Update app.py

app.py

@@ -1,11 +1,25 @@
 import gradio as gr
+import spaces
 from transformers import pipeline
+from typing import List, Dict, Any
 
-def merge_tokens(tokens):
+def merge_tokens(tokens: List[Dict[str, any]]) -> List[Dict[str, any]]:
+    """
+    Merges tokens that belong to the same entity into a single token.
+
+    Args:
+        tokens (List[Dict[str, any]]): A list of token dictionaries, each containing information about 
+                                       the entity, word, start, end, and score.
+
+    Returns:
+        List[Dict[str, any]]: A list of merged token dictionaries, where tokens that are part of the 
+                              same entity are combined into a single token with updated word, end, 
+                              and score values.
+    """
     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
+            # If the 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']
@@ -16,19 +30,51 @@ def merge_tokens(tokens):
 
     return merged_tokens
 
-get_completion = pipeline("ner", model="dslim/bert-base-NER")
+# Initialize Model
+get_completion = pipeline("ner", model="dslim/bert-base-NER", device=0)
+
+@spaces.GPU(duration=120)
+def ner(input: str) -> Dict[str, Any]:
+    """
+    Performs Named Entity Recognition (NER) on the given input text and merges tokens that belong 
+    to the same entity into a single entity.
+
+    Args:
+        input (str): The input text to analyze for named entities.
 
-def ner(input):
+    Returns:
+        Dict[str, Any]: A dictionary containing the original text and a list of identified entities 
+                        with merged tokens.
+                        - "text": The original input text.
+                        - "entities": A list of dictionaries, where each dictionary contains information 
+                          about a recognized entity, including the word, entity type, score, and positions.
+    """
     output = get_completion(input)
     merged_tokens = merge_tokens(output)
     return {"text": input, "entities": merged_tokens}
 
-gr.close_all()
-demo = gr.Interface(fn=ner,
-                    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
+####### GRADIO APP #######
+title = """<h1 id="title"> Named Entity Recognition </h1>"""
+
+description = """
+- The model used for Recognizing entities [BERT-BASE-NER](https://huggingface.co/dslim/bert-base-NER).
+"""
+
+css = '''
+h1#title {
+  text-align: center;
+}
+'''
+
+theme = gr.themes.Soft()
+demo = gr.Blocks(css=css, theme=theme)
+
+with demo:
+  gr.Markdown(title)
+  gr.Markdown(description)
+  interface = gr.Interface(fn=ner,
+                    inputs=[gr.Textbox(label="Text to find entities", lines=10)],
                     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"])