Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the model locally
+model_name = "dslim/bert-base-NER"
+ner_pipeline = pipeline("ner", model=model_name, tokenizer=model_name, aggregation_strategy="simple")
+
+def merge_tokens(tokens):
+    merged_tokens = []
+    for token in tokens:
+        if merged_tokens and token['entity_group'].startswith('I-') and merged_tokens[-1]['entity_group'].endswith(token['entity_group'][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_text):
+    # Use the pipeline to get entities
+    output = ner_pipeline(input_text)
+    merged_tokens = merge_tokens(output)
+    return {"text": input_text, "entities": merged_tokens}
+
+# 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="NER with dslim/bert-base-NER",
+                    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
+                    allow_flagging="never",
+                    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"])
+
+demo.launch()