cli_scanner.py

import sys
import asyncio
import os
import ml_engine

def run_cli_scan():
    if len(sys.argv) < 3 or sys.argv[1] != "scan":
        print("Usage: python cli_scanner.py scan <domain>")
        sys.exit(1)
        
    domain = sys.argv[2]
    
    # Check if the model exists, if not train it
    model_path = "s3_model.joblib"
    if not os.path.exists(model_path):
        print("[+] Training ML model for the first time...")
        ml_engine.train(model_path)
    else:
        print("[+] ML model found.")
        
    print(f"\n[+] Starting ML-powered scan for domain: {domain}")
    from dlp_scanner import S3DLPAuditor
    
    class MockWebSocket:
        async def send_json(self, data):
            if data["type"] == "finding":
                f = data["data"]
                print(f"  [!] SENSITIVE FILE FOUND: {f['file_name']} (Reason: {f['trigger_reason']}) -> {f['full_url']}")
            elif data["type"] == "progress":
                print(f"  ... scanned {data['stats']['scanned']} files ...")
            elif data["type"] == "error":
                print(f"  [X] ERROR: {data['message']}")
            elif data["type"] == "status":
                 print(f"  [*] {data['message']}")
            elif data["type"] == "finished":
                print(f"\n[+] Scan Complete! Scanned {data['stats']['scanned']} files. Found {data['stats']['flagged_high_risk']} sensitive files.")
            elif data["type"] == "info":
                 print(f"  [i] {data['message']}")

    async def run_scan():
        auditor = S3DLPAuditor(bucket_name=domain)
        await auditor.audit_bucket(MockWebSocket())

    asyncio.run(run_scan())

if __name__ == "__main__":
    run_cli_scan()