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app.py

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+import gradio as gr
+from transformers import pipeline
+
+ner = pipeline('ner')
+
+def merge_tokens(tokens):
+    merged_tokens = []
+    for token in tokens:
+        if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
+            # If current token continues the entity of the last one, merge them
+            last_token = merged_tokens[-1]
+            last_token['word'] += token['word'].replace('##', '')
+            last_token['end'] = token['end']
+            last_token['score'] = (last_token['score'] + token['score']) / 2
+        else:
+            # Otherwise, add the token to the list
+            merged_tokens.append(token)
+
+    return merged_tokens
+
+def named(input):
+    output = ner(input)
+    merged_word = merge_tokens(output)
+    return {'text': input, 'entities': merged_word}
+
+a = gr.Interface(fn=named, 
+                 inputs=[gr.Textbox(label="Text input", lines= 2)],
+                 outputs=[gr.HighlightedText(label='Text with entities')],
+                 title='Named Entity Recognition', examples=["My name is Andrew, I'm building DeeplearningAI and I live in California", "My name is Poli, I live in Vienna and work at HuggingFace"])
+a.launch()
+    

requirements.txt

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+torch
+transformers