app.py

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

# Load the BERT tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Define the prediction function
def predict_sentiment(text):
    # Tokenize the text
    encoded_text = tokenizer.encode_plus(
        text, 
        max_length=128, 
        padding='max_length', 
        truncation=True, 
        return_attention_mask=True, 
        return_tensors='pt'
    )

    # Make the prediction
    output = model(encoded_text['input_ids'], attention_mask=encoded_text['attention_mask'])
    prediction = torch.argmax(output.logits, dim=1).item()
    
    # Return the predicted sentiment
    if prediction == 1:
        return "Positive"
    else:
        return "Negative"

# Define the Streamlit app
def app():
    st.title("BERT Sentiment Analysis (non pipeline")
    st.write("Upload a CSV file with a 'text' column and I'll predict the sentiment for each row.")

    # Get user input
    file = st.file_uploader("Upload CSV file", type=["csv"])
    if file is not None:
        df = pd.read_csv(file)

        # Make the predictions and add them to the dataframe
        df['sentiment'] = df['text'].apply(predict_sentiment)

        # Display the results
        st.write(df)

if __name__ == '__main__':
    app()