app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
import os

# Define the path where model and tokenizer files are located
model_directory = "AdilHayat173/token_classification"

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_directory)
model = AutoModelForTokenClassification.from_pretrained(model_directory)
nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

st.title("Token Classification with Hugging Face")

# Text input from user
user_input = st.text_area("Enter text for token classification:", "")

if st.button("Classify Text"):
    if user_input:
        # Token classification
        results = nlp(user_input)

        # Display results
        st.write("### Token Classification Results")
        for entity in results:
            st.write(f"**Token:** {entity['word']}")
            st.write(f"**Label:** {entity['entity_group']}")
            st.write(f"**Score:** {entity['score']:.4f}")
            st.write("---")
    else:
        st.write("Please enter some text for classification.")