Create app.py

app.py

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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 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
+@st.cache_resource
+def load_model(filepath):
+    return tf.keras.models.load_model(filepath)
+
+# Tokenizer Setup
+@st.cache_resource
+def setup_tokenizer():
+    tokenizer = Tokenizer(num_words=5000)
+    # Predefined vocabulary for demonstration purposes; replace with your actual tokenizer setup.
+    tokenizer.fit_on_texts(["example prompt", "malicious attack", "valid input prompt"])
+    return tokenizer
+
+# 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):")
+model = None
+tokenizer = None
+
+if model_path:
+    try:
+        model = load_model(model_path)
+        tokenizer = setup_tokenizer()
+        st.success("Model 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)
+
+    except Exception as e:
+        st.error(f"Error Loading Model: {e}")
+
+# Footer
+st.write("---")
+st.write("Developed for detecting and preventing prompt injection attacks.")