Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Replace with your Hugging Face model repository path
+model_repo_path = 'facebook/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)
+                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.")