README.md

metadata

title: Uric-Acid Colorimetric Concentration Predictor
emoji: 🧪
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.9.1
python_version: '3.11'
app_file: app.py
pinned: false
license: mit

Uric-Acid Colorimetric Concentration Predictor

Interactive demo for the smartphone-based colorimetric uric-acid (UA) biosensor built on a bimetallic Ag–Cu micro-flower (Ag-Cu-MF) nanozyme + TMB chemistry.

Enter the R, G, B of an oxidised-TMB reaction well and the buffer; if you have the same phone's blank (0 μM) reading, enable Use per-device blank for best accuracy. Four models predict UA concentration in parallel and the ANN released with the manuscript is highlighted.

Models

Model	Description	Held-out R²	RMSE (μM)
Linear (raw RGB)	per-buffer OLS on R, G, B	0.587	128.5
Linear (ΔRGB)	per-buffer OLS on ΔR, ΔG, ΔB	0.722	105.4
Random Forest	500 trees, depth 10, all features	0.893	65.3
ANN (this work)	5-seed MLP-Wide-Reg [128, 64] ensemble	0.874	70.9

Metrics are the published values on the 28-sample held-out fold (one random replicate per buffer × concentration cell). ΔRGB = blank − measurement, computed per device. Features are R, G, B, ΔR, ΔG, ΔB plus a 4-D one-hot buffer code.

Files

• app.py — Gradio interface
• model.pt — 5-seed ANN ensemble + fitted scaler
• rf_model.pkl — Random Forest reference
• baselines_linear.json — per-buffer linear coefficients (raw RGB and ΔRGB)
• mean_blanks.json — cohort-mean blank RGB per buffer (fallback)
• inference.py — standalone (non-UI) inference helper

Caveats

• Trained on a single Ag-Cu-MF + TMB chemistry, four buffers, six phones.
• Predictions are clamped at 0 μM and are most reliable within 0–600 μM.
• Mean-blank fallback is approximate for devices far from the training cohort.
• Research use only; not a medical device.