Spaces:
Paused
Paused
File size: 2,312 Bytes
88bdcff 706520f 88bdcff 8771f89 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
title: FDAM AI Pipeline
emoji: "\U0001F525"
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: "6.3.0"
app_file: app.py
pinned: false
suggested_hardware: l4x4
---
# FDAM AI Pipeline
**Fire Damage Assessment Methodology v4.0.1** - An AI-powered system that generates professional Cleaning Specifications / Scope of Work documents for fire damage restoration.
## Features
- **AI-Powered Image Analysis**: Uses Qwen3-VL vision model to detect fire damage zones, conditions, and materials
- **FDAM Compliant**: Implements Fire Damage Assessment Methodology v4.0.1 standards
- **Automated Calculations**: Air filtration, sample density, labor estimates per FDAM formulas
- **Professional PDF Output**: Generates ready-to-use Scope of Work documents
- **Session Persistence**: Save and resume assessments via browser localStorage
## How to Use
1. **Project Info**: Enter project details, facility classification, and assessor information
2. **Building/Rooms**: Add rooms with dimensions (length, width, ceiling height)
3. **Images**: Upload fire damage photos and associate with rooms
4. **Observations**: Record qualitative observations (odor, soot, char, etc.)
5. **Generate**: Click "Generate Assessment" to run AI analysis and produce documents
## Technical Details
### Model Stack (~38-43GB VRAM)
- **Vision**: Qwen3-VL-30B-A3B-Thinking-FP8 (~30-35GB) - Reasoning-enhanced analysis with structured JSON output
- **Embeddings**: Qwen3-VL-Embedding-2B (~4GB)
- **Reranker**: Qwen3-VL-Reranker-2B (~4GB)
### Zone Classifications
- **Burn Zone**: Direct fire involvement, structural damage
- **Near-Field**: Adjacent to burn zone, heavy smoke/heat exposure
- **Far-Field**: Smoke migration only, light deposits
### Condition Levels
- **Background**: No visible contamination
- **Light**: Faint discoloration, minimal deposits
- **Moderate**: Visible film/deposits
- **Heavy**: Thick deposits, surface texture obscured
- **Structural Damage**: Physical damage requiring repair
## Development
```bash
# Local development (mock models)
MOCK_MODELS=true python app.py
# Run tests
pytest tests/ -v
```
## Requirements
- Python 3.10+
- 96GB GPU memory for real model inference (4x L4 or equivalent)
- See `requirements.txt` for full dependencies
## License
Proprietary - For authorized use only.
|