app.py

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Replace with your Hugging Face model repository path
model_repo_path = 'Muh113/Bart_Large'

# Check for GPU availability and set device accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_repo_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_repo_path).to(device)

# Streamlit app layout
st.title("Minecraft Question Answering App")

# User input
question_input = st.text_area("Enter a Minecraft-related question", height=150)

# Answer the question
if st.button("Get Answer"):
    if question_input:
        with st.spinner("Generating answer..."):
            try:
                # Tokenize the input question
                inputs = tokenizer(question_input, return_tensors="pt", truncation=True, max_length=116).to(device)
                # Generate the answer
                outputs = model.generate(inputs['input_ids'], max_length=150, num_beams=4, early_stopping=True)
                # Decode the generated answer
                answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
                st.subheader("Answer")
                st.write(answer)
            except Exception as e:
                st.error(f"Error during question answering: {e}")
    else:
        st.warning("Please enter a question to get an answer.")