modification date' }, + indexed_at: { type: 'string', format: 'date-time', description: 'When photo was indexed' } + } + }, + Tag: { + type: 'object', + properties: { + id: { type: 'string', description: 'Unique tag identifier' }, + name: { type: 'string', description: 'Tag name' }, + color: { type: 'string', description: 'Tag color in hex format' }, + created_at: { type: 'string', format: 'date-time', description: 'Tag creation date' } + } + }, + ClassificationResult: { + type: 'object', + properties: { + label: { type: 'string', description: 'Classification label' }, + score: { type: 'number', minimum: 0, maximum: 1, description: 'Confidence score (0-1)' } + } + }, + CaptionResult: { + type: 'object', + properties: { + caption: { type: 'string', description: 'Generated caption text' }, + confidence: { type: 'number', minimum: 0, maximum: 1, description: 'Caption confidence score' } + } + }, + ClassifierConfig: { + type: 'object', + properties: { + minConfidence: { + type: 'number', + minimum: 0, + maximum: 1, + description: 'Minimum confidence threshold for classifications' + }, + maxResults: { + type: 'integer', + minimum: 1, + maximum: 50, + description: 'Maximum number of results to return' + } + } + }, + BatchClassifyRequest: { + type: 'object', + properties: { + directory: { type: 'string', description: 'Directory to process (optional)' }, + limit: { type: 'integer', minimum: 1, maximum: 100, description: 'Number of photos to process per batch' }, + offset: { type: 'integer', minimum: 0, description: 'Offset for pagination' }, + minConfidence: { type: 'number', minimum: 0, maximum: 1, description: 'Minimum confidence threshold' }, + maxResults: { type: 'integer', minimum: 1, maximum: 50, description: 'Maximum results per photo' }, + onlyUntagged: { type: 'boolean', description: 'Process only photos without existing tags' }, + comprehensive: { type: 'boolean', description: 'Use both ViT + CLIP models for more diverse tags' }, + dryRun: { type: 'boolean', description: 'Preview results without saving to database' } + } + }, + Error: { + type: 'object', + properties: { + error: { type: 'string', description: 'Error message' } + } + } + } + } + } + }) + return spec +} \ No newline at end of file