Update app.py

app.py

@@ -1,14 +1,18 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, Trainer, TrainingArguments, AutoModelForCausalLM, AutoTokenizer
 from typing import List, Dict
+import os
 
 # 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
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 try:
-    generator = pipeline("text-generation", model=model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
 except Exception as e:
     raise ValueError(f"Error initializing the model '{model_name}': {e}")
 
+# Function to generate attack scenarios
 def generate_attack(prompt: str, history: List[Dict[str, str]]) -> List[str]:
     """
     Simulates a Blackhat AI scenario by generating attack strategies and potential impacts.
@@ -42,20 +46,83 @@ def generate_attack(prompt: str, history: List[Dict[str, str]]) -> List[str]:
     except Exception as e:
         return [f"Error generating response: {e}"]
 
+# Function for fine-tuning the model with the uploaded dataset
+def fine_tune_model(dataset: str) -> 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
+        train_args = TrainingArguments(
+            output_dir="./results",
+            evaluation_strategy="steps",
+            save_steps=10,
+            per_device_train_batch_size=4,
+            num_train_epochs=1,
+            logging_dir="./logs",
+        )
+
+        trainer = Trainer(
+            model=model,
+            args=train_args,
+            train_dataset=data,
+            tokenizer=tokenizer
+        )
+        
+        trainer.train()
+        model.save_pretrained("./fine_tuned_model")
+        return "Model fine-tuned successfully!"
+    except Exception as e:
+        return f"Error fine-tuning the model: {e}"
+
 # Define the Gradio interface
 demo = gr.Interface(
     fn=generate_attack,
     inputs=[
         gr.Textbox(label="Prompt", placeholder="Enter your simulation prompt here..."),
-        gr.Dataframe(headers=["user", "assistant"], label="Message History", type="array")
+        gr.Dataframe(headers=["user", "assistant"], label="Message History", type="array"),
+        gr.File(label="Upload Dataset for Fine-Tuning", file_count="single", type="file")
+    ],
+    outputs=[
+        gr.Textbox(label="Generated Response"),
+        gr.Textbox(label="Fine-Tuning Status", interactive=False)
     ],
-    outputs=gr.Textbox(label="Generated Response"),
-    title="Blackhat AI Simulator",
+    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."
     )
 )
 
+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)
+    else:
+        return "No dataset uploaded."
+
+# Add a separate fine-tuning section to the interface
+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)
+
 if __name__ == "__main__":
     demo.launch()