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| 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 **[email protected]**. | |
| """, | |
| 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() |