Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from huggingface_hub import login
 from transformers import pipeline
 import os
 
@@ -10,39 +9,20 @@ 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="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}
 
-    # 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."""
@@ -60,16 +40,21 @@ demo = gr.Interface(
     #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'
+    🔬 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
-    ⚡ Powered by Oracle Cloud Infrastructure
+    ⚡ Model Trained on Oracle Cloud Infrastructure (OCI)
     
-    By [Hicham Assoudi] - Oracle Consultant & AI Researcher
+    By [Hicham Assoudi] - AI Researcher (Ph.D.) • Oracle Consultant • Author 
     """,
     examples=[
-        ["Le patient souffre d'hypertension et diabète de type 2. Traitement: Metformine 500mg."],
+        ["Le medecin donne des antibiotiques en cas d'infections des voies respiratoires."],
+        ["Le médecin recommande des corticoïdes pour réduire l'inflammation dans les poumons."],
+        ["Pour soulager les symptômes d'allergie, le médecin prescrit des antihistaminiques."],
+        ["Pour gérer le diabète, le médecin prescrit une insulinothérapie."],
+        ["Après une blessure musculaire, le patient doit suivre une physiothérapie."],
+        ["En cas d'infection bactérienne, le médecin recommande une antibiothérapie."],
         ["Antécédents: infarctus du myocarde en 2019. Allergie à la pénicilline."]
     ]
 )
@@ -79,13 +64,13 @@ 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'
+    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](your-book-link) | Use code `NERSPACE` for 15% off
+    [Get the Book](https://a.co/d/eg7my5G)
     """)
     
     with gr.Row():