app.py

import streamlit as st
from transformers import AutoModelWithHeads, AutoTokenizer
import torch

# Load pre-trained BERT model with adapter support
st.title("Adapter Transformers for Text Classification")

@st.cache_resource
def load_model():
    model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
    
    # Add and activate an adapter
    adapter_name = "my_adapter"
    model.add_adapter(adapter_name)
    model.train_adapter(adapter_name)
    model.set_active_adapters(adapter_name)
    
    # Add a classification head (binary classification)
    model.add_classification_head(adapter_name, num_labels=2)
    return model, tokenizer

# Load the model
model, tokenizer = load_model()

# Streamlit input
input_text = st.text_input("Enter text for classification:", "Steve Jobs founded Apple")

if input_text:
    # Tokenize the input
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    
    # Make the prediction
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_class = logits.argmax(dim=-1).item()

    # Display the prediction
    if predicted_class == 0:
        st.write("Prediction: Negative")
    else:
        st.write("Prediction: Positive")