README.md

metadata

title: SENTINEL_AI
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
short_description: FastAPI AI image detector with batch scan support

SENTINEL_AI

SENTINEL_AI is a FastAPI-powered AI image detector with a dark cyberpunk HTML frontend.

The app supports:

• single-image prediction
• batch prediction
• Default Scan
• AI-Sensitive

Hugging Face Spaces deployment

This repository is prepared for a Hugging Face Docker Space.

Included deployment files:

• Dockerfile
• .dockerignore
• this README.md with Space metadata
• requirements.txt for a lighter inference-only build
• requirements-full.txt if you want the older training and Streamlit tooling locally

Hugging Face will build the Docker image and run the app on port 7860.

App routes

• GET / serves the frontend from static/index.html
• GET /health returns a simple health check
• POST /predict accepts one uploaded image
• POST /predict/batch accepts multiple uploaded images

The app loads the trained model at startup from:

• artifacts/ai_image_detector.keras

Local development

Python

python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --host 127.0.0.1 --port 8000

If you want the older training and Streamlit dependencies too:

pip install -r requirements-full.txt

Docker

docker build -t sentinel-ai .
docker run --rm -p 7860:7860 sentinel-ai

Then open:

• http://127.0.0.1:7860

Project structure

• main.py: FastAPI entrypoint
• static/index.html: frontend UI
• src/ai_image_detector/: shared inference code
• artifacts/: trained model and related artifacts
• predict.py: terminal prediction helper
• train.py: model training script

Notes

• The app requires a trained model artifact before deployment.
• data/ is not needed for inference-only deployment, so it is excluded from the Docker build context.
• If you retrain the model, make sure the updated artifacts/ai_image_detector.keras is included before pushing to the Space.
• The model file artifacts/ai_image_detector.keras is not excluded by .dockerignore, so it will be copied into the Docker image.

Publish to Hugging Face Spaces

1. Create a new Hugging Face Space.
2. Choose Docker as the SDK.
3. Push this repository to the Space repository.
4. Wait for the Docker build to finish.
5. Open the Space URL once the build is green.

Example git flow:

git init
git lfs install
git add .gitattributes
git add .
git commit -m "Prepare Hugging Face Docker Space"
git branch -M main
git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
git push -u origin main

If you already have a Space repository, you can also clone that repo first and copy this project into it before committing and pushing.

After deploy, these routes should be available:

• /
• /health
• /predict
• /predict/batch