app.py

import streamlit as st
from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load the pre-trained BERT model and tokenizer
model_name = "bert-base-uncased"  # Replace with your specific model if needed
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

# Streamlit UI
st.title("BERT Text Classification")
text_input = st.text_input("Enter text for classification:")

if text_input:
    # Tokenize input
    inputs = tokenizer(text_input, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class = torch.argmax(logits, dim=1).item()
    st.write(f"Predicted Class: {predicted_class}")