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| import gradio as gr | |
| from huggingface_hub import login | |
| 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: | |
| login(token=os.environ["HFTOKEN"]) | |
| ner_pipeline = pipeline( | |
| "token-classification", | |
| model="TypicaAI/HealthcareNER-Fr", | |
| use_auth_token=os.environ["HFTOKEN"], | |
| aggregation_strategy="simple" # 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) | |
| # Highlight entities in the text | |
| html_output = highlight_entities(text, entities) | |
| # Log usage | |
| log_demo_usage(text, len(entities)) | |
| return html_output | |
| def highlight_entities(text, entities): | |
| """Highlight identified entities in the input text.""" | |
| highlighted_text = text | |
| for entity in entities: | |
| entity_text = entity["word"] | |
| highlighted_text = highlighted_text.replace( | |
| entity_text, | |
| f'<mark style="background-color: yellow;">{entity_text}</mark>' | |
| ) | |
| return f"<p>{highlighted_text}</p>" | |
| 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 | |
| demo = gr.Interface( | |
| fn=process_text, | |
| inputs=gr.Textbox( | |
| label="Paste French medical text", | |
| placeholder="Le patient présente une hypertension artérielle...", | |
| lines=5 | |
| ), | |
| outputs=gr.HTML(label="Identified Medical Entities"), | |
| title="French Healthcare NER Demo | As featured in 'NLP on OCI'", | |
| description=""" | |
| 🔬 Live demo of the French Healthcare NER model built in Chapter 5 of 'NLP on OCI' | |
| 📚 Follow along with the book to build this exact model step-by-step | |
| 🏥 Perfect for medical text analysis, clinical studies, and healthcare compliance | |
| ⚡ Powered by Oracle Cloud Infrastructure | |
| By [Hicham Assoudi] - Oracle Consultant & AI Researcher | |
| """, | |
| examples=[ | |
| ["Le patient souffre d'hypertension et diabète de type 2. Traitement: Metformine 500mg."], | |
| ["Antécédents: infarctus du myocarde en 2019. Allergie à la pénicilline."] | |
| ] | |
| ) | |
| # 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 'NLP on OCI' | |
| - ✓ Step-by-step implementation | |
| - ✓ Performance optimization | |
| - ✓ Enterprise deployment patterns | |
| - ✓ Complete source code | |
| [Get the Book](your-book-link) | Use code `NERSPACE` for 15% off | |
| """) | |
| 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() | |