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


# Define the main demo interface
with gr.Blocks() as demo:
    with gr.Row():
        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.HighlightedText(),
            #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 6 of the book 'Natural Language Processing on Oracle Cloud Infrastructure: Building Transformer-Based NLP Solutions Using Oracle AI and Hugging Face Kindle Edition'
            
            📚 Follow along with the book to build this exact model step-by-step
            🏥 Perfect for medical text analysis, clinical studies, and healthcare compliance
            ⚡ Model Trained on Oracle Cloud Infrastructure (OCI)
            
            By [Hicham Assoudi] - AI Researcher (Ph.D.) • Oracle Consultant • Author 
            """,
            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."]
            ]
        )

    # 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()