Create app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+
+# Load NER model for English and Arabic
+ner_pipeline_en = pipeline('ner', grouped_entities=True)  # English model
+ner_pipeline_ar = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-ner', grouped_entities=True)  # Arabic model
+
+def get_ner_pipeline(language='English'): #Return the NER model based on the specified language.
+    if language == 'Arabic':
+        return ner_pipeline_ar  # Return Arabic model
+    return ner_pipeline_en  # Return English model
+
+def highlight_entities(text, language='English'): #Extract entities and return the text with highlighted entities.
+    ner_pipeline = get_ner_pipeline(language)  # Get the appropriate NER model
+    entities = ner_pipeline(text)  # Process the input text
+
+    # Create a list to store the highlighted text
+    highlighted_text_data = []
+    last_index = 0
+
+    for entity in entities:
+        entity_name = entity['word']  # Get the entity name
+        entity_type = entity['entity_group']  # Get the entity type
+        # Add text before the entity
+        highlighted_text_data.append((text[last_index: text.index(entity_name, last_index)], None))
+        # Add the entity with its type
+        highlighted_text_data.append((f"{entity_name}", entity_type))
+        last_index = text.index(entity_name, last_index) + len(entity_name)
+
+    # Add any remaining text after the last entity
+    highlighted_text_data.append((text[last_index:], None))
+
+    return highlighted_text_data  # Return the highlighted entities
+
+# Custom CSS for right-to-left (RTL) text alignment
+custom_css = """
+#output {
+    direction: rtl;  /* Right-to-left for Arabic */
+    text-align: right; /* Align right for Arabic */
+}
+"""
+
+# Gradio interface setup
+interface = gr.Interface(
+    fn=highlight_entities,  # Function to call
+    inputs=[
+        gr.Textbox(label="Input Text", lines=5, placeholder="Enter your text here..."),  # Text input
+        gr.Radio(label="Select Language", choices=["English", "Arabic"], value="English")  # Language selection
+    ],
+    outputs=gr.HighlightedText(label="Highlighted NER Results", elem_id="output"),  # Output as highlighted text
+    title="Named Entity Recognition",  # Interface title
+    description="Select a language and enter text to extract and highlight named entities.",  # Description
+    examples=[
+        ["Hugging Face Inc. is a company based in New York City.", "English"],
+        ["أحمد هو عالم في مجال الذكاء الاصطناعي", "Arabic"] ],  # Add example inputs
+    css=custom_css  # Apply custom CSS for RTL
+)
+
+# Launch the interface
+interface.launch()  # Start the Gradio interface