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import streamlit as st
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import numpy as np
from lime.lime_text import LimeTextExplainer
import matplotlib.pyplot as plt
# Streamlit Title
st.title("Prompt Injection Detection and Prevention")
st.write("Detect malicious prompts and understand predictions using deep learning and LIME.")
# Cache Data Loading
@st.cache_data
def load_data(filepath):
return pd.read_csv(filepath)
# Cache Model Loading
@st.cache_resource
def load_model(filepath):
return tf.keras.models.load_model(filepath)
# File Upload Section
uploaded_file = st.file_uploader("Upload your dataset (.csv)", type=["csv"])
if uploaded_file is not None:
data = load_data(uploaded_file)
st.write("Dataset Preview:")
st.write(data.head())
# Data Preprocessing
data['label'] = data['label'].replace({'valid': 0, 'malicious': 1})
X = data['input'].values
y = data['label'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Tokenization and Padding
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(X_train)
max_length = 100
X_train_pad = pad_sequences(tokenizer.texts_to_sequences(X_train), maxlen=max_length)
X_test_pad = pad_sequences(tokenizer.texts_to_sequences(X_test), maxlen=max_length)
# Load Deep Learning Model
model_path = st.text_input("Enter the path to your trained model (.h5):")
if model_path:
try:
model = load_model(model_path)
st.success("Model Loaded Successfully!")
# Test Prediction Functionality
def preprocess_prompt(prompt, tokenizer, max_length):
sequence = tokenizer.texts_to_sequences([prompt])
return pad_sequences(sequence, maxlen=max_length)
def detect_prompt(prompt):
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
# User Input for Prompt Detection
st.subheader("Test a Prompt")
user_prompt = st.text_input("Enter a prompt to test:")
if user_prompt:
class_label, confidence_score = detect_prompt(user_prompt)
st.write(f"Predicted Class: **{class_label}**")
st.write(f"Confidence Score: **{confidence_score:.2f}%**")
# LIME Explanation
explainer = LimeTextExplainer(class_names=["Valid", "Malicious"])
def lime_explain(prompt):
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])
explanation = explainer.explain_instance(
prompt,
predict_fn,
num_features=10
)
return explanation
st.subheader("LIME Explanation")
if user_prompt:
explanation = lime_explain(user_prompt)
explanation_as_html = explanation.as_html()
st.components.v1.html(explanation_as_html, height=500)
except Exception as e:
st.error(f"Error Loading Model: {e}")
# Footer
st.write("---")
st.write("Developed for detecting and preventing prompt injection attacks using Streamlit.")
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