setup the program

app.py

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+import gradio as gr
+from transformers import pipeline
+
+ner = pipeline(task="token-classification", model="IsmaelMousa/modernbert-ner-conll2003", aggregation_strategy="max")
+
+def extract(text):
+    """
+    Extract named entities from text
+    
+    :param text: input text
+    :return: formatted output and highlighted text
+    """
+    if not text.strip(): return "Please enter some text to analyze.", []
+    
+    results = ner(text)
+    output  = ""
+    
+    for entity in results:
+        word    = entity["word"]
+        label   = entity["entity_group"]
+        score   = entity["score"]
+        output += f"**{word}** → {label} (confidence: {score:.2%})\n"
+    
+    if not output: output = "No named entities found in the text."
+    
+    highlighted = []
+    last        = 0
+    
+    for entity in results:
+        start = entity["start"]
+        end   = entity["end"]
+        
+        if start > last: highlighted.append((text[last:start], None))
+        
+        highlighted.append((text[start:end], entity["entity_group"]))
+        last = end
+    
+    if last < len(text): highlighted.append((text[last:], None))
+    
+    return output, highlighted if highlighted else [(text, None)]
+
+examples = [["Hi, I'm Ismael Mousa from Palestine working for NVIDIA inc."]		   ,
+	    ["Apple Inc. was founded by Steve Jobs in Cupertino, California."]		   ,
+    	    ["The Eiffel Tower in Paris, France attracts millions of visitors every year."],
+            ["Barack Obama was the 44th President of the United States."]                  ,]
+
+with gr.Blocks(title="Named Entity Recognition") as demo:
+    gr.Markdown(
+        """
+        # 🏷️ Named Entity Recognition
+        Extract named entities (persons, organizations, locations) from text using ModernBERT.
+        
+        **Model:** [IsmaelMousa/modernbert-ner-conll2003](https://huggingface.co/IsmaelMousa/modernbert-ner-conll2003)
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Text", placeholder="Enter text to analyze...", lines=5)
+            submit_btn = gr.Button("Extract Entities", variant="primary")
+        
+        with gr.Column():
+            output_text      = gr.Markdown(label="Detected Entities")
+            highlighted_text = gr.HighlightedText(label="Highlighted Text", combine_adjacent=True, show_legend=True)
+    
+    gr.Examples(examples=examples, inputs=input_text, outputs=[output_text, highlighted_text], fn=extract, cache_examples=False)
+    
+    submit_btn.click(fn=extract, inputs=input_text, outputs=[output_text, highlighted_text])
+    input_text.submit(fn=extract, inputs=input_text, outputs=[output_text, highlighted_text])
+
+
+if __name__ == "__main__": demo.launch()

requirements.txt

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+transformers
+torch
+gradio