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import streamlit as st
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from lime.lime_text import LimeTextExplainer
import matplotlib.pyplot as plt
# Load model and tokenizer
@st.cache(allow_output_mutation=True)
def load_model_and_tokenizer(model_path, tokenizer_path):
model = tf.keras.models.load_model(model_path)
tokenizer = pd.read_pickle(tokenizer_path)
return model, tokenizer
# Preprocess input for prediction
def preprocess_prompt(prompt, tokenizer, max_length):
sequence = tokenizer.texts_to_sequences([prompt])
padded_sequence = pad_sequences(sequence, maxlen=max_length)
return padded_sequence
# Make predictions
def detect_prompt(prompt, model, tokenizer, max_length):
processed_prompt = preprocess_prompt(prompt, tokenizer, max_length)
prediction = model.predict(processed_prompt)[0][0]
class_label = "Malicious" if prediction >= 0.5 else "Valid"
confidence_score = prediction * 100 if prediction >= 0.5 else (1 - prediction) * 100
return class_label, confidence_score
# Explain predictions using LIME
def lime_explain(prompt, model, tokenizer, max_length):
def predict_fn(prompts):
sequences = tokenizer.texts_to_sequences(prompts)
padded_sequences = pad_sequences(sequences, maxlen=max_length)
predictions = model.predict(padded_sequences)
return np.hstack([1 - predictions, predictions]) # [P(valid), P(malicious)]
explainer = LimeTextExplainer(class_names=["Valid", "Malicious"])
explanation = explainer.explain_instance(prompt, predict_fn, num_features=10)
return explanation
# Set up Streamlit app
st.title("Prompt Injection Detection and Prevention")
st.write("Detect malicious prompts and understand predictions using deep learning and LIME.")
# Load model and tokenizer
model_path = "path/to/your/saved_model"
tokenizer_path = "path/to/your/tokenizer.pkl"
max_length = 100 # Update based on your model
model, tokenizer = load_model_and_tokenizer(model_path, tokenizer_path)
# Input prompt
user_input = st.text_area("Enter your prompt:", height=150)
if st.button("Detect"):
if user_input.strip() == "":
st.error("Please enter a prompt.")
else:
# Prediction
class_label, confidence_score = detect_prompt(user_input, model, tokenizer, max_length)
st.subheader("Detection Result:")
st.write(f"**Class:** {class_label}")
st.write(f"**Confidence Score:** {confidence_score:.2f}%")
# Generate LIME explanation
st.subheader("Explanation:")
explanation = lime_explain(user_input, model, tokenizer, max_length)
fig = explanation.as_pyplot_figure()
st.pyplot(fig)
# Sidebar information
st.sidebar.title("About")
st.sidebar.info(
"""
This app uses a deep learning model to classify prompts as "Malicious" or "Valid."
LIME explanations are provided to interpret the predictions.
"""
)
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