import torch
from unsloth import FastLanguageModel
import pandas as pd
from transformers import TextStreamer
import logging
from typing import List, Dict
import json

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

class NetworkFlowAnalyzer:
    def __init__(self, model_path: str):
        self.logger = setup_logging()
        self.logger.info(f"Loading model from {model_path}")
        
        # Load model and tokenizer
        self.model, self.tokenizer = FastLanguageModel.from_pretrained(
            model_path,
            max_seq_length=2048,
            load_in_4bit=True,
        )
        
        # Enable faster inference
        FastLanguageModel.for_inference(self.model)
        
        # Set up tokenizer for generation
        self.tokenizer.pad_token = self.tokenizer.eos_token
        self.tokenizer.padding_side = "right"
        
    def format_flow_prompt(self, row: pd.Series) -> str:
        """Format a 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_flow(self, row: pd.Series, max_new_tokens: int = 256, stream: bool = False) -> str:
        """Analyze a single network flow"""
        # Prepare input
        prompt = self.format_flow_prompt(row)
        inputs = self.tokenizer(
            prompt, 
            return_tensors="pt",
            truncation=True,
            max_length=2048
        ).to("cuda")
        
        # Set up streamer if requested
        streamer = TextStreamer(self.tokenizer) if stream else None
        
        # Generate prediction
        outputs = self.model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            streamer=streamer,
            use_cache=True
        )
        
        if not stream:
            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return None

    def analyze_batch(self, df: pd.DataFrame, output_file: str = None) -> List[Dict]:
        """Analyze a batch of network flows"""
        results = []
        
        for idx, row in df.iterrows():
            self.logger.info(f"Analyzing flow {idx+1}/{len(df)}")
            
            try:
                analysis = self.analyze_flow(row)
                result = {
                    "flow_id": idx,
                    "source_ip": row["IPV4_SRC_ADDR"],
                    "destination_ip": row["IPV4_DST_ADDR"],
                    "analysis": analysis,
                    "true_label": row.get("Label", "Unknown"),
                    "attack_type": row.get("Attack", "Unknown")
                }
                results.append(result)
                
            except Exception as e:
                self.logger.error(f"Error analyzing flow {idx}: {str(e)}")
                continue
        
        if output_file:
            with open(output_file, 'w') as f:
                json.dump(results, f, indent=2)
        
        return results

def main():
    logger = setup_logging()
    
    # Initialize analyzer
    analyzer = NetworkFlowAnalyzer("cybersec_model_output/checkpoint-4329")
    
    # Load test data
    test_df = pd.read_csv("data/test.csv")
    logger.info(f"Loaded {len(test_df)} test samples")
    
    # Option 1: Stream analysis of a single flow
    logger.info("\nAnalyzing single flow with streaming:")
    analyzer.analyze_flow(test_df.iloc[0], stream=True)
    
    # Option 2: Batch analysis
    logger.info("\nAnalyzing batch of flows:")
    sample_size = min(100, len(test_df))  # Analyze first 100 flows
    results = analyzer.analyze_batch(
        test_df.head(sample_size),
        output_file="analysis_results.json"
    )
    
    # Print some statistics
    logger.info(f"\nAnalyzed {len(results)} flows")
    if "Label" in test_df.columns:
        true_positives = sum(1 for r in results if "malicious" in r["analysis"].lower() 
                           and r["true_label"] == 1)
        logger.info(f"Detected {true_positives} potentially malicious flows")

if __name__ == "__main__":
    main()