haroonwaheed19/skinsightsystem

SkinSight AI Service 🧴

Neural Inference Engine for Dermatological Classification

 

This microservice provides high-performance skin disease analysis using a hybrid deep learning architecture. It is built with FastAPI for low-latency inference and PyTorch for neural computation.

🏛️ Model Architecture

SkinSight employs a Hybrid Fusion Stack:

1. 1.EfficientNet-B3: Captures high-resolution spatial features and textures from skin scans.
2. 2.Swin Transformer (Tiny): Utilizes shift-window attention to capture global context and long-range dependencies across the lesion area.
3. 3.Adaptive Fusion Head: A custom neural head that dynamically weights features from both backbones based on the input complexity.

🚀 API Endpoints

POST /analyze

Performs real-time neural inference on a skin image.

• —Request Body:

  {
    "image_url": "https://...",
    "image_base64": "data:image/jpeg;base64,..."
  }

• —Response:

  {
    "status": "success",
    "top1": { "disease": "Melanoma", "confidence": 0.92, "urgency": "HIGH" },
    "top5": [...]
  }

POST /report

Generates a Clinical PDF Report.

• —Request Body: ReportRequest (Includes patient name, email, and diagnostic findings).
• —Response: Binary PDF data with Content-Disposition: attachment.

GET /health

Returns system status, engine version, and uptime metrics.

🛠️ Performance & Security

• —Standalone Reliability: Prioritizes local branding and research assets, ensuring full functionality in isolated Docker or Hugging Face environments.
• —Scientific Reporting: Clinical results are enriched with 10+ localized research figures (ai-service/research).
• —Asynchronous Inference: Uses Starlette's run_in_threadpool to prevent blocking the async loop.
• —Production Metadata: Includes optimized .dockerignore and .gitattributes (LFS) for tracking.
• —Asset Optimization: Integrated optimize_branding.py workflow for production visuals.
• —Secure Model Loading: Implements weights_only=True for internal .pth files.

📦 Deployment (Hugging Face)

This service is optimized for Hugging Face Spaces using Docker:

1. 1.Dockerized Runtime: Multi-stage build with optimized ignore rules for minimal image size.
2. 2.GPU Optional: Optimized for both CUDA and fast CPU-based medical inference.
3. 3.Asset Portability: Maintains local clinical branding and research figures for standalone report generation.
4. 4.Git LFS: Tracks large neural weights (.pth) and high-resolution clinical figures for efficient repository syncing.

🏗️ Development

python -m venv fyp
./fyp/Scripts/activate
pip install -r requirements.txt
python main.py