Create app.py

app.py

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+import gradio as gr
+
+from transformers import pipeline
+get_completion = pipeline("ner", model="dslim/bert-base-NER")
+
+def merge_tokens(tokens):
+    merged_tokens = []
+    for token in tokens:
+        if (merged_tokens and token['word'].startswith('##')) or (merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:])):
+            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
+            merged_tokens[-1] = last_token
+
+        else:
+            # Otherwise, add the token to the list
+            merged_tokens.append(token)
+
+    return merged_tokens
+
+def ner_merged(input):
+    output = get_completion(input)
+    merged_tokens = merge_tokens(output)
+    return {"text": input, "entities": merged_tokens}
+
+demo = gr.Interface(fn=ner_merged,
+                    # inputs=[gr.Textbox(label="Text to find entities", lines=2)],
+                    # outputs=[gr.HighlightedText(label="Text with entities")],
+                    # title="NER with dslim/bert-base-NER",
+                    # description="Find entities using the `dslim/bert-base-NER` model under the hood!",
+                    inputs=[gr.Textbox(label="Type or paste text to find Named Entities or even select and submit below examples", lines=2)],
+                    outputs=[gr.HighlightedText(label="Text with Named Entities identified")],
+                    title="Named Entity Recognition test and demo app by Srinivas.V ",
+                    description="Find entities",
+                    allow_flagging="never",
+                    examples=["My name is Srinivas and I live in Dubai, United Arab Emirates. I love DeepLearningAI",
+                    "I am a Data Scientist and I am a citizen of Bharat"])
+demo.launch()