| import gradio as gr | |
| from transformers import pipeline | |
| # Load your model with aggregation (recommended for NER) | |
| model = pipeline("ner", model="blaze999/Medical-NER", aggregation_strategy="simple") | |
| # Define the function | |
| def recognize_entities(text): | |
| result = model(text) | |
| return result | |
| # Create the Gradio interface | |
| interface = gr.Interface( | |
| fn=recognize_entities, | |
| inputs=gr.Textbox(lines=4, placeholder="Enter medical text here..."), | |
| outputs="json", | |
| title="Medical Named Entity Recognition", | |
| description="This app uses a custom model to recognize medical entities in text." | |
| ) | |
| # Launch the interface | |
| interface.launch() | |