demo.py

import pandas as pd
from unsloth import FastLanguageModel
from transformers import TextStreamer
import torch
import random
import logging

def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s'
    )
    return logging.getLogger(__name__)

def print_separator(title="", char="=", length=80):
    """Print a separator with optional title"""
    if title:
        side_length = (length - len(title) - 2) // 2
        print(char * side_length + f" {title} " + char * side_length)
    else:
        print(char * length)

def format_flow_prompt(row):
    """Format a single network flow into a prompt"""
    flow_text = f"""Network Flow Description:
Source: {row['IPV4_SRC_ADDR']} (Port: {row['L4_SRC_PORT']})
Destination: {row['IPV4_DST_ADDR']} (Port: {row['L4_DST_PORT']})
Protocol Information:
- Protocol ID: {row['PROTOCOL']}
- Layer 7 Protocol: {row['L7_PROTO']}
- TCP Flags: {row['TCP_FLAGS']}
Traffic Metrics:
- Bytes: {row['IN_BYTES']} inbound, {row['OUT_BYTES']} outbound
- Packets: {row['IN_PKTS']} inbound, {row['OUT_PKTS']} outbound
- Duration: {row['FLOW_DURATION_MILLISECONDS']} milliseconds"""

    # Format in LLaMA-3 style
    return f"""<|begin_of_text|><|start_header_id|>user<|end_header_id|>
Analyze this network flow for potential security threats:

{flow_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""

def analyze_single_flow(model_path="cybersec_model_output/checkpoint-4329", 
                       test_file="data/test.csv", 
                       index=None,
                       attack_type=None):
    """Analyze a single network flow and show the model's complete response"""
    logger = setup_logging()
    
    print_separator("LOADING DATA AND MODEL", "=")
    # Load test data
    logger.info(f"Loading test data from {test_file}")
    test_df = pd.read_csv(test_file)
    
    # Select sample based on criteria
    if attack_type:
        attack_samples = test_df[test_df['Attack'].str.lower() == attack_type.lower()]
        if len(attack_samples) == 0:
            raise ValueError(f"No samples found for attack type: {attack_type}")
        sample = attack_samples.iloc[random.randint(0, len(attack_samples)-1)]
    elif index is not None:
        sample = test_df.iloc[index]
    else:
        sample = test_df.iloc[random.randint(0, len(test_df)-1)]
    
    # Load model
    logger.info(f"Loading model from {model_path}")
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_path,
        max_seq_length=2048,
        load_in_4bit=True,
    )
    
    # Set up tokenizer
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side = "right"
    FastLanguageModel.for_inference(model)
    
    print_separator("SAMPLE INFORMATION", "=")
    logger.info(f"Selected flow index: {sample.name}")
    logger.info(f"True label: {sample['Attack']}")
    
    # Prepare input
    prompt = format_flow_prompt(sample)
    inputs = tokenizer(
        prompt,
        return_tensors="pt",
        truncation=True,
        max_length=2048
    ).to("cuda")
    
    # Set up streamer for real-time output
    streamer = TextStreamer(tokenizer)
    
    with open("model_output.txt", "w") as f:
        # Write separators and metadata
        f.write("=" * 80 + "\n")
        f.write(f"NETWORK FLOW ANALYSIS\n")
        f.write("=" * 80 + "\n\n")
        
        f.write("-" * 80 + "\n")
        f.write("METADATA\n")
        f.write("-" * 80 + "\n")
        f.write(f"Flow Index: {sample.name}\n")
        f.write(f"True Label: {sample['Attack']}\n\n")
        
        f.write("-" * 80 + "\n")
        f.write("INPUT PROMPT\n")
        f.write("-" * 80 + "\n")
        f.write(f"{prompt}\n\n")
        
        print_separator("MODEL OUTPUT", "=")
        logger.info("Generating analysis...")
        
        # Generate and capture output
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            streamer=streamer,
            use_cache=True
        )
        
        # Write the complete output
        f.write("-" * 80 + "\n")
        f.write("COMPLETE OUTPUT (including special tokens)\n")
        f.write("-" * 80 + "\n")
        full_output = tokenizer.decode(outputs[0], skip_special_tokens=False)
        f.write(f"{full_output}\n\n")
        
        # Write cleaned output
        f.write("-" * 80 + "\n")
        f.write("CLEANED OUTPUT (without special tokens)\n")
        f.write("-" * 80 + "\n")
        cleaned_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
        f.write(cleaned_output)
        
        f.write("\n" + "=" * 80 + "\n")
    
    print_separator()
    logger.info("Output saved to model_output.txt")
    print_separator()

def main():
    # Example usage:
    print_separator("STARTING ANALYSIS", "=")
    
    # Choose one of these options:
    
    # 1. Random sample
    analyze_single_flow()
    
    # 2. Specific attack type
    # analyze_single_flow(attack_type="ddos")
    
    # 3. Specific index
    # analyze_single_flow(index=42)
    
    print_separator("ANALYSIS COMPLETE", "=")

if __name__ == "__main__":
    main()