app.py

import gradio
from transformers import pipeline

def process_swedish_text(text):
  # Models from https://huggingface.co./models
  # https://huggingface.co./KBLab/bert-base-swedish-cased-ner
  nlp = pipeline('ner', model='KBLab/bert-base-swedish-cased-ner', tokenizer='KBLab/bert-base-swedish-cased-ner')
  # Run NER
  nlp_results = nlp(text)
  print('nlp_results:', nlp_results)
  # Fix TypeError("'numpy.float32' object is not iterable")
  nlp_results_adjusted = map(lambda entity: dict(entity, **{ 'score': float(entity['score']) }), nlp_results)
  print('nlp_results_adjusted:', nlp_results_adjusted)
  # Return values
  return {'entities': list(nlp_results_adjusted)}

gradio_interface = gradio.Interface(
  fn=process_swedish_text,
  inputs="text",
  outputs="json",
  examples=[
    ["Jag heter Tom och bor i Stockholm."],
    ["Groens malmgård är en av Stockholms malmgårdar, belägen vid Malmgårdsvägen 53 på Södermalm i Stockholm."]
  ]
)
gradio_interface.launch()