Create model.h5

model.h5

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+# Example model training script
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import numpy as np
+import pickle
+
+# Sample dataset
+texts = ["This is valid", "This is malicious", "Valid text", "Malicious text"]
+labels = [0, 1, 0, 1]  # 0: Valid, 1: Malicious
+
+# Tokenization
+tokenizer = Tokenizer(num_words=1000)
+tokenizer.fit_on_texts(texts)
+sequences = tokenizer.texts_to_sequences(texts)
+padded_sequences = pad_sequences(sequences, maxlen=50)
+
+# Save the tokenizer
+with open("tokenizer.pkl", "wb") as f:
+    pickle.dump(tokenizer, f)
+
+# Model architecture
+model = Sequential([
+    Embedding(input_dim=1000, output_dim=64, input_length=50),
+    LSTM(64, return_sequences=False),
+    Dropout(0.5),
+    Dense(1, activation="sigmoid")
+])
+
+# Compile and train the model
+model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
+model.fit(padded_sequences, np.array(labels), epochs=10)
+
+# Save the model
+model.save("model.h5")