app.py

import streamlit as st
import pandas as pd
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Download and load the model and tokenizer
model_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Define a function to classify a single text
def classify_text(text):
    # Tokenize the text and add special tokens
    inputs = tokenizer.encode_plus(
        text,
        add_special_tokens=True,
        return_tensors='pt',
        max_length=512
    )
    
    # Get the input IDs and attention mask
    input_ids = inputs['input_ids']
    attention_mask = inputs['attention_mask']
    
    # Get the predicted label
    with torch.no_grad():
        outputs = model(input_ids, attention_mask)
        logits = outputs[0]
        predicted_label = torch.argmax(logits, dim=1).item()
        
    return predicted_label

# Define the Streamlit app
def main():
    st.title('Text Classification with BERT')
    
    # Allow the user to upload a CSV file
    uploaded_file = st.file_uploader('Upload a CSV file', type='csv')
    if uploaded_file is not None:
        data = pd.read_csv(uploaded_file)
        
        # Create a new column for the predicted labels
        data['predicted_label'] = data['text'].apply(classify_text)
        
        st.write(data)

if __name__ == '__main__':
    main()