Update app.py

app.py

@@ -1,37 +1,44 @@
-# Import the required libraries
-import os  # os provides a way of using operating system-dependent functionality
-import io  # this library provides core tools for working with streams of data
-from IPython.display import Image, display, HTML  # This is used for displaying rich content (e.g., images, HTML) in Jupyter Notebooks
-from PIL import Image  # Python Imaging Library (PIL) is used for opening, manipulating, and saving image files
-import base64   # This library encodes and decodes data in base64 format
-import requests
-import json
-from dotenv import load_dotenv, find_dotenv
 import gradio as gr
-
-
-# Load environment variables from .env file
-load_dotenv(find_dotenv())
-hf_api_key = os.getenv('HF_API_KEY')
-API_URL = os.getenv('HF_API_NER_BASE')
-
-#Get completion to be defined?
-
-def ner(input):
-    output = get_completion(input, parameters=None, endpoint_url=API_URL)
-    return {"text": input, "entities": output}
-
-# Create a Gradio interface
-iface = 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 Michela and I live in Italy", "My name is Andrew and work at HuggingFace"])
-
-# Launch the app (this will allow you to test locally before uploading to Hugging Face)
-iface.launch()
+from transformers import pipeline
+
+# Load the NER pipeline from Hugging Face
+ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
+
+# Function to perform NER
+def perform_ner(text):
+    # Run NER on the input text
+    results = ner_pipeline(text)
+    
+    # Format results for better readability
+    formatted_results = [
+        {
+            "Entity": result["entity"],
+            "Word": result["word"],
+            "Score": round(result["score"], 4),
+            "Start": result["start"],
+            "End": result["end"]
+        }
+        for result in results
+    ]
+    return formatted_results
+
+# Define the Gradio interface
+title = "Named Entity Recognition (NER) App"
+description = "Enter text below to extract named entities like persons, organizations, and locations using a Hugging Face model."
+
+interface = gr.Interface(
+    fn=perform_ner,                   # The function to process input
+    inputs=gr.Textbox(lines=5, label="Input Text"),  # Text input for the user
+    outputs=gr.Dataframe(             # Display results as a table
+        headers=["Entity", "Word", "Score", "Start", "End"], 
+        label="Recognized Entities"
+    ),
+    title=title,
+    description=description
+)
+
+# Launch the Gradio app
+interface.launch()