app.py

import streamlit as st
from transformers import pipeline

# Load your fine-tuned model from Hugging Face
MODEL_NAME = "Tryfonas/fine-tuned-bert-classifier-bds24"  # Update with your Hugging Face model name
classifier = pipeline("text-classification", model=MODEL_NAME)

# Streamlit UI
st.title("BERT Text Classifier")
st.write("Enter text below to classify:")

# User input text
user_input = st.text_area("Input Text", "Type here...")

if st.button("Classify"):
    if user_input.strip():
        # Get model prediction
        result = classifier(user_input)

        # Extract label and confidence score
        label = result[0]['label']  # Model output label
        confidence = result[0]['score']  # Confidence score

        # Convert model labels to "Positive" or "Negative"
        if label == "LABEL_1":  # Adjust based on your model's labeling
            sentiment = "Positive 😊"
        elif label == "LABEL_0":
            sentiment = "Negative 😞"
        else:
            sentiment = "Unknown 🤔"

        # Display results
        st.subheader("Prediction:")
        st.write(f"**Sentiment:** {sentiment}")
    else:
        st.warning("Please enter some text.")