| import gradio as gr | |
| from transformers import pipeline | |
| # Specify the name of the model | |
| model_name = 'ehri-ner/xlm-roberta-large-ehri-ner-all' | |
| # Load the model from Hugging Face | |
| ner_model = pipeline('ner', model=model_name) | |
| def predict(text): | |
| # Use the model to predict the named entities | |
| entities = ner_model(text) | |
| # Format the output | |
| result = {entity['entity']: entity['word'] for entity in entities} | |
| return result | |
| # Define the Gradio interface | |
| iface = gr.Interface(fn=predict, | |
| inputs=gr.inputs.Textbox(lines=2, placeholder='Enter text here...'), | |
| outputs='json') | |
| # Launch the interface | |
| iface.launch() |