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comment-moderation-api
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comment-moderation-api
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# Comment Moderation API A lightweight, developer‑friendly REST service for real‑time toxicity scoring, built with FastAPI. You can deploy it as‑is, fine‑tune your own moderation model, and plug it into Discord, Telegram, or in‑game chat. ## Highlights - `POST /analyze` → returns `toxicity` (0–1) and `confidence` for one or many messages. - `POST /train` → fine‑tune a simple TF‑IDF + Linear model on your labeled data. - Optional Redis caching and IP‑based rate limiting. - Drop‑in integration examples for **Discord**, **Telegram**, and **game chats**. - Dockerfile + GitHub Actions to build and test. --- ## Quick Start ### 1) Setup (Python 3.10+) ```bash pip3 install -r requirements.txt uvicorn app.main:app --reload --port 8000 ``` Open http://127.0.0.1:8000/docs for the interactive API. ### 2) Analyze Text ```bash curl -X POST http://127.0.0.1:8000/analyze -H "Content-Type: application/json" -d '{"texts": ["I hate you", "Have a great day!"]}' ``` Response: ```json { "results": [ {"text":"I hate you","toxicity":0.93,"confidence":0.93,"label":"toxic"}, {"text":"Have a great day!","toxicity":0.03,"confidence":0.97,"label":"neutral"} ] } ``` > Out of the box, the service falls back to a **heuristic baseline** (regex + token stats). > Train a small supervised model via `/train` for better quality on your domain. ### 3) Optional: Redis Cache + Rate Limiting Create an `.env` file (or export env vars): ```bash REDIS_URL=redis://localhost:6379/0 RATE_LIMIT=60/minute # or 100/hour etc. CACHE_TTL_SECONDS=600 ``` Run a local Redis (example via Docker): ```bash docker run -p 6379:6379 redis:7 ``` ### 4) Train Your Model Send labeled data to `/train`. Labels are strings: `"toxic"` or `"neutral"` (you can extend this). ```bash curl -X POST http://127.0.0.1:8000/train -H "Content-Type: application/json" -d '{ "samples": [ {"text":"you are stupid","label":"toxic"}, {"text":"let’s play tonight","label":"neutral"}, {"text":"go to hell","label":"toxic"}, {"text":"thanks for the help","label":"neutral"} ] }' ``` Response includes basic metrics and saves a model to `models/model.joblib`: ```json { "status": "ok", "trained_on": 4, "metrics": {"accuracy": 0.75, "f1_macro": 0.73} } ``` Restart is **not** needed—models hot‑reload automatically when saved. --- ## Endpoints ### `POST /analyze` Request: ```json { "texts": ["string", "..."] } ``` Response: ```json { "results": [ {"text":"...","toxicity":0.42,"confidence":0.84,"label":"neutral"} ] } ``` ### `POST /train` Request: ```json { "samples": [ {"text":"...", "label":"toxic|neutral"} ] } ``` Response: ```json { "status":"ok", "trained_on": N, "metrics": { "accuracy": ..., "f1_macro": ... } } ``` --- ## Integrations - **Discord:** `examples/discord_bot.py` - **Telegram:** `examples/telegram_bot.py` - **Game Chat / generic:** `examples/game_chat.py` Each example reads `API_BASE` from env (default `http://127.0.0.1:8000`). --- ## Docker ```bash docker build -t comment-moderation-api . docker run -p 8000:8000 --env-file .env comment-moderation-api ``` --- ## Project Layout ``` app/ main.py # FastAPI app + middleware + routing models.py # Pydantic schemas deps.py # Redis / rate limit utilities router_analyze.py # /analyze endpoint router_train.py # /train endpoint ml/ pipeline.py # ModelManager: load/save/predict; heuristic fallback examples/ discord_bot.py telegram_bot.py game_chat.py models/ # persisted models (gitignored) tests/ test_analyze.py ``` --- ## Notes - This project intentionally keeps training simple (TF‑IDF + Linear model). You can swap in your favorite deep model—just expose a `predict_proba(texts)` in `ModelManager`. - Safety & fairness: always test models on your real data slices and consider human‑in‑the‑loop review for edge cases.
