app.py

# import gradio as gr

# def greet(name):
#     return "Hello " + name + "!!"

# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
# demo.launch()

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

# Load model and tokenizer from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("Mhammad2023/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained(
    "Mhammad2023/bert-finetuned-ner", 
    from_tf=True,
    torch_dtype=torch.float32,
    device_map="cpu"
)
classifier = pipeline("token-classification", model=model, tokenizer=tokenizer, device="cpu")

def predict(text):
    results = classifier(text)
    if not results:
        return "No entities found"
    
    output = []
    for entity in results:
        output.append(f"{entity['word']}: {entity['entity']} ({round(entity['score']*100, 2)}%)")
    
    return "\n".join(output)

gr.Interface(fn=predict, inputs="text", outputs="text", title="Named Entity Recognition").launch()