Rename pentesting_assistant.py to chat_ai.py

chat_ai.py

@@ -0,0 +1,40 @@
+from transformers import AlbertTokenizer, AlbertForSequenceClassification
+from datasets import load_dataset
+from transformers import Trainer, TrainingArguments
+
+# Load dataset (ganti dengan nama dataset dan versi kamu)
+dataset = load_dataset('your_username/your_dataset_name', 'your_dataset_version')
+
+# Load tokenizer and model
+tokenizer = AlbertTokenizer.from_pretrained('google/albert-base-v2')
+model = AlbertForSequenceClassification.from_pretrained('google/albert-base-v2')
+
+# Define preprocessing function
+def preprocess_function(examples):
+    return tokenizer(examples['text'], truncation=True, padding='max_length')
+
+# Preprocess dataset
+encoded_dataset = dataset.map(preprocess_function, batched=True)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    per_device_train_batch_size=8,
+    num_train_epochs=3,
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+)
+
+# Create trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=encoded_dataset['train'],
+    eval_dataset=encoded_dataset['validation'],
+)
+
+# Train model
+trainer.train()
+
+# Save trained model
+trainer.save_model('./your_private_albert_model')

pentesting_assistant.py

@@ -1,18 +0,0 @@
-from transformers import pipeline, AlbertTokenizer, AlbertForSequenceClassification
-
-# Load model
-model = AlbertForSequenceClassification.from_pretrained('./pentesting_albert_model')
-tokenizer = AlbertTokenizer.from_pretrained('./pentesting_albert_model')
-
-# Create text-generation pipeline
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-while True:
-    user_input = input("Kamu: ")
-    if user_input.lower() == "keluar":
-        break
-
-    # Generate response
-    response = generator(user_input, max_length=50, num_return_sequences=1, do_sample=True, top_k=50, top_p=0.95)
-
-    print("Asisten: ", response[0]['generated_text'])