README.md

metadata

title: PolicyPulse AI Sandbox
emoji: 🛡️
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false

PolicyPulse AI | Content Moderation Sandbox

A high-fidelity OpenEnv for benchmarking automated moderation policies with fairness constraints. Developed for the Meta-PyTorch Hackathon.

👨‍⚖️ Evaluation Guide for Hackathon Judges

This project features a dual-use architecture to satisfy strict automated baseline graders while giving human judges rich visual capabilities.

1. Automated Baseline Testing (Strict Compliance)

The environment complies strictly with the OpenEnv criteria. You can run the autonomous scripts without modification. The inference.py script automatically uses secure environment variables:

export API_BASE_URL="https://api.openai.com/v1"
export MODEL_NAME="gpt-4o-mini"
export HF_TOKEN="your-api-key"
python inference.py clear_cut_moderation 42

It outputs the strict [START], [STEP], and [END] logging required for baseline reproducibility.

2. Interactive Sandbox & APIs (Dynamic Overrides)

If you want to manually test the API with your API keys without fighting server-level configs, our FastAPI deployment allows live parameter injection:

• Web UI: Navigate to the Space URL root to access the interactive Operation Center. You can enter your custom Base URL and API Key directly into the UI to push moderation tasks to your preferred LLM instantly.
• REST API: Open the /docs route. The payload for both /evaluate and /predict_and_step accepts optional overrides (api_base_url, model_name, api_key). If left blank, they gracefully fall back to the Hugging Face Space environment settings.

🚀 Deployment Instructions

This project is designed to run on Hugging Face Spaces using Docker.

Local Development

pip install -r requirements.txt
python3 app.py

Grader Compliance

The environment uses SocialStreamModerationEnv and is compliant with Task 1, 2, and 3 requirements.

License

MIT