import gradio as gr
from transformers import pipeline
from huggingface_hub import login
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/magbert-ner",
            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 main demo interface
demo = gr.Interface(
    fn=process_text,
    inputs=gr.Textbox(
        label="Paste French text",
        placeholder="La Coupe du monde de football 2030 se déroulera au Maroc, en Espagne et au Portugal.",
        lines=5
    ),
    outputs=gr.HighlightedText(label="Identified Entities"),
    title="🌟 MagBERT-NER: High-Performing French NER",
    description="""
    _By **[Hicham Assoudi](https://huggingface.co/hassoudi)** – AI Researcher (Ph.D.), Oracle Consultant, and Author._ 🔗 [Follow me on LinkedIn](https://www.linkedin.com/in/assoudi)
    
    MagBERT-NER is a robust **Named Entity Recognition (NER)** model for the **French language**, trained on a **manually curated dataset** from diverse Moroccan sources. It’s designed to handle **names, places, currencies**, and other entities with exceptional precision, especially in **Moroccan contexts**.  
    ## 🚀 Highlights:  
    - **20,000+ Downloads**: Trusted by developers and researchers for French NER tasks.  
    - **🌐 Recognized by John Snow Labs**: Adapted for scalability and enterprise-grade applications like **healthcare**.
    """,
    article="""
    ## ⚠️ Disclaimer
    This is a **free demo model** provided without support. While it showcases high precision and is ideal for educational and exploratory purposes, it is not intended for production or commercial use.  

    For production-grade or commercial models, please contact us at **assoudi@typica.ai**.
    """,
    examples=[
        ["Fatima Zahra a acheté une babouche artisanale au souk de Fès pour 250 dirhams."],
        ["Youssef a participé à une réunion importante à Tétouan pendant Ramadan."],
        ["Amina a vendu des tapis au marché local pour 1 200 dirhams."],
        ["Le projet Al Massira, situé près d'Oujda, sera lancé en septembre."],
        ["Khadija a reçu un prix pour ses recherches sur l'arganier lors d'une cérémonie à Agadir."],
        ["L'association Amal prévoit une collecte de fonds à Tanger pour soutenir les artisans locaux."],    ]
)

# Launch the Gradio demo
if __name__ == "__main__":
    demo.launch()