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import streamlit as st |
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import llama_cpp |
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@st.cache_resource |
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def load_model(model_path): |
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return llama_cpp.Llama(model_path=model_path) |
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st.title("CyberSecurity Assistant") |
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model_path = "./llama-3-3b-cybersecurity-quantized.gguf" |
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temperature = st.sidebar.slider('Temperature', 0.0,2.0,0.2, step=0.1) |
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if model_path: |
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try: |
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llm = load_model(model_path) |
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st.sidebar.success('Model loaded Successfully') |
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except Exception as e: |
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st.sidebar.error(f'Error loading the model: {e}') |
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llm = None |
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else: |
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st.warning("Model path not found") |
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instruction= 'You are a Cybersecurity AI Assistant, will be glad to answer your questions related to Cybersecurity, particularly LLM Security.' |
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if llm: |
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user_input = st.text_input("Your message", "") |
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user_input= f'{instruction} \n\nUser: {user_input}\nAI' |
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if user_input: |
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with st.spinner("Generating response..."): |
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try: |
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response = llm(user_input, temperature=temperature, max_tokens=256) |
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st.write(f"**Cybersecurity Assistant:** {response['choices'][0]['text'].strip()}") |
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except Exception as e: |
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st.error(f"Error in Generation: {e}") |
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