Moderation API

A FastAPI-based content moderation service using BERT for text classification. This service provides an API endpoint that can analyze text content for various types of harmful content, similar to OpenAI's moderation API.

Features

• Text content moderation using BERT model
• Multiple category detection including:
  • Harassment
  • Hate speech
  • Self-harm
  • Sexual content
  • Violence
• FastAPI-based REST API
• Docker containerization with both CPU and GPU support
• GitHub Actions CI/CD pipeline

Model Information

This service uses the ifmain/ModerationBERT-En-02 model, which is a fine-tuned BERT model specifically designed for content moderation. The model can detect and classify content across 11 different categories:

• Harassment
• Harassment/Threatening
• Hate
• Hate/Threatening
• Self-harm
• Self-harm/Instructions
• Self-harm/Intent
• Sexual
• Sexual/Minors
• Violence
• Violence/Graphic

The model outputs both binary classifications (flagged/not flagged) and confidence scores for each category, allowing for fine-grained content analysis.

Quick Start with Docker Compose

CPU only

version: '3.8'

services:
  moderation-api:
    image: ghcr.io/haouarihk/moderationapi:main-cpu
    ports:
      - 8000:8000
    restart: unless-stopped

GPU Support

version: '3.8'

services:
  moderation-api:
    image: ghcr.io/haouarihk/moderationapi:main
    ports:
      - 8000:8000
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    restart: unless-stopped

The API will be available at http://localhost:8000

API Endpoint

POST /v1/moderations

Analyzes text content for harmful content.

Request Body

{
  "input": [
    "Your text to analyze here",
    "Multiple texts can be analyzed at once"
  ]
}

Response

{
  "id": "modr-xxxxxx",
  "model": "ModerationBERT-En-02",
  "results": [
    {
      "flagged": true,
      "categories": {
        "sexual": false,
        "hate": true,
        "harassment": false,
        "self_harm": false,
        "sexual_minors": false,
        "hate_threatening": false,
        "violence_graphic": false,
        "self_harm_intent": false,
        "self_harm_instructions": false,
        "harassment_threatening": false,
        "violence": false
      },
      "category_scores": {
        "sexual": 0.123,
        "hate": 0.789,
        "harassment": 0.234,
        "self_harm": 0.045,
        "sexual_minors": 0.012,
        "hate_threatening": 0.345,
        "violence_graphic": 0.067,
        "self_harm_intent": 0.089,
        "self_harm_instructions": 0.023,
        "harassment_threatening": 0.156,
        "violence": 0.234
      }
    }
  ]
}

Alternative Installation Methods

Docker

If you prefer using Docker directly:

# Pull the CPU image
docker pull ghcr.io/haouarihk/moderationapi:latest-cpu

# Run the container
docker run -p 8000:8000 ghcr.io/haouarihk/moderationapi:latest-cpu

Local Development

For local development, you'll need:

• Python 3.8+
• CUDA-capable GPU (recommended)
• Docker (optional)

1. Clone the repository:

git clone https://github.com/haouarihk/moderationAPI.git
cd moderationAPI

2. Install dependencies:

pip install -r requirements.txt

3. Run the API:

uvicorn app:app --host 0.0.0.0 --port 8000

Environment Variables

No environment variables are required for basic operation.

CI/CD

This project uses separate GitHub Actions workflows for CPU and GPU images:

1. GPU Image Workflow (.github/workflows/docker-build-gpu.yml):

   • Builds and pushes the GPU-optimized image
   • Tagged as latest
   • Uses CUDA base image

2. CPU Image Workflow (.github/workflows/docker-build-cpu.yml):

   • Builds and pushes the CPU-optimized image
   • Tagged as latest-cpu
   • Uses slim base image

Both workflows:

• Trigger on pushes to the main branch
• Push to GitHub Container Registry (GHCR)
• Include commit SHA tags for specific versions

License

[Add your license here]

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request