app.py

import gradio as gr
title = "Model for Biomedical NER"
description = "Gradio Demo of a pretrained model used for NER without fine-tuning. To test model predictions, simply add your text, or click one of the examples to load them. These predictions are used to perform NER as described in the link below."
article = "<p style='text-align: center'><a href='https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html' target='_blank'>Model pretrained on biomedical corpus and used for NER without fine-tuning</a> | <a href='https://huggingface.co/ajitrajasekharan/biomedical' target='_blank'>HF model page</a></p><p style='text-align: left; color: #6f6f6f'><a href='https://huggingface.co/spaces/ajitrajasekharan/Qualitative-pretrained-model-evaluation' target='_blank'><br/><I><u>Note:Streamlit version of this app is a better choice to examine model than this app:-</u> <br/>- Control over number of results to display<br/>- Examine both masked position and [CLS] predictions <br/>- Compare this model results with other pretrained BERT models.</i></a></p>"
examples = [
    ["Lou Gehrig who works for XCorp suffers from [MASK]"],["A [MASK] level below 60 indicates chronic kidney disease"],["There are no specific treatment options specifically indicated for [MASK]"],["Paul Erdos died at [MASK]"]
]
gr.Interface.load("huggingface/ajitrajasekharan/biomedical",title=title,description=description,article=article, examples=examples, allow_flagging="never",enable_queue=True).launch()