← all systems · case file 04 · screen_risk() · Live on HF Spaces · Zenodo Preprint

PredictLab

From raw health data to real insights.

  • scikit-learn
  • FastAPI
  • Llama 3.1 (Groq)
  • Docker
  • Hugging Face Spaces

Summary

A containerized, full-stack clinical risk screening platform on Hugging Face Spaces. Three scikit-learn classifiers screen for diabetes, stroke and heart disease across 30 clinical inputs, and Llama 3.1 8B on Groq explains each result in accessible language. Published as a research preprint on Zenodo in May 2026.1Live demo on Hugging FaceInteractive Demo · huggingface.co

What the sources say, with how each number was obtained
ValueWhatMethodSource
3Clinical screening modelsAn SVM (diabetes), a Random Forest (stroke) and a Logistic Regression (heart disease), each a scikit-learn pipeline behind one FastAPI service.[2] repo
30Health inputs analysedInput fields across the three screening forms: 8 + 10 + 12.[2] repo
PreprintPublished on Zenodo, May 2026Zenodo record v1.0, 17 May 2026. A preprint, not peer-reviewed.[3] doi

Problem

A bare 'Diabetic / Not diabetic' output raises more questions than it answers for anyone without a medical or machine-learning background.1Live demo on Hugging FaceInteractive Demo · huggingface.co

Architecture

SVM, Random Forest and Logistic Regression behind one FastAPI service; Llama 3.1 explains each result

  1. 01Three disease models

    An SVM for diabetes, a Random Forest for stroke and Logistic Regression for heart disease, each in its own scikit-learn pipeline.

  2. 02One FastAPI service

    A single FastAPI backend serves the web interface, the prediction endpoints and the AI assistant with no frontend build step, containerized with Docker.

  3. 03Contextual explanations

    Patient inputs and model outputs go to Llama 3.1 8B on Groq, which explains the result in plain language and answers follow-up questions.

github.com/vinayak533 · predictlab
PredictLab: silent screen recording of the product in use.
Fig. 1PredictLab, recorded from the running build.source: Live demo on Hugging Face

Not claimed

Can it diagnose me?
No source says it diagnoses anything, so I won't say it does. It's a screening tool that explains risk. For a diagnosis, see a doctor.

Sources

  1. [1]Live demo on Hugging FaceInteractive Demo ↗
  2. [2]GitHub repositoryRepository ↗
  3. [3]Research preprint (Zenodo DOI)Research Preprint ↗

source [1] · Interactive Demo · PredictLab

Live demo on Hugging Face

huggingface.co/spaces/VINAYAKKV/Predict-Ai

source [2] · Repository · PredictLab

GitHub repository

github.com/vinayak533/PredictLab-

source [3] · Research Preprint · PredictLab

Research preprint (Zenodo DOI)

doi.org/10.5281/zenodo.20253956

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