# DAG Visualization for QA_LLM_Module

## Overview

This enhancement adds DAG (Directed Acyclic Graph) visualization capability to the QA_LLM_Module, allowing users to see visual representations of robot task dependencies generated by the LLM.

## Features

- **Automatic DAG Generation**: Visualizations are generated automatically when the LLM outputs valid task JSON
- **IEEE-Style Formatting**: Professional, publication-ready graphs
- **Hierarchical Layout**: Tasks are organized by dependency levels
- **Color-Coded Nodes**:
  - 🔴 Red: Start tasks (no dependencies)
  - 🟣 Purple: End tasks (no dependents)
  - 🟠 Orange: Intermediate tasks
- **Detailed Information**: Each task shows function name, assigned robots, and object keywords
- **Two Visualization Modes**: Detailed and simplified versions
- **🔒 Safety Confirmation Workflow**: 
  - Task plans require operator approval before deployment
  - "Validate & Deploy Task Plan" button for safety compliance
  - Visual status updates (Pending → Approved & Deployed)
  - Prevents accidental execution of unverified plans

## Installation

The required dependencies are already added to `requirements.txt`:

```bash
pip install matplotlib==3.9.4
pip install networkx==3.4
```

## Usage

### Automatic Integration with Safety Workflow

When you run the main application and input a query that generates task JSON:

1. **DAG Generation**: The visualization appears automatically with "Pending Approval" status
2. **Safety Review**: Operator reviews the task dependency graph
3. **Confirmation**: Click "🔒 Validate & Deploy Task Plan" to send to construction site
4. **Deployment**: Graph updates to "APPROVED & DEPLOYED" status

```bash
python3 main.py
```

### Enhanced Safety Workflow Steps

1. 🤖 **LLM generates task plan** → DAG shows "Pending Approval"
2. 👨‍💼 **Operator reviews visualization** → Confirms task safety and correctness
3. 📝 **[Optional] Edit Task Plan** → Manual JSON editing if modifications needed
4. 🔄 **[Optional] Update DAG Visualization** → Regenerate graph with edits
5. 🔒 **Validate & Deploy Task Plan** → Final approval and deployment to robots
6. ✅ **Confirmation displayed** → DAG shows "APPROVED & DEPLOYED"

### Three-Button IEEE-Style Workflow

- **📝 Edit Task Plan**: Opens JSON editor for manual modifications
- **🔄 Update DAG Visualization**: Regenerates graph from edited JSON
- **🔒 Validate & Deploy Task Plan**: Final safety approval and deployment

### Manual Testing

You can test the DAG visualization independently:

```bash
python3 test_dag_visualization.py
python3 dag_demo.py
```

## API Reference

### DAGVisualizer Class

```python
from dag_visualizer import DAGVisualizer

visualizer = DAGVisualizer()
```

#### Methods

##### `create_dag_visualization(task_data, title="Robot Task Dependency Graph")`
Creates a detailed DAG visualization with full task information.

**Parameters:**
- `task_data` (dict): Task data in the expected JSON format
- `title` (str): Title for the graph

**Returns:**
- `str`: Path to the generated PNG image, or `None` if failed

##### `create_simplified_dag_visualization(task_data, title="Robot Task Graph")`
Creates a simplified DAG visualization suitable for smaller displays.

**Parameters:**
- `task_data` (dict): Task data in the expected JSON format
- `title` (str): Title for the graph

**Returns:**
- `str`: Path to the generated PNG image, or `None` if failed

## Expected JSON Format

The visualizer expects task data in the following format:

```json
{
  "tasks": [
    {
      "task": "task_name",
      "instruction_function": {
        "name": "function_name",
        "robot_ids": ["robot_id_1", "robot_id_2"],
        "dependencies": ["dependency_task_name"],
        "object_keywords": ["object1", "object2"]
      }
    }
  ]
}
```

## Examples

### Single Task
```json
{
  "tasks": [
    {
      "task": "target_area_for_specific_robots_1",
      "instruction_function": {
        "name": "target_area_for_specific_robots",
        "robot_ids": ["robot_dump_truck_01"],
        "dependencies": [],
        "object_keywords": ["puddle1"]
      }
    }
  ]
}
```

### Multiple Tasks with Dependencies
```json
{
  "tasks": [
    {
      "task": "excavate_soil",
      "instruction_function": {
        "name": "excavate_soil_from_pile",
        "robot_ids": ["robot_excavator_01"],
        "dependencies": [],
        "object_keywords": ["soil_pile"]
      }
    },
    {
      "task": "transport_soil",
      "instruction_function": {
        "name": "transport_soil_to_pit",
        "robot_ids": ["robot_dump_truck_01"],
        "dependencies": ["excavate_soil"],
        "object_keywords": ["pit"]
      }
    }
  ]
}
```

## File Structure

```
QA_LLM_Module/
├── dag_visualizer.py          # Main visualization module
├── test_dag_visualization.py  # Test suite
├── dag_demo.py               # Demo script
├── gradio_llm_interface.py   # Updated with DAG integration
├── main.py                   # Updated Gradio interface
└── requirements.txt          # Updated dependencies
```

## Integration Details

### Modified Files

1. **gradio_llm_interface.py**:
   - Added `DAGVisualizer` import
   - Modified `predict()` method to generate visualizations
   - Returns additional DAG image output

2. **main.py**:
   - Added `gr.Image` component for DAG display
   - Updated input/output mappings for DAG visualization

3. **requirements.txt**:
   - Added `matplotlib==3.9.4`
   - Added `networkx==3.4`

## Troubleshooting

### Common Issues

1. **"No module named 'matplotlib'"**
   - Solution: `pip install matplotlib networkx`

2. **"No tasks found or invalid graph structure"**
   - Solution: Ensure your JSON follows the expected format

3. **Images not displaying in Gradio**
   - Solution: Check that the image paths are accessible and files exist

### Debug Mode

Enable detailed logging by setting the log level:

```python
from loguru import logger
logger.add("dag_debug.log", level="DEBUG")
```

## Performance Notes

- Visualizations are generated in temporary directories
- Images are automatically cleaned up by the system
- Large graphs (>20 nodes) may take longer to render
- Memory usage scales with graph complexity

## Future Enhancements

Potential improvements for future versions:

1. **Interactive Graphs**: Clickable nodes with detailed information
2. **Real-time Updates**: Live visualization updates as tasks execute
3. **Export Options**: Support for PDF, SVG, and other formats
4. **Custom Styling**: User-configurable colors and layouts
5. **Animation**: Animated execution flow visualization