Create app.py

app.py

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+import gradio as gr
+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()