senn-bandgap 0.1.0

SENN bandgap predictor for perovskite compositions

SENN Bandgap Predictor

This repository packages a SENN perovskite bandgap model as:

1. a Python package,
2. a command-line tool,
3. a FastAPI HTTP API,
4. a Streamlit web app.

Input convention

The model input order is:

[MA, FA, Cs, Br, Cl, I]

Internally:

• site_a = [MA, FA, Cs]
• site_x = [Br, Cl, I]

Local installation

cd senn_bandgap_deploy_template
pip install -e .

Python usage

from senn_bandgap import BandgapPredictor

predictor = BandgapPredictor()
y = predictor.predict([[0.0, 1.0, 0.0, 0.0, 0.0, 1.0]])
print(y[0])

CLI usage

senn-bandgap --MA 0 --FA 1 --Cs 0 --Br 0 --Cl 0 --I 1

Start FastAPI server

uvicorn api_fastapi:app --host 0.0.0.0 --port 8000

Open:

http://127.0.0.1:8000/docs

Example request:

curl -X POST "http://127.0.0.1:8000/predict"   -H "Content-Type: application/json"   -d '{"MA":0,"FA":1,"Cs":0,"Br":0,"Cl":0,"I":1}'

Start Streamlit app

streamlit run app_streamlit.py

Deployment options

Streamlit Community Cloud

Push this repository to GitHub, then select app_streamlit.py as the entrypoint.

Hugging Face Spaces

Create a Space and use Gradio or Streamlit as the SDK. For Streamlit, keep app_streamlit.py and requirements.txt.

Docker + FastAPI

docker build -t senn-bandgap .
docker run -p 8000:8000 senn-bandgap

Important note

If the original training used input normalization, target scaling, or composition preprocessing, you must reproduce exactly the same preprocessing during inference. Otherwise the API will run, but the predicted bandgap values may be numerically inconsistent with training.