app.py

import streamlit as st
from transformers import pipeline

# Load pre-trained model and tokenizer from Hugging Face
model_name = "google-bert/bert-base-uncased"
pipe = pipeline("text-classification", model=model_name)

# Custom labels for your classification task
labels = {
    "LABEL_0": "Negative",
    "LABEL_1": "Positive"
}

# Streamlit app
st.title("Text Classification")

st.write("This app uses a pre-trained BERT model to classify text into positive or negative sentiment.")

input_text = st.text_area("Enter text to classify")

if st.button("Classify"):
    if input_text:
        # Perform classification
        result = pipe(input_text)
        
        # Extract label and score
        label = labels.get(result[0]['label'], result[0]['label'])
        score = result[0]['score']

        st.write(f"**Predicted Class:** {label}")
        st.write(f"**Confidence:** {score:.4f}")
    else:
        st.write("Please enter some text to classify.")