Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
+#Create Named Entity Recognition Pipeline
+nerp = pipeline("ner", model=model, tokenizer=tokenizer)
+#Build the Named Entity Recognition App
+import gradio as gr
+#Import Merging Tokens function from Helper to Display Output Relevant for User
+from helper import merge_tokens
+#Define Named Entity Recognition Function 
+def ner(input):
+    output = nerp(input)
+    merged_tokens = merge_tokens(output)
+    return {"text": input, "entities": merged_tokens}
+#Set up the User Interface and Launch    
+nerapp = gr.Interface(fn=ner,
+                    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` ",
+                    allow_flagging="never",
+                    examples=["My name is Ranjith, I love AI and I live in Chennai", "My name is Akshay, I live in Germany and work at Cocacola"])
+
+nerapp.launch()