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| import gradio as gr | |
| from transformers import pipeline | |
| import os | |
| # Initialize global pipeline | |
| ner_pipeline = None | |
| def load_healthcare_ner_pipeline(): | |
| """Load the Hugging Face pipeline for Healthcare NER.""" | |
| global ner_pipeline | |
| if ner_pipeline is None: | |
| ner_pipeline = pipeline( | |
| "token-classification", | |
| model="TypicaAI/HealthcareNER-Fr", | |
| aggregation_strategy="first" # Groups B- and I- tokens into entities | |
| ) | |
| return ner_pipeline | |
| def process_text(text): | |
| """Process input text and return highlighted entities.""" | |
| pipeline = load_healthcare_ner_pipeline() | |
| entities = pipeline(text) | |
| return {"text": text, "entities": entities} | |
| def log_demo_usage(text, num_entities): | |
| """Log demo usage for analytics.""" | |
| print(f"Processed text: {text[:50]}... | Entities found: {num_entities}") | |
| # Define the Gradio interface | |
| # Create the Gradio layout | |
| with gr.Blocks() as demo: | |
| gr.Markdown("### French Healthcare NER Demo") | |
| gr.Markdown( | |
| """ | |
| As featured in *Natural Language Processing on Oracle Cloud Infrastructure: Building Transformer-Based NLP Solutions Using Oracle AI and Hugging Face*. | |
| """ | |
| ) | |
| with gr.Row(): | |
| input_box = gr.Textbox( | |
| label="Paste French medical text", | |
| placeholder="Le patient présente une hypertension artérielle...", | |
| lines=5 | |
| ) | |
| output_box = gr.HighlightedText(label="Identified Medical Entities") | |
| submit_button = gr.Button("Analyze Text") | |
| submit_button.click(fn=process_text, inputs=input_box, outputs=output_box) | |
| gr.Markdown("### Examples") | |
| gr.Examples( | |
| examples=[ | |
| ["Le medecin donne des antibiotiques en cas d'infections des voies respiratoires e.g. pneumonie."], | |
| ["Dans le cas de l'asthme, le médecin peut recommander des corticoïdes pour réduire l'inflammation dans les poumons."], | |
| ["Pour soulager les symptômes d'allergie, le médecin prescrit des antihistaminiques."], | |
| ["Si le patient souffre de diabète de type 2, le médecin peut prescrire une insulinothérapie par exemple: Metformine 500mg."], | |
| ["Après une blessure musculaire ou une maladies douloureuses des tendons comme une tendinopathie, le patient pourrait suivre une kinésithérapie ou une physiothérapie."], | |
| ["En cas d'infection bactérienne, le médecin recommande une antibiothérapie."], | |
| ["Antécédents: infarctus du myocarde en 2019. Allergie à la pénicilline."] | |
| ], | |
| inputs=input_box | |
| ) | |
| # Footer/Disclaimer section | |
| gr.Markdown( | |
| """ | |
| --- | |
| ### Disclaimer | |
| This is a **demo model** provided for educational purposes. It was trained on a limited dataset and is not intended for production use, clinical decision-making, or real-world medical applications. | |
| """ | |
| ) | |
| # Add marketing elements | |
| with gr.Blocks() as marketing_elements: | |
| gr.Markdown(""" | |
| ### 📖 Get the Complete Guide | |
| Learn how to build and deploy this exact model in 'Natural Language Processing on Oracle Cloud Infrastructure: Building Transformer-Based NLP Solutions Using Oracle AI and Hugging Face Kindle Edition' | |
| - ✓ Step-by-step implementation | |
| - ✓ Performance optimization | |
| - ✓ Enterprise deployment patterns | |
| - ✓ Complete source code | |
| [Get the Book](https://a.co/d/eg7my5G) | |
| """) | |
| with gr.Row(): | |
| email_input = gr.Textbox( | |
| label="Get the French Healthcare NER Dataset", | |
| placeholder="Enter your business email" | |
| ) | |
| submit_btn = gr.Button("Access Dataset") | |
| # Launch the Gradio demo | |
| if __name__ == "__main__": | |
| demo.launch() | |