How do I build a desktop AI agent that uses natural language commands, interactive simulation and AI?

Import Re
Download json
import time
Import random
Im threading
Import datetime from datetime
Import Dict, list, any, Tuple
Import dataclasses from asdict
Import Enum


try:
 Clear_output, HTML and IPython.display
 Matplotlib.pyplot can be imported as a plt
 Import numpy as an np
 COLAB_MODE is True
If you get an ImportError, it's because your import is not working.
 COLAB_MODE=False

class TaskType(Enum).
   FILE_OPERATION = "file_operation"
   BROWSER_ACTION = "browser_action"
   SYSTEM_COMMAND = "system_command"
   APPLICATION_TASK = "application_task"
 Workflow = "workflow"


@dataclass
CLASS TASK:
 Id: str
 type: TaskType
 Command: str
 Status: str = "pending"
 "" ""
   timestamp: str = ""
 Execution_time: floating = 0.0

Class VirtualDesktopReturn
   """Simulates a desktop environment with applications and file system"""
  
   def __init__(self):
       self.applications = {
           "browser": {"status": "closed", "tabs": [], "current_url": ""},
           "file_manager": {"status": "closed", "current_path": "/home/user"},
           "text_editor": {"status": "closed", "current_file": "", "content": ""},
           "email": {"status": "closed", "unread": 3, "inbox": []},
           "terminal": {"status": "closed", "history": []}
       }
      
       self.file_system = {
           "/home/user/": {
               "documents/": {
                   "report.txt": "Important quarterly report content...",
                   "notes.md": "# Meeting Notesn- Project updaten- Budget review"
               },
               "downloads/": {
                   "data.csv": "name,age,citynJohn,25,NYCnJane,30,LA",
                   "image.jpg": "[Binary image data]"
               },
               "desktop/": {}
           }
       }
      
       self.screen_state = {
           "active_window": None,
           "mouse_position": (0, 0),
           "clipboard": ""
       }
  
   def get_system_info(self) -> Dict:
       return {
           "cpu_usage": random.randint(5, 25),
           "memory_usage": random.randint(30, 60),
           "disk_space": random.randint(60, 90),
           "network_status": "connected",
           "uptime": "2 hours 15 minutes"
       }


Class NLPProcessor
   """Processes natural language commands and extracts intents"""
  
   def __init__(self):
       self.intent_patterns = start)s+program",
               r"(restart
  
   def extract_intent(self, command: str) -> Tuple[TaskType, float]:
       """Extract task type and confidence from natural language command"""
       command_lower = command.lower()
       best_match = TaskType.SYSTEM_COMMAND
       best_confidence = 0.0
      
       for task_type, patterns in self.intent_patterns.items():
 For pattern in patterns
 If you re.search the pattern, then command_lower:
                   confidence = len(re.findall(pattern, command_lower)) * 0.3
                   if confidence > best_confidence:
                       best_match = task_type
 "confidence" = best_confidence
      
       return best_match, min(best_confidence, 1.0)
  
   def extract_parameters(self, command: str, task_type: TaskType) -> Dict[str, str]:
       """Extract parameters from command based on task type"""
       params = {}
       command_lower = command.lower()
      
       if task_type == TaskType.FILE_OPERATION:
           file_match = re.search(r'[w/.-]+.w+', command)
 File_match:
 Check out the params['filename'] = file_match.group()
          
           path_match = re.search(r'/[w/.-]+', command)
 If the path is not matched, then:
 Params['path'] = path_match.group()
      
       elif task_type == TaskType.BROWSER_ACTION:
           url_match = re.search(r'https?://[w.-]+|[w.-]+.(com|org|net|edu)', command)
 If url_match
 params['url'] = url_match.group()
          
           search_match = re.search(r'(?:search|find|google)s+["']?([^"']+)["']?', command_lower)
 Search_match
 амами['query'] = search_match.group(1)
      
       elif task_type == TaskType.APPLICATION_TASK:
           app_match = re.search(r'(browser|editor|email|terminal|calculator)', command_lower)
 App_match
 params['application'] = app_match.group(1)
      
 Return params

class TaskExecutor:
   """Executes tasks on the virtual desktop"""
  
   def __init__(self, desktop: VirtualDesktop):
       self.desktop = desktop
       self.execution_log = []
  
   def execute_file_operation(self, params: Dict[str, str], command: str) -> str:
       """Simulate file operations"""
 If "open" Lower():
           filename = params.get('filename', 'unknown.txt')
 Return f"✓ Opened file: {filename}n📁 File contents loaded in text editor"
      
 The elif "create" Lower():
           filename = params.get('filename', 'new_file.txt')
 Return f"✓ Created new file: {filename}n📝 File ready for editing"
      
 The elif "list" Lower():
           files = list(self.desktop.file_system["/home/user/documents/"].keys())
 Return f"📂 Files found:n" + "n".join([f"  • {f}" for f in files])
      
 Return to the Homepage "✓ File operation completed successfully"
  
   def execute_browser_action(self, params: Dict[str, str], command: str) -> str:
       """Simulate browser actions"""
 If "open" Lower() You can also find out more about "visit" Lower():
 The url is params.get() ('url,' "example.com")
           self.desktop.applications["browser"]["current_url"] " = url
           self.desktop.applications["browser"]["status"] = "open"
 Return f"🌐 Navigated to: {url}n✓ Page loaded successfully"
      
 The elif "search" Lower():
 Params.get ('query,''search term")
 Return f"🔍 Searching for: '{query}'n✓ Found 1,247 results"
      
 Return to the Homepage "✓ Browser action completed"
  
   def execute_system_command(self, params: Dict[str, str], command: str) -> str:
       """Simulate system commands"""
 If "check" Lower() You can also find out more about "show" Lower():
           info = self.desktop.get_system_info()
 Return f"💻 System Status:n" + 
 The f"  CPU: {info['cpu_usage']}%n" + 
 The f"  Memory: {info['memory_usage']}%n" + 
 The s."  Disk: {info['disk_space']}% usedn" + 
 The f"  Network: {info['network_status']}"
      
 Return to the Homepage "✓ System command executed"
  
   def execute_application_task(self, params: Dict[str, str], command: str) -> str:
       """Simulate application tasks"""
       app = params.get('application', 'unknown')
      
 If "open" Lower():
           self.desktop.applications[app]["status"] = "open"
 Return f"🚀 Launched {app.title()}n✓ Application ready for use"
      
 The elif "close" Lower():
           if app in self.desktop.applications:
               self.desktop.applications[app]["status"] = "closed"
 The return of f"❌ Closed {app.title()}"
      
 Return f"✓ {app.title()} task completed"
  
   def execute_workflow(self, params: Dict[str, str], command: str) -> str:
       """Simulate complex workflow execution"""
 Steps = [
           "Analyzing workflow requirements...",
           "Preparing automation steps...",
           "Executing batch operations...",
           "Validating results...",
           "Generating report..."
       ]
      
 Result = "🔄 Workflow Execution:n"
       for i, step in enumerate(steps, 1):
 ""+=f"  {i}. {step} ✓n"
 If COLAB_MODE is:
               time.sleep(0.1) 
      
 Return result = "✅ Workflow completed successfully!"


Class DesktopAgent
   """Main desktop automation agent class - coordinates all components"""
  
   def __init__(self):
 VirtualDesktop()
 Self.nlp = NLPProcessor()
       self.executor = TaskExecutor(self.desktop)
       self.task_history = []
 Self-active = True
       self.stats = {
           "tasks_completed": 0,
           "success_rate": 100.0,
           "average_execution_time": 0.0
       }
  
   def process_command(self, command: str) -> Task:
       """Process a natural language command and execute it"""
       start_time = time.time()
      
       task_id = f"task_{len(self.task_history) + 1:04d}"
       task_type, confidence = self.nlp.extract_intent(command)
      
 Task (= task)
           id=task_id,
           type=task_type,
           command=command,
           timestamp=datetime.now().strftime("%H:%M:%S")
       )
      
       try:
           params = self.nlp.extract_parameters(command, task_type)
          
           if task_type == TaskType.FILE_OPERATION:
               result = self.executor.execute_file_operation(params, command)
           elif task_type == TaskType.BROWSER_ACTION:
               result = self.executor.execute_browser_action(params, command)
           elif task_type == TaskType.SYSTEM_COMMAND:
               result = self.executor.execute_system_command(params, command)
           elif task_type == TaskType.APPLICATION_TASK:
               result = self.executor.execute_application_task(params, command)
 Elif task_type = TaskType.
 Result = self.executor.execute_workflow(params, command)
           else:
               result = "⚠️ Command type not recognized"
          
           task.status = "completed"
           task.result = result
           self.stats["tasks_completed"] += 1
          
 Take e. as an example:
           task.status = "failed"
           task.result = f"❌ Error: {str(e)}"
      
       task.execution_time = round(time.time() - start_time, 3)
       self.task_history.append(task)
       self.update_stats()
      
       return task
  
   def update_stats(self):
       """Update agent statistics"""
 If task_history.
 If t.status is equal to, then successful_tasks will be sum(1 in task_history for t). "completed")
           self.stats["success_rate"] = round((successful_tasks / len(self.task_history)) * 100, 1)
          
 total_time = sum (t.execution_time of t for self.task_history).
           self.stats["average_execution_time"] = round(total_time / len(self.task_history), 3)
  
   def get_status_dashboard(self) -> str:
       """Generate a status dashboard"""
       recent_tasks = self.task_history[-5:] If self.task_history then                 AI DESKTOP AGENT STATUS             []
      
       dashboard = f"""
╭━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╮
│                🤖 AI DESKTOP AGENT STATUS            │
├──────────────────────────────────────────────────────┤
│ 📊 Statistics:                                       │
│   • Tasks Completed: {self.stats['tasks_completed']:

def run_advanced_demo():
   """Run an advanced interactive demo of the AI Desktop Agent"""
  
   print("🚀 Initializing Advanced AI Desktop Automation Agent...")
   time.sleep(1)
  
 Agent = DesktopAgent()
  
   print("n" + "="*60)
   print("🤖 AI DESKTOP AUTOMATION AGENT - ADVANCED TUTORIAL")
   print("="*60)
   print("A sophisticated AI agent that understands natural language")
   print("commands and automates desktop tasks in a simulated environment.")
   print("n💡 Try these example commands:")
   print("  • 'open the browser and go to github.com'")
   print("  • 'create a new file called report.txt'")
   print("  • 'check system performance'")
   print("  • 'show me the files in documents folder'")
   print("  • 'automate email processing workflow'")
  
   demo_commands = [
       "check system status and show CPU usage",
       "open browser and navigate to github.com",
       "create a new file called meeting_notes.txt",
       "list all files in the documents directory",
       "launch text editor application",
       "automate data backup workflow"
   ]
  
   print(f"n🎯 Running {len(demo_commands)} demonstration commands...n")
  
   for i, command in enumerate(demo_commands, 1):
 You can also print(f"[{i}/{len(demo_commands)}] Command: '{command}'")
       print("-" * 50)
      
       task = agent.process_command(command)
      
       print(f"Task ID: {task.id}")
       print(f"Type: {task.type.value}")
       print(f"Status: {task.status}")
       print(f"Execution Time: {task.execution_time}s")
       print(f"Result:n{task.result}")
       print()
      
 COLAB_MODE
           time.sleep(0.5) 
  
   print("n" + "="*60)
   print("📊 FINAL AGENT STATUS")
   print("="*60)
   print(agent.get_status_dashboard())
  
 Return Agent


def interactive_mode(agent):
   """Run interactive mode for user input"""
   print("n🎮 INTERACTIVE MODE ACTIVATED")
   print("Type your commands below (type 'quit' to exit, 'status' for dashboard):")
   print("-" * 60)
  
 It is True
       try:
           user_input = input("n🤖 Agent> ").strip()
          
 If user_input.lower() You can also find out more about the following: ['quit', 'exit', 'q']:
               print("👋 AI Agent shutting down. Goodbye!")
 Break the Rules
          
 User_input.lower() You can also find out more about the following: ['status', 'dashboard']:
               print(agent.get_status_dashboard())
 Continue reading
          
 User_input.lower() You can also find out more about the following: ['help', '?']:
               print("📚 Available commands:")
               print("  • Any natural language command")
               print("  • 'status' - Show agent dashboard")
               print("  • 'help' - Show this help")
               print("  • 'quit' - Exit AI Agent")
 Continue reading
          
 If user_input is not elif:
 Continue reading
          
           print(f"Processing: '{user_input}'...")
           task = agent.process_command(user_input)
          
           print(f"n✨ Task {task.id} [{task.type.value}] - {task.status}")
           print(task.result)
          
 Other than KeyboardInterrupt
           print("nn👋 AI Agent interrupted. Goodbye!")
 Breaking News
 Exception to the rule:
           print(f"❌ Error: {e}")




If __name__ is equal to "__main__":
   agent = run_advanced_demo()
  
 If COLAB_MODE is:
       print("n🎮 To continue with interactive mode, run:")
       print("interactive_mode(agent)")
   else:
       interactive_mode(agent)