app.py

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

# Load the pre-trained model and tokenizer
model_path = "https://huggingface.co/jonaskoenig/topic_classification_04"  # Replace with the path to your saved model
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertForSequenceClassification.from_pretrained(model_path)

# Set up Streamlit app
st.title("Topic Classification App")

# User input for text
user_input = st.text_area("Enter text for topic classification:", "")

# Function to make predictions
def predict_topic(text):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()
    return predicted_class

# Make predictions and display result
if st.button("Predict"):
    if user_input:
        st.info("Making Prediction...")
        prediction = predict_topic(user_input)
        st.success(f"Predicted Topic: {prediction}")
    else:
        st.warning("Please enter some text for prediction.")