documentation.py

DOC_CONTENT = """
# 🖼️ Image Labeling Tool - User Guide

## 📋 Overview

This tool helps you create labeled image datasets quickly and efficiently using AI-powered automatic labeling. Perfect for machine learning projects, computer vision tasks, and dataset preparation.

---

## 🚀 Getting Started

### Step 1: Upload Images
- Click **📁 Upload images** button
- Select multiple image files from your computer
- Supported formats: JPG, PNG, GIF, and other common image formats
- Images will appear in a grid layout

### Step 2: Generate Labels
You have two options for labeling:

#### Option A: Individual Labeling
- Click **✨ Generate label** below any image
- AI will analyze the image and create a detailed description
- Edit the generated text if needed

#### Option B: Batch Labeling
- Click **🏷️ Labelize all images** button
- AI will process all images automatically
- Progress bar shows labeling status
- All images get labeled simultaneously

### Step 3: Review and Edit
- Labels appear as text boxes below each image
- Click on any text box to edit the description
- Make changes as needed for your specific use case

### Step 4: Download Dataset
- Configure download options in the **📦 Download Options** section
- Choose folder organization preference:
  - ✅ **Organized**: Images in `images/` folder, labels in `labels/` folder
  - ❌ **Flat**: All files in root directory
- Click **💾 Download dataset** to get your labeled dataset

---

## 🎯 Features

### 🤖 AI-Powered Labeling
- Uses advanced Florence-2 model for accurate image descriptions
- Generates detailed, contextual descriptions
- Supports multiple description styles

### 📊 Dataset Management
- Add/remove images easily
- Edit labels manually
- Real-time progress tracking
- Efficient batch processing

### 📦 Flexible Export Options
- **Organized mode**: Perfect for ML frameworks expecting separate folders
- **Flat mode**: Ideal for simple file organization
- Automatic text file generation with matching names

### 🎨 User-Friendly Interface
- Clean, intuitive design
- Visual progress indicators
- Responsive layout
- Emoji-enhanced navigation

---

## 💡 Tips & Best Practices

### For Better Labels
- Use high-quality, clear images
- Ensure good lighting and focus
- Avoid blurry or low-resolution images
- Consider image diversity for training datasets

### For Efficient Workflow
- Start with a small batch to test label quality
- Use batch processing for large datasets
- Review and edit labels for consistency
- Download frequently to save progress

### For Dataset Quality
- Ensure consistent labeling style
- Add specific details relevant to your use case
- Remove irrelevant or poor-quality images
- Test your dataset with your target application

---

## 🔧 Technical Details

### Supported Image Formats
- JPEG (.jpg, .jpeg)
- PNG (.png)
- GIF (.gif)
- BMP (.bmp)
- TIFF (.tiff, .tif)
- WebP (.webp)

### Label Format
- Plain text files (.txt)
- UTF-8 encoding
- Same basename as corresponding image
- Example: `photo1.jpg` → `photo1.txt`

### File Organization

#### Organized Mode
```
dataset.zip
├── images/
│   ├── photo1.jpg
│   ├── photo2.png
│   └── ...
└── labels/
    ├── photo1.txt
    ├── photo2.txt
    └── ...
```

#### Flat Mode
```
dataset.zip
├── photo1.jpg
├── photo1.txt
├── photo2.png
├── photo2.txt
└── ...
```

---

## 🎯 Use Cases

### Machine Learning
- **Image Classification**: Create labeled datasets for training classifiers
- **Object Detection**: Generate descriptions for object detection tasks
- **Image Retrieval**: Build searchable image databases
- **Data Augmentation**: Create consistent label sets for augmented data

### Content Management
- **Photo Archives**: Organize personal or professional image collections
- **E-commerce**: Generate product descriptions automatically
- **Social Media**: Create alt-text and captions for images
- **Digital Asset Management**: Tag and organize visual content

### Research & Education
- **Academic Projects**: Prepare datasets for computer vision research
- **Teaching Materials**: Create labeled examples for students
- **Accessibility**: Generate descriptions for visually impaired users
- **Documentation**: Auto-generate figure descriptions

---

## ⚠️ Important Notes

### Performance
- Processing time depends on image count and size
- Batch processing is more efficient than individual labeling
- Large datasets may take several minutes to process

### Privacy
- Images are processed locally on your machine
- No data is sent to external servers during processing
- Downloaded datasets contain only your images and labels

### Limitations
- Very large images (>10MB) may take longer to process
- Complex images with multiple objects may need manual refinement
- AI accuracy varies with image quality and complexity

---

## 🆘 Troubleshooting

### Common Issues

**Images not uploading?**
- Check file format compatibility
- Ensure files aren't corrupted
- Try smaller batches first

**Labels seem inaccurate?**
- Improve image quality and lighting
- Edit labels manually after generation
- Use consistent image style for better results

**Download not working?**
- Ensure you have labeled images first
- Check available disk space
- Try both folder organization options

**Performance slow?**
- Close other applications
- Use smaller image batches
- Consider image size optimization

### Getting Help
- Check image formats and sizes
- Ensure stable internet connection for model loading
- Restart the application if issues persist

---

## 🎉 Ready to Start!

1. **Upload** your images using the 📁 button
2. **Generate** labels individually or in batch
3. **Review** and edit as needed
4. **Download** your labeled dataset

Happy labeling! 🚀
"""