app.py

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

# Specify the local paths where the model and tokenizer are saved
model_dir = "path_to_save_model"
tokenizer_dir = "path_to_save_tokenizer"

# Load the model and tokenizer from the local directories
model = AutoModelForSequenceClassification.from_pretrained(model_dir)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)

# Function to predict the sentiment
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return torch.argmax(probs, dim=-1).item(), probs

# Streamlit interface
st.title("KIWI Classifier")
st.write("Enter a question or statement to classify:")

user_input = st.text_area("Your input", "")
if st.button("Classify"):
    if user_input:
        label, probabilities = predict_sentiment(user_input)
        st.write(f"Prediction: {label}")
        st.write(f"Probabilities: {probabilities.tolist()}")
    else:
        st.write("Please enter some text to classify.")

# Additional instructions or information
st.write("This application uses a fine-tuned BERT model to classify questions and statements.")