Canstralian
commited on
Create pentest_ai_streamlit.py
Browse files- pentest_ai_streamlit.py +49 -0
pentest_ai_streamlit.py
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import streamlit as st
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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# Load the model and tokenizer from Hugging Face
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MODEL_NAME = "Canstralian/pentest_ai"
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@st.cache_resource
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def load_model():
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
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return pipeline("text-generation", model=model, tokenizer=tokenizer)
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# Load the pentest_ai model
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pentest_ai = load_model()
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# Streamlit interface setup
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st.title("Pentest AI Assistant")
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st.write("This tool allows you to ask penetration testing and cybersecurity-related queries, and it will generate AI-powered suggestions or commands.")
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# Text input for user's question
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user_input = st.text_area("Enter your question or command:")
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# Button to trigger generation
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if st.button("Generate Response"):
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if user_input.strip() == "":
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st.error("Please enter a valid input.")
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else:
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# Generate response using the model
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with st.spinner("Generating response..."):
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response = pentest_ai(user_input, max_length=150, num_return_sequences=1)[0]['generated_text']
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# Display the model's response
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st.subheader("AI Response:")
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st.write(response)
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# Add an example button to help users see a sample
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if st.button("Show Example"):
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example_query = "How do I scan a network for open ports?"
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with st.spinner("Generating response for example query..."):
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example_response = pentest_ai(example_query, max_length=150, num_return_sequences=1)[0]['generated_text']
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st.subheader("Example Query:")
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st.write(example_query)
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st.subheader("AI Response:")
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st.write(example_response)
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# Instructions for the user
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st.info("Note: This AI model provides general advice. Always ensure you're testing on systems you have permission to, and follow legal and ethical guidelines.")
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