pentest_ai_streamlit.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import json

# Load the model and tokenizer
@st.cache(allow_output_mutation=True)
def load_model():
    model_path = "Canstralian/pentest_ai"
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,  # Use float16 if CUDA is available
        device_map="auto",
        load_in_4bit=False,
        load_in_8bit=True,
        trust_remote_code=True,
    )
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
    return model, tokenizer

# Function to generate text from the model
def generate_text(model, tokenizer, instruction):
    # Check if CUDA is available and send tensors to the appropriate device
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    tokens = tokenizer.encode(instruction, return_tensors='pt').to(device)
    generated_tokens = model.generate(
        tokens,
        max_length=1024,
        top_p=1.0,
        temperature=0.5,
        top_k=50
    )
    return tokenizer.decode(generated_tokens[0], skip_special_tokens=True)

# Load the JSON data (simulated here for simplicity)
@st.cache(allow_output_mutation=True)
def load_json_data():
    json_data = [
        {"name": "Raja Clarke", "email": "consectetuer@yahoo.edu", "country": "Chile", "company": "Urna Nunc Consulting"},
        {"name": "Melissa Hobbs", "email": "massa.non@hotmail.couk", "country": "France", "company": "Gravida Mauris Limited"},
        {"name": "John Doe", "email": "john.doe@example.com", "country": "USA", "company": "Example Corp"},
        {"name": "Jane Smith", "email": "jane.smith@example.org", "country": "Canada", "company": "Innovative Solutions Inc"}
    ]
    return json_data

# Streamlit UI
st.title("Penetration Testing AI Assistant")

# Load model and tokenizer
model, tokenizer = load_model()

# Generate some text based on user input
instruction = st.text_area("Enter your question for the AI assistant:")
if st.button("Generate"):
    if instruction:
        response = generate_text(model, tokenizer, instruction)
        st.subheader("Generated Response:")
        st.write(response)
    else:
        st.warning("Please enter a question to generate a response.")

# Displaying user data from JSON
st.subheader("User Data (from JSON)")
user_data = load_json_data()

# Display user details in a readable format
for user in user_data:
    st.write(f"**Name:** {user['name']}")
    st.write(f"**Email:** {user['email']}")
    st.write(f"**Country:** {user['country']}")
    st.write(f"**Company:** {user['company']}")
    st.write("---")  # Separator