create the app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+
+get_completion = pipeline("ner", model="dslim/bert-base-NER")
+
+def ner(input):
+    output = get_completion(input)
+    return {"text": input, "entities": output}
+
+
+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 ner(input):
+    output = get_completion(input)#, parameters=None, ENDPOINT_URL=API_URL
+    merged_tokens = merge_tokens(output)
+    return {"text": input, "entities": merged_tokens}
+
+
+
+if __name__ == "__main__":
+    # Create the Gradio interface
+    demo = gr.Interface(fn=ner,
+                    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
+                    outputs=[gr.HighlightedText(label="Text with entities")],
+                    title="Sanofi Digital M&S Demo: Name Entity Recognition",
+                    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
+                    allow_flagging="never",
+                    examples=["My name is Yunke, I lead a data science team at Sanofi. I live in Cambridge, Massachusattes", "My address is: 450 water street, Cambridge, MA"])
+    # Launch the interface
+    demo.launch() #share=True
+    
+

requirements.txt

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+transformers
+gradio
+torch
+# dotenv