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secure_nlp_engine.py
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secure_nlp_engine.py
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import os
from transformers import T5Tokenizer, T5ForConditionalGeneration
from Crypto.Cipher import AES
import base64
import time
class SecureNLPEngine:
def __init__(self, encryption_key, model_name='t5-small'):
"""Initialize the engine, load model, and set encryption key."""
self.encryption_key = encryption_key
self.tokenizer = T5Tokenizer.from_pretrained(model_name)
self.model = T5ForConditionalGeneration.from_pretrained(model_name)
self.is_training_initialized = False
def encrypt(self, data):
"""Encrypt input data using AES."""
cipher = AES.new(self.encryption_key, AES.MODE_GCM)
ciphertext, tag = cipher.encrypt_and_digest(data.encode())
return base64.b64encode(cipher.nonce + tag + ciphertext).decode()
def decrypt(self, encrypted_data):
"""Decrypt input data using AES."""
encrypted_data = base64.b64decode(encrypted_data)
nonce, tag, ciphertext = encrypted_data[:16], encrypted_data[16:32], encrypted_data[32:]
cipher = AES.new(self.encryption_key, AES.MODE_GCM, nonce=nonce)
return cipher.decrypt_and_verify(ciphertext, tag).decode()
def parse_input(self, user_input):
"""Securely parse input and return shell command."""
decrypted_input = self.decrypt(user_input) # Decrypt input
inputs = self.tokenizer.encode(f"Translate to command: {decrypted_input}", return_tensors='pt')
outputs = self.model.generate(inputs, max_length=50, num_beams=5, early_stopping=True)
command = self.tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
encrypted_command = self.encrypt(command) # Encrypt output
return encrypted_command
def check_for_data(self):
"""Auto-detect new data availability for training."""
if os.path.exists('server_data.csv'):
print("High-quality data detected. Initializing training.")
self.train_model('server_data.csv')
self.is_training_initialized = True
else:
print("No external data detected. Skipping training.")
def train_model(self, dataset):
"""Fine-tune the NLP model using advanced server data."""
# Placeholder for model training code. We can refine this later as needed.
print(f"Training model with dataset: {dataset}")
time.sleep(3) # Simulate training delay
print("Model training completed!")
# Example of using SecureNLPEngine
if __name__ == "__main__":
engine = SecureNLPEngine(encryption_key=b'your_16_byte_key')
# Check for available high-quality data and initiate training
engine.check_for_data()
# Example of processing user input (encrypted for security)
encrypted_input = engine.encrypt("What is the status of the server?")
result = engine.parse_input(encrypted_input)
print(f"Encrypted output command: {result}")