Update app.py

app.py

@@ -1,113 +1,24 @@
 import gradio as gr
 from transformers import pipeline, Trainer, TrainingArguments, AutoModelForCausalLM, AutoTokenizer
-from typing import List, Dict
-import os
-from datasets import Dataset
+import torch
 
-# Initialize the Hugging Face pipeline (replace with a valid model)
-model_name = "gpt2"  # Example model, replace with your own
+# Initialize model and tokenizer
+model_name = "huggingface/transformer_model"  # Replace with the actual model name
+model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-try:
-    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.
-    """
-    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):
-        return ["Error: History must be a list of dictionaries."]
-    
-    # Prepare messages for the AI
-    messages = [{"role": "system", "content": f"Responding to {prompt}..."}]
-    for val in history:
-        if "user" in val:
-            messages.append({"role": "user", "content": val["user"]})
-        if "assistant" in val:
-            messages.append({"role": "assistant", "content": val["assistant"]})
-
-    # Append the current user prompt
-    messages.append({"role": "user", "content": prompt})
-
-    # Generate a response using the Hugging Face model
-    try:
-        response = generator(messages[-1]["content"], max_length=100, num_return_sequences=1)
-        return [response[0]["generated_text"]]
-    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_file) -> str:
-    """
-    Fine-tunes the model using the uploaded dataset.
-    """
-    try:
-        # 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",
-            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=dataset,
-            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.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)
-    ],
-    title="Blackhat AI Simulator with Live Fine-Tuning",
-    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):
-    if dataset_file is not None:
-        return fine_tune_model(dataset_file)
-    else:
-        return "No dataset uploaded."
-
-# 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")
+# Define Gradio interface
+def upload_and_finetune(file):
+    # Your fine-tuning code here
+    # Example: Load dataset, preprocess, and fine-tune model
+    return f"File {file.name} uploaded successfully!"
+
+# Create Gradio interface with correct parameter
+interface = gr.Interface(
+    fn=upload_and_finetune, 
+    inputs=[gr.File(label="Upload Dataset for Fine-Tuning", file_count="single", type="file")], 
+    outputs="text"
 )
 
-# Launch the interface
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()