Case Study
Mental Health Detection (Text)
End-to-end NLP classification service using FastAPI and transformer-based text embeddings to identify mental-health related risk categories.
FastAPIHuggingFaceNLPDeployment7 min read
Goal
Create a deployable service that can classify user text into predefined mental-health categories to support triage and analytics workflows.
System design
- Text input API with validation and rate-safe request handling.
- Inference pipeline with preprocessing + model scoring.
- Structured response format for confidence-aware client rendering.
Deployment notes
The API was tested as a lightweight service with clear input/output contracts, enabling easy integration into front-end and dashboard tools.
Key takeaways
- Serving ML models is as much API design as it is model quality.
- Deterministic preprocessing improves production consistency.
- Model confidence should be exposed explicitly for safe usage.