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| title: Sentinel API | |
| emoji: 🛡️ | |
| colorFrom: blue | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: "4.44.1" | |
| app_file: run.py | |
| pinned: false | |
| <div align="center"> | |
| # 🛡️ Sentinel | |
| ### Real-Time AI Threat Detection & Self-Healing MLOps Platform | |
| [](https://www.python.org/) | |
| [](https://fastapi.tiangolo.com/) | |
| [](https://mlflow.org/) | |
| [](https://www.docker.com/) | |
| [](https://www.prefect.io/) | |
| [](https://huggingface.co/) | |
| _An enterprise-grade, distributed MLOps system featuring automated data drift monitoring, evaluation-gated retraining loops, and context-aware natural language threat analysis._ | |
| [Live Cloud API](#-live-cloud-deployment) • [Key Features](#-core-architecture--components) • [Tech Stack](#️-technology-stack) • [Quick Start](#-quick-start) | |
| --- | |
| </div> | |
| ## 🎯 Overview | |
| **Sentinel** moves beyond standard academic Machine Learning notebooks by implementing a complete, closed-loop production lifecycle. It is designed to intercept and analyze text streams for high-risk threat language using dual-language neural classifiers (English and Romanized Hinglish). | |
| It doesn't just predict; it **self-heals**. Sentinel routes predictions through deterministic risk guardrails, logs audit telemetry to a persistent remote vault, and autonomously detects data drift to retrain, benchmark, and promote models without human intervention. | |
| ### ✨ Recent Engineering Milestones | |
| - **Multi-Cloud Distributed Architecture:** Successfully decoupled compute from storage, deploying the heavy inference FastAPI layer on **Hugging Face Spaces (16GB RAM)** while securely routing telemetry to a persistent **Render PostgreSQL Vault**. | |
| - **Inference Optimization:** Shaved ~10GB of bloat from the production Docker container (11.3GB ➔ 1.45GB) by strategically overriding default PyTorch CUDA binaries with CPU-only wheels and eliminating pip cache layers, achieving a lightweight edge-ready image. | |
| - **Continuous Integration:** Implemented GitHub Actions CI/CD pipelines to automatically build, test, and verify Docker containers and database connections on every push. | |
| --- | |
| ## 🏛️ Core Architecture & Components | |
| ```text | |
| Incoming Text Stream | |
| ├── 1. FastAPI Gateway (Pydantic Schema Validation) | |
| ├── 2. Heuristic Language Router [English vs. Romanized Hinglish] | |
| ├── 3. DistilBERT Classifier (Hot-loaded via MLflow @production Registry) | |
| ├── 4. Contextual Risk Engine (Probability × Target × Immediacy) | |
| └── 5. PostgreSQL Audit Vault (Remote Cloud Logging) | |
| 1. Defense-in-Design InferenceDual-Language Specialization: Decouples English and Romanized Hindi (Hinglish) into independent pipelines (distilbert-base-uncased and distilbert-base-multilingual-cased) trained with class-weighted cross-entropy loss to handle severe real-world data imbalance (~4% positive rates).Contextual Risk Engine: Intercepts raw neural probabilities, applying regular-expression and keyword-based heuristics to evaluate target specificity and temporal immediacy, returning structured decisions (SAFE, REVIEW, HIGH RISK).2. Autonomous MLOps LoopObservability & Drift: Evidently AI streams production audit logs directly from PostgreSQL, performing statistical distribution comparisons against historical training baselines.Orchestration & Retraining: Prefect manages Directed Acyclic Graphs (DAGs) that orchestrate background retraining jobs using a local GPU when drift triggers occur.The Evaluation Gate ("The Arena"): Prevents silent production regressions by executing strict holdout test-set benchmarking. A candidate model is prohibited from receiving the @production registry tag unless it programmatically outperforms the active champion.🛠️ Technology StackLayerTechnologiesML & NLPPyTorch, Hugging Face Transformers (DistilBERT), scikit-learnBackend & APIFastAPI, Pydantic, SQLAlchemy ORM, Uvicorn, Gradio SDK (Mount)Data & StoragePostgreSQL (Render)MLOps & CI/CDMLflow, Evidently AI, Prefect, GitHub ActionsInfrastructureDocker, Docker Compose, Hugging Face Spaces☁️ Live Cloud DeploymentThe API is actively hosted in the cloud using a custom FastAPI mount injected into a Hugging Face Space.Live Swagger UI: View Documentation & Test LiveExample Cloud API RequestBashcurl -X 'POST' \ | |
| '[https://ankit03-sentinel-api.hf.space/api/v1/analyze](https://ankit03-sentinel-api.hf.space/api/v1/analyze)' \ | |
| -H 'accept: application/json' \ | |
| -H 'Content-Type: application/json' \ | |
| -d '{ | |
| "text": "I am going to find you tonight and make sure you disappear." | |
| }' | |
| Structured JSON Response:JSON{ | |
| "threat_probability": 0.94, | |
| "risk_level": "HIGH", | |
| "immediacy": "HIGH", | |
| "target_identified": true, | |
| "confidence": 0.91, | |
| "reason": "Explicit intent + targeted threat language" | |
| } | |
| 🚀 Local Quick StartEnsure you have Docker Desktop and Python 3.11+ installed locally.1. Clone the RepositoryBashgit clone [https://github.com/your-username/Sentinel.git](https://github.com/your-username/Sentinel.git) | |
| cd Sentinel | |
| 2. Spin Up Infrastructure (Docker Compose)Boots a local PostgreSQL database, MLflow server, and the FastAPI backend simultaneously:Bashdocker compose up -d --build | |
| 3. Seed the MLflow Model RegistryPacks and registers the pre-trained production artifact weights into the Dockerized registry:Bashpython ml/training/register_to_docker_mlflow.py | |
| 4. Run Drift Simulation & Self-Healing PipelineBash# Simulate adversarial text drift and write logs to PostgreSQL | |
| python ml/training/simulate_traffic.py | |
| # Detect distribution shift via Evidently AI | |
| python ml/training/drift_detector.py | |
| # Trigger the Prefect automated retrain and evaluation loop | |
| python ml/training/retrain_pipeline.py | |
| 📂 Repository StructurePlaintext├── .github/workflows/ # GitHub Actions CI/CD pipelines | |
| ├── backend/ # FastAPI server, SQLAlchemy schemas, business logic | |
| ├── ml/ | |
| │ ├── data/ # Sourcing scripts, weak-label pipelines, test splits | |
| │ └── training/ # DistilBERT training, Evidently configs, Prefect DAGs | |
| ├── docker-compose.yml # Multi-container local infrastructure | |
| ├── Dockerfile # Optimized 1.45GB CPU-only container definition | |
| ├── app.py # Cloud entrypoint (Trojan Horse FastAPI mount) | |
| └── requirements.txt # Production dependencies | |
| ``` | |