Update app.py

app.py

@@ -2,9 +2,10 @@ import gradio as gr
 from transformers import pipeline, Trainer, TrainingArguments, AutoModelForCausalLM, AutoTokenizer
 from typing import List, Dict
 import os
+from datasets import Dataset
 
-# Initialize the Hugging Face pipeline (make sure to replace with your model name)
-model_name = "your_huggingface_model_name"  # Ensure to use a valid model
+# Initialize the Hugging Face pipeline (replace with a valid model)
+model_name = "gpt2"  # Example model, replace with your own
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 try:
     model = AutoModelForCausalLM.from_pretrained(model_name)
@@ -16,13 +17,7 @@ except Exception as e:
 def generate_attack(prompt: str, history: List[Dict[str, str]]) -> List[str]:
     """
     Simulates a Blackhat AI scenario by generating attack strategies and potential impacts.
-    Args:
-        prompt (str): The user's input to the simulator.
-        history (List[Dict]): The user's message history with timestamps.
-    Returns:
-        List[str]: A list of attack responses from the AI.
     """
-    # Validate inputs
     if not prompt.strip():
         return ["Error: Prompt cannot be empty."]
     if not isinstance(history, list) or not all(isinstance(h, dict) for h in history):
@@ -47,20 +42,20 @@ def generate_attack(prompt: str, history: List[Dict[str, str]]) -> List[str]:
         return [f"Error generating response: {e}"]
 
 # Function for fine-tuning the model with the uploaded dataset
-def fine_tune_model(dataset: str) -> str:
+def fine_tune_model(dataset_file) -> str:
     """
     Fine-tunes the model using the uploaded dataset.
-    Args:
-        dataset (str): The path to the dataset for fine-tuning.
-    Returns:
-        str: A message indicating whether fine-tuning was successful or failed.
     """
     try:
-        # Process the dataset (dummy processing for illustration)
-        with open(dataset, "r") as file:
-            data = file.readlines()
-
-        # Simulate fine-tuning with the provided dataset
+        # Process the dataset
+        dataset_path = os.path.join("uploads", dataset_file.name)
+        with open(dataset_path, "wb") as f:
+            f.write(dataset_file.read())
+        
+        # Load the dataset (make sure it's in the right format)
+        dataset = Dataset.from_text(dataset_path)
+        
+        # Fine-tune the model (dummy training example for illustration)
         train_args = TrainingArguments(
             output_dir="./results",
             evaluation_strategy="steps",
@@ -69,11 +64,11 @@ def fine_tune_model(dataset: str) -> str:
             num_train_epochs=1,
             logging_dir="./logs",
         )
-
+        
         trainer = Trainer(
             model=model,
             args=train_args,
-            train_dataset=data,
+            train_dataset=dataset,
             tokenizer=tokenizer
         )
         
@@ -96,33 +91,23 @@ demo = gr.Interface(
         gr.Textbox(label="Fine-Tuning Status", interactive=False)
     ],
     title="Blackhat AI Simulator with Live Fine-Tuning",
-    description=(
-        "This simulator generates adversarial scenarios, analyzes attack vectors, "
-        "and provides ethical countermeasures. Use responsibly for cybersecurity training and awareness."
-    )
+    description="Generate adversarial scenarios and fine-tune the model with custom datasets."
 )
 
+# Event handler for fine-tuning after dataset upload
 def handle_fine_tuning(dataset_file):
-    """
-    This function is used to trigger the fine-tuning process after file upload.
-    """
     if dataset_file is not None:
-        dataset_path = os.path.join("uploads", dataset_file.name)
-        with open(dataset_path, "wb") as f:
-            f.write(dataset_file.read())
-        return fine_tune_model(dataset_path)
+        return fine_tune_model(dataset_file)
     else:
         return "No dataset uploaded."
 
-# Add a separate fine-tuning section to the interface
+# Add a button to trigger fine-tuning manually
 demo.add_component(
     gr.Button("Fine-Tune Model", variant="primary", elem_id="fine-tune-btn"),
     gr.File(label="Upload Dataset for Fine-Tuning", file_count="single", type="file"),
     outputs=gr.Textbox(label="Fine-Tuning Status")
 )
 
-# Bind the fine-tuning button
-demo.interactive(fn=handle_fine_tuning)
-
+# Launch the interface
 if __name__ == "__main__":
     demo.launch()