app.py

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()