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/Minecraft_Query_Wizard'

# Check for GPU availability and set the 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)

# Inject CSS for background image
page_bg_img = '''
<style>
body {
    background-image: url("https://wallpapercave.com/wp/wp5772281.jpg");
    background-size: cover;
}
.block-container {
    background-color: rgba(255, 255, 255, 0.8);
    border-radius: 10px;
    padding: 20px;
}
</style>
'''
st.markdown(page_bg_img, unsafe_allow_html=True)

# Streamlit app layout
st.title("Minecraft Query Wizard")

# 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