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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM
import torch
# Sidebar for user input
st.sidebar.header("Model Configuration")
model_choice = st.sidebar.selectbox("Select a model", [
"CyberAttackDetection",
"text2shellcommands",
"pentest_ai"
])
# Define the model names
model_mapping = {
"CyberAttackDetection": "Canstralian/CyberAttackDetection",
"text2shellcommands": "Canstralian/text2shellcommands",
"pentest_ai": "Canstralian/pentest_ai"
}
model_name = model_mapping.get(model_choice, "Canstralian/CyberAttackDetection")
# Load model and tokenizer on demand
@st.cache_resource
def load_model(model_name):
try:
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
if model_name == "Canstralian/text2shellcommands":
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
else:
model = AutoModelForSequenceClassification.from_pretrained(model_name)
return tokenizer, model
except Exception as e:
st.error(f"Error loading model: {e}")
return None, None
# Load the model and tokenizer
tokenizer, model = load_model(model_name)
# Input text box in the main panel
st.title(f"{model_choice} Model")
user_input = st.text_area("Enter text:")
# Make prediction if user input is provided
if user_input and model and tokenizer:
if model_choice == "text2shellcommands":
# For text2shellcommands model, generate shell commands
inputs = tokenizer(user_input, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model.generate(**inputs)
generated_command = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write(f"Generated Shell Command: {generated_command}")
else:
# For CyberAttackDetection and pentest_ai models, perform classification
inputs = tokenizer(user_input, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=-1).item()
st.write(f"Predicted Class: {predicted_class}")
st.write(f"Logits: {logits}")
else:
st.info("Please enter some text for prediction.")
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