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Update app.py
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app.py
CHANGED
@@ -12,6 +12,22 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = AutoTokenizer.from_pretrained(model_repo_path)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_repo_path).to(device)
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# Streamlit app layout
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st.title("Minecraft Query Wizard")
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@@ -28,10 +44,3 @@ if st.button("Get Answer"):
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# Generate the answer
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outputs = model.generate(inputs['input_ids'], max_length=150, num_beams=4, early_stopping=True)
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# Decode the generated answer
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
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st.subheader("Answer")
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st.write(answer)
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except Exception as e:
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st.error(f"Error during question answering: {e}")
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else:
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st.warning("Please enter a question to get an answer.")
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tokenizer = AutoTokenizer.from_pretrained(model_repo_path)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_repo_path).to(device)
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# Inject CSS for background image
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page_bg_img = '''
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<style>
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body {
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background-image: url("https://wallpapercave.com/wp/wp5772281.jpg");
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background-size: cover;
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}
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.block-container {
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background-color: rgba(255, 255, 255, 0.8);
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border-radius: 10px;
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padding: 20px;
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}
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</style>
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'''
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st.markdown(page_bg_img, unsafe_allow_html=True)
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# Streamlit app layout
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st.title("Minecraft Query Wizard")
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# Generate the answer
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outputs = model.generate(inputs['input_ids'], max_length=150, num_beams=4, early_stopping=True)
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# Decode the generated answer
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