┌─────────────────────────────────────────────────────────────────┐
│                    Dental AI Treatment Planner                   │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐       │
│  │   YOLOv11    │───▶│  MCP Server  │◀───│  RAG Engine  │       │
│  │  Detection   │    │  (FastMCP)   │    │ (LangChain)  │       │
│  └──────────────┘    └──────────────┘    └──────────────┘       │
│         │                   │                   │                │
│         ▼                   ▼                   ▼                │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐       │
│  │ Dental X-ray │    │   Claude/    │    │  Clinical    │       │
│  │   Analysis   │    │   ChatGPT    │    │  Guidelines  │       │
│  └──────────────┘    └──────────────┘    └──────────────┘       │
│                                                                  │
│  OUTPUT: Diagnosis + Treatment Plan + Cost Estimate              │
└─────────────────────────────────────────────────────────────────┘

# Clone the repository
git clone https://github.com/Kannaseka/Dental-AI-Treament-Planner.git
cd dental-ai-treatment-planner

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

# Or using uv (faster)
uv pip install -e .

pip install -e ".[dev]"

# Analyze an X-ray
dental-ai analyze --image path/to/xray.jpg

# Get treatment plan
dental-ai treatment --condition "Deep Caries" --severity "severe"

# Start MCP server
dental-mcp --transport stdio

from src.vision.dental_analyzer import DentalVisionAnalyzer
from src.rag.dental_rag import DentalGuidelinesRAG

# Initialize analyzer
analyzer = DentalVisionAnalyzer()

# Analyze X-ray
result = analyzer.analyze("dental_xray.jpg")
print(f"Risk Score: {result.risk_score}")
print(f"Detections: {len(result.detections)}")

# Get treatment plan
rag = DentalGuidelinesRAG()
plan = rag.generate_treatment_plan("Caries", "moderate")
print(f"Treatment: {plan.primary_treatment.name}")
print(f"Cost: {plan.primary_treatment.estimated_cost_range}")

# Start the API server
uvicorn src.api.main:app --host 0.0.0.0 --port 8000

# Or with auto-reload for development
uvicorn src.api.main:app --reload

# Analyze X-ray
curl -X POST "http://localhost:8000/api/v1/analyze" \
  -F "file=@dental_xray.jpg"

# Get treatment plan
curl -X POST "http://localhost:8000/api/v1/treatment-plan" \
  -H "Content-Type: application/json" \
  -d '{"condition": "Caries", "severity": "moderate"}'

# Get cost estimate
curl -X POST "http://localhost:8000/api/v1/cost-estimate" \
  -H "Content-Type: application/json" \
  -d '{"treatments": ["Root Canal - Molar", "Crown - Zirconia"]}'

# Start interactive demo
python src/api/demo.py

# Opens at http://localhost:7860

{
  "mcpServers": {
    "dental-ai": {
      "command": "python",
      "args": ["-m", "src.mcp_server.server"],
      "cwd": "/path/to/dental-ai-treatment-planner"
    }
  }
}

# Add MCP server
claude mcp add dental-ai -- python -m src.mcp_server.server

User: Analyze this dental X-ray and create a treatment plan

Claude: [Uses dental_analyze_xray tool]
I've analyzed the X-ray. Here are my findings:

**Risk Score: 65/100**

**Detected Conditions:**
1. Deep Caries (Tooth #36)
   - Severity: Severe
   - Confidence: 78%

**Recommended Treatment:**
Stepwise Caries Excavation
- Estimated Cost: AED 800 - 1,800
- Success Rate: 85%

**Recommendations:**
- Schedule treatment within 1-2 weeks
- Consider root canal if pulp exposure occurs

dental-ai-treatment-planner/
├── src/
│   ├── vision/
│   │   └── dental_analyzer.py    # YOLOv11 X-ray analysis
│   ├── rag/
│   │   └── dental_rag.py         # Clinical guidelines RAG
│   ├── mcp_server/
│   │   └── server.py             # MCP server implementation
│   └── api/
│       ├── main.py               # FastAPI REST API
│       └── demo.py               # Gradio demo interface
├── data/
│   ├── models/                   # YOLO model weights
│   ├── guidelines/               # Clinical guidelines PDFs
│   └── sample_xrays/            # Sample X-ray images
├── tests/
├── docs/
├── pyproject.toml
└── README.md

# Run all tests
pytest

# Run with coverage
pytest --cov=src --cov-report=html

# Run specific test
pytest tests/test_vision.py -v

# Model configuration
export DENTAL_MODEL_PATH=/path/to/dental_yolo.pt
export CONFIDENCE_THRESHOLD=0.25

# API configuration
export API_HOST=0.0.0.0
export API_PORT=8000

# RAG configuration
export EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2