README.md

metadata

title: MedicalClassifier
emoji: 📚
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.26.0
python_version: '3.13'
app_file: app.py
pinned: false
license: apache-2.0
short_description: Pneumonia classifier student lab
models:
  - therayelab/pneumoniamnist-resnet18
datasets:
  - galilai-group/medmnist

MedicalClassifier · Student Lab

Educational Gradio demo: upload a teaching image, guess a hidden test label, and evaluate all 624 official PneumoniaMNIST test images with a confusion matrix, sensitivity, specificity, precision, mistake gallery and CSV export.

Not for diagnosis or treatment. Do not upload identifiable patient images. Softmax scores are not calibrated clinical probabilities. Uploaded-image provenance is unknown.

Run locally

python -m venv .venv
# Activate the environment for your operating system.
python -m pip install -r requirements.txt
python app.py

Model and dataset download on first use. CPU is sufficient; first benchmark may take a minute. Inference is cached in memory, and downloads are stored in .cache/ (override with APP_CACHE). No paid inference service is needed.

Deploy to the existing Space

Upload app.py, core.py, requirements.txt, and this README.md to therayelab/MedicalClassifier. Keep its CPU Basic hardware. Alternatively, sign in locally using hf auth login with a token permitted to write to that Space, then run from this directory:

hf upload therayelab/MedicalClassifier . --repo-type space --include app.py --include core.py --include requirements.txt --include README.md

Never put a token in the source files. Public model and dataset access needs no Space secret.

Provenance and limitations

Model revision: a5e0ebebfcf20bee6701e6d528e33c5bf7a33b94. Inference uses its saved processor and safetensors weights with remote code disabled. IDs 0/1 map to Normal/Pneumonia according to official MedMNIST metadata. The model's training settings name train and validation; this app uses only test. This does not independently establish no prior exposure or duplicate images.

The official 28×28 archive is downloaded from https://zenodo.org/records/10519652/files/pneumoniamnist.npz?download=1 and checked against MD5 28209eda62fecd6e6a2d98b1501bb15f. It is the same archive used by the training dataset loader. No dataset remote code is executed.

Test-set threshold changes are teaching experiments, not independent evaluation after threshold optimization. The single-image views use argmax. The model cannot reliably reject non-X-rays or establish clinical validity. New institutions, populations and acquisition methods can change performance.

PneumoniaMNIST: CC BY 4.0. Attribution: Yang et al., MedMNIST v2, Scientific Data (2023), https://medmnist.com/. App temporary uploads and CSVs use Gradio cache cleanup, hourly for files at least one hour old. Do not use this public demo to process sensitive patient data.