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| import streamlit as st | |
| import tensorflow as tf | |
| from tensorflow.keras.preprocessing.text import Tokenizer | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| import numpy as np | |
| from lime.lime_text import LimeTextExplainer | |
| import pickle | |
| import matplotlib.pyplot as plt | |
| # Streamlit Title | |
| st.title("Prompt Injection Detection and Prevention") | |
| st.write("Classify prompts as malicious or valid and understand predictions using LIME.") | |
| # Cache Model Loading | |
| def load_model(filepath): | |
| return tf.keras.models.load_model(filepath) | |
| # Load Tokenizer Function | |
| def load_tokenizer(filepath): | |
| try: | |
| with open(filepath, 'rb') as handle: | |
| tokenizer = pickle.load(handle) | |
| return tokenizer | |
| except Exception as e: | |
| st.error(f"Error loading tokenizer: {e}") | |
| return None | |
| # Preprocessing Function | |
| def preprocess_prompt(prompt, tokenizer, max_length=100): | |
| sequence = tokenizer.texts_to_sequences([prompt]) | |
| return pad_sequences(sequence, maxlen=max_length) | |
| # Prediction Function | |
| def detect_prompt(prompt, tokenizer, model): | |
| processed_prompt = preprocess_prompt(prompt, tokenizer) | |
| 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 | |
| # LIME Explanation | |
| def lime_explain(prompt, model, tokenizer, max_length=100): | |
| explainer = LimeTextExplainer(class_names=["Valid", "Malicious"]) | |
| 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 | |
| # Load Model Section | |
| st.subheader("Load Your Trained Model") | |
| model_path = st.text_input("Enter the path to your trained model (.h5):") | |
| tokenizer_path = st.text_input("Enter the path to your tokenizer file (.pickle):") | |
| model = None | |
| tokenizer = None | |
| if model_path and tokenizer_path: | |
| try: | |
| model = load_model(model_path) | |
| tokenizer = load_tokenizer(tokenizer_path) | |
| if model and tokenizer: | |
| st.success("Model and Tokenizer Loaded Successfully!") | |
| # User Prompt Input | |
| st.subheader("Classify Your Prompt") | |
| user_prompt = st.text_input("Enter a prompt to classify:") | |
| if user_prompt: | |
| class_label, confidence_score = detect_prompt(user_prompt, tokenizer, model) | |
| st.write(f"Predicted Class: **{class_label}**") | |
| st.write(f"Confidence Score: **{confidence_score:.2f}%**") | |
| # LIME Explanation | |
| st.subheader("LIME Explanation") | |
| explanation = lime_explain(user_prompt, model, tokenizer) | |
| explanation_as_html = explanation.as_html() | |
| st.components.v1.html(explanation_as_html, height=500) | |
| else: | |
| st.error("Failed to load model or tokenizer.") | |
| except Exception as e: | |
| st.error(f"Error Loading Model or Tokenizer: {e}") | |
| # Footer | |
| st.write("---") | |
| st.write("Developed for detecting and preventing prompt injection attacks.") | |