app.py

import gradio as gr
from transformers import pipeline

# Load NER model from Hugging Face Hub
model_checkpoint = "./"  # Replace with your Hugging Face repo
ner_pipeline = pipeline("token-classification", model=model_checkpoint, tokenizer=model_checkpoint)

# Define function for Gradio
def ner_prediction(text):
    entities = ner_pipeline(text)
    output = []
    for entity in entities:
        word = entity["word"]
        label = entity["entity"]
        confidence = round(entity["score"], 4)
        output.append(f"{word}: {label} ({confidence})")
    return "\n".join(output)

# Create Gradio interface
iface = gr.Interface(
    fn=ner_prediction,
    inputs=gr.Textbox(lines=3, placeholder="Enter a sentence..."),
    outputs="text",
    title="Named Entity Recognition (NER)",
    description="Enter a sentence and see which words are recognized as named entities.",
    examples=[["Barack Obama was born in Hawaii."], ["Google is headquartered in Mountain View, California."]]
)

# Run the Gradio app
iface.launch()