Delete app.py

app.py

@@ -1,39 +0,0 @@
-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()