app.py

import json

import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")

EXAMPLE_TEXTS = []
with open("examples.json", "r") as f:
    example_json = json.load(f)
    EXAMPLE_TEXTS = [x["text"] for x in example_json]


pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")


def ner(text):
    raw = pipe(text)
    result = {
        "text": text,
        "entities": [
            {
                "entity": x["entity_group"],
                "word": x["word"],
                "score": x["score"],
                "start": x["start"],
                "end": x["end"],
            }
            for x in raw
        ],
    }
    return result, {}


interface = gr.Interface(
    ner,
    inputs=gr.Textbox(label="Input", value=""),
    outputs=[gr.HighlightedText(combine_adjacent=True), "json"],
    examples=EXAMPLE_TEXTS,
)

interface.launch()