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app.py

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+import streamlit as st
+import tensorflow as tf
+import pickle
+
+# Model ve Vectorizer'ı yükleme
+model = tf.keras.models.load_model("xss_detection_model-3.h5")
+
+with open("vectorizer.pkl", "rb") as file:
+    vectorizer = pickle.load(file)
+
+# Streamlit başlığı
+st.title("XSS Detector")
+
+# Kullanıcı girdisi
+user_input = st.text_area("XSS payload'ınızı buraya girin", height=100)
+
+# Tespit butonu
+if st.button("Tespit Et"):
+    transformed_input = vectorizer.transform([user_input]).toarray()
+    prediction = model.predict(transformed_input)
+
+    # Sonucu ekranda gösterme
+    if prediction[0] > 0.5:
+        st.write("Bu bir XSS payload!")
+    else:
+        st.write("Bu bir XSS payload DEĞİL!")

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:774888e42c2f547fef2cab476fe86ae4ba004441540e3c3bec32543ee395803a
+size 20314872

requirements.txt

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+streamlit
+tensorflow
+pickle-mixin

xss_detection_model-3.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f132cd032e4a7163d1ee98b5db4beb6578cef6e42a95e3b9c7a5160d7069f29c
+size 306960