modbender Figure Legend Generator
Generate publication-quality figure legends for scientific research charts and images.
Supported Chart Types
| Chart Type | Description |
|---|---|
| Bar Chart | Compare values across categories |
| Line Graph | Show trends over time or continuous data |
| Scatter Plot | Display relationships between variables |
| Box Plot | Show distribution and outliers |
| Heatmap | Display matrix data intensity |
| Microscopy | Fluorescence/confocal images |
| Flow Cytometry | FACS plots and histograms |
| Western Blot | Protein expression bands |
Usage
python scripts/main.py --input <image_path> --type <chart_type> [--output <output_path>]
Parameters
| Parameter | Required | Description |
|---|---|---|
--input |
Yes | Path to chart image |
--type |
Yes | Chart type (bar/line/scatter/box/heatmap/microscopy/flow/western) |
--output |
No | Output path for legend text (default: stdout) |
--format |
No | Output format (text/markdown/latex), default: markdown |
--language |
No | Language (en/zh), default: en |
Examples
# Generate legend for bar chart
python scripts/main.py --input figure1.png --type bar
# Save to file
python scripts/main.py --input plot.jpg --type line --output legend.md
# Chinese output
python scripts/main.py --image.png --type scatter --language zh
Legend Structure
Generated legends follow academic standards:
- Figure Number - Sequential numbering
- Brief Title - Concise description
- Main Description - What the figure shows
- Data Details - Key statistics/measurements
- Methodology - Brief experimental context
- Statistics - P-values, significance markers
- Scale Bars - For microscopy images
Technical Notes
- Difficulty: Low
- Dependencies: PIL, pytesseract (optional OCR)
- Processing: Vision analysis for chart type detection
- Output: Structured markdown by default
References
references/legend_templates.md- Templates by chart typereferences/academic_style_guide.md- Formatting guidelines
Risk Assessment
| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python scripts with tools | High |
| Network Access | External API calls | High |
| File System Access | Read/write data | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Data handled securely | Medium |
Security Checklist
- No hardcoded credentials or API keys
- No unauthorized file system access (../)
- Output does not expose sensitive information
- Prompt injection protections in place
- API requests use HTTPS only
- Input validated against allowed patterns
- API timeout and retry mechanisms implemented
- Output directory restricted to workspace
- Script execution in sandboxed environment
- Error messages sanitized (no internal paths exposed)
- Dependencies audited
- No exposure of internal service architecture
Prerequisites
# Python dependencies
pip install -r requirements.txt
Evaluation Criteria
Success Metrics
- Successfully executes main functionality
- Output meets quality standards
- Handles edge cases gracefully
- Performance is acceptable
Test Cases
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- Performance: Large dataset → Acceptable processing time
Lifecycle Status
- Current Stage: Draft
- Next Review Date: 2026-03-06
- Known Issues: None
- Planned Improvements:
- Performance optimization
- Additional feature support