Create app.py

app.py

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+
+
+
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import pickle
+
+# Load the trained model and tokenizer
+model = tf.keras.models.load_model("deep_learning_model.h5")
+
+with open("tokenizer.pkl", "rb") as handle:
+    tokenizer = pickle.load(handle)
+
+# Input parameters
+max_length = 100
+
+# Streamlit UI
+st.title("Prompt Injection Detection")
+st.write("Enter a prompt to check whether it is malicious or valid:")
+
+user_input = st.text_area("Input Text", placeholder="Type your input here...")
+
+if st.button("Analyze"):
+    if user_input.strip() == "":
+        st.error("Please enter some text to analyze.")
+    else:
+        # Preprocess user input
+        input_seq = tokenizer.texts_to_sequences([user_input])
+        input_pad = pad_sequences(input_seq, maxlen=max_length)
+
+        # Predict
+        prediction = model.predict(input_pad)[0][0]
+        if prediction >= 0.5:
+            st.error("🚨 The input is classified as *Malicious*.")
+        else:
+            st.success("✅ The input is classified as *Valid*.")