Generative AI
How can you build Age Desktop for intelligent commerce with natural language and effective simultaneous pronunciation?
In this lesson, we travel through the development of an Advanced AI Desktop Agent Agent to run outside of the seams in Google Colab. We designate the interpretation of the nature language, imitate desktop activities such as file activities, browser actions, transaction, and provide an answer connecting by visual nature. By combining the NLP, jobs, and desktop made, we create an accurate and stronger program, allowing us to get debilitating ideas without depending on foreign APIs. Look Full codes here.
import re
import json
import time
import random
import threading
from datetime import datetime
from typing import Dict, List, Any, Tuple
from dataclasses import dataclass, asdict
from enum import Enum
try:
from IPython.display import display, HTML, clear_output
import matplotlib.pyplot as plt
import numpy as np
COLAB_MODE = True
except ImportError:
COLAB_MODE = FalseWe start by taking the important yaton libraries that support data management, logical, and imitations. We put some special tools to conduct the course in partnership in a seamless place. Look Full codes here.
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"
result: str = ""
timestamp: str = ""
execution_time: float = 0.0We describe the formation of our Automation Program. We create an ENUM to install the functions and data work that help us to track each command with its details, the situation and its effectiveness. Look Full codes here.
class VirtualDesktop:
"""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 = {
TaskType.FILE_OPERATION: [
r"(open|create|delete|copy|move|find)s+(file|folder|document)",
r"(save|edit|write)s+.*.(txt|doc|pdf|csv)",
r"(list|show)s+(files|directories)",
r"(download|upload)s+.*"
],
TaskType.BROWSER_ACTION: [
r"(open|visit|go to|navigate)s+.*.(com|org|net)",
r"(search|google|find)s+.*",
r"(click|press|select)s+(button|link)",
r"(fill|enter|type)s+.*"
],
TaskType.SYSTEM_COMMAND: [
r"(check|show)s+(system|cpu|memory|disk)",
r"(run|execute|start)s+program",
r"(restart|shutdown|sleep)",
r"(install|update|configure)s+.*"
],
TaskType.APPLICATION_TASK: [
r"(open|start|launch)s+(browser|editor|email|terminal)",
r"(close|quit|exit)s+.*",
r"(send|compose|reply)s+(email|message)",
r"(edit|modify|change)s+.*"
],
TaskType.WORKFLOW: [
r"(automate|batch|bulk)s+.*",
r"(combine|merge|join)s+.*",
r"(schedule|remind|notify)s+.*",
r"(backup|sync|export)s+.*"
]
}
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 re.search(pattern, command_lower):
confidence = len(re.findall(pattern, command_lower)) * 0.3
if confidence > best_confidence:
best_match = task_type
best_confidence = 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)
if file_match:
params['filename'] = file_match.group()
path_match = re.search(r'/[w/.-]+', command)
if path_match:
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)
if search_match:
params['query'] = search_match.group(1)
elif task_type == TaskType.APPLICATION_TASK:
app_match = re.search(r'(browser|editor|email|terminal|calculator)', command_lower)
if app_match:
params['application'] = app_match.group(1)
return paramsWemitate a visible desktop for applications, file system, and program methods while building the NLP processor. We create rules to identify the user's goals in natural language instructions and produce useful parameters, such as file names, URLs, or application terms. This allows us to destroy the natural language in line with systematic works. Look Full codes here.
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" in command.lower():
filename = params.get('filename', 'unknown.txt')
return f"✓ Opened file: {filename}n📁 File contents loaded in text editor"
elif "create" in command.lower():
filename = params.get('filename', 'new_file.txt')
return f"✓ Created new file: {filename}n📝 File ready for editing"
elif "list" in command.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 "✓ File operation completed successfully"
def execute_browser_action(self, params: Dict[str, str], command: str) -> str:
"""Simulate browser actions"""
if "open" in command.lower() or "visit" in command.lower():
url = 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"
elif "search" in command.lower():
query = params.get('query', 'search term')
return f"🔍 Searching for: '{query}'n✓ Found 1,247 results"
return "✓ Browser action completed"
def execute_system_command(self, params: Dict[str, str], command: str) -> str:
"""Simulate system commands"""
if "check" in command.lower() or "show" in command.lower():
info = self.desktop.get_system_info()
return f"💻 System Status:n" +
f" CPU: {info['cpu_usage']}%n" +
f" Memory: {info['memory_usage']}%n" +
f" Disk: {info['disk_space']}% usedn" +
f" Network: {info['network_status']}"
return "✓ 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" in command.lower():
self.desktop.applications[app]["status"] = "open"
return f"🚀 Launched {app.title()}n✓ Application ready for use"
elif "close" in command.lower():
if app in self.desktop.applications:
self.desktop.applications[app]["status"] = "closed"
return 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):
result += f" {i}. {step} ✓n"
if COLAB_MODE:
time.sleep(0.1)
return result + "✅ Workflow completed successfully!"
class DesktopAgent:
"""Main desktop automation agent class - coordinates all components"""
def __init__(self):
self.desktop = 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.WORKFLOW:
result = self.executor.execute_workflow(params, command)
else:
result = "⚠️ Command type not recognized"
task.status = "completed"
task.result = result
self.stats["tasks_completed"] += 1
except Exception as e:
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 self.task_history:
successful_tasks = sum(1 for t in self.task_history if t.status == "completed")
self.stats["success_rate"] = round((successful_tasks / len(self.task_history)) * 100, 1)
total_time = sum(t.execution_time for t in 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 else []
dashboard = f"""
╭━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╮
│ 🤖 AI DESKTOP AGENT STATUS │
├──────────────────────────────────────────────────────┤
│ 📊 Statistics: │
│ • Tasks Completed: {self.stats['tasks_completed']:<10} │
│ • Success Rate: {self.stats['success_rate']:<10}% │
│ • Avg Exec Time: {self.stats['average_execution_time']:<10}s │
├──────────────────────────────────────────────────────┤
│ 🖥️ Desktop Applications: │
"""
for app, info in self.desktop.applications.items():
status_icon = "🟢" if info["status"] == "open" else "🔴"
dashboard += f"│ {status_icon} {app.title():<12} ({info['status']:<6}) │n"
dashboard += "├──────────────────────────────────────────────────────┤n"
dashboard += "│ 📋 Recent Tasks: │n"
if recent_tasks:
for task in recent_tasks:
status_icon = "✅" if task.status == "completed" else "❌"
dashboard += f"│ {status_icon} {task.timestamp} - {task.type.value:<15} │n"
else:
dashboard += "│ No tasks executed yet │n"
dashboard += "╰━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╯"
return dashboardWe use the employee's work that changes our combined intentions into concrete effects and practical results on the desktop desktop. Then we find everything together in desktopagent, when we process environmental language, issuing jobs, and continuously follow the success, latency, and a live status dashboard. Look Full codes here.
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):
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()
if 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)
while True:
try:
user_input = input("n🤖 Agent> ").strip()
if user_input.lower() in ['quit', 'exit', 'q']:
print("👋 AI Agent shutting down. Goodbye!")
break
elif user_input.lower() in ['status', 'dashboard']:
print(agent.get_status_dashboard())
continue
elif user_input.lower() in ['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
elif not user_input:
continue
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)
except KeyboardInterrupt:
print("nn👋 AI Agent interrupted. Goodbye!")
break
except Exception as e:
print(f"❌ Error: {e}")
if __name__ == "__main__":
agent = run_advanced_demo()
if COLAB_MODE:
print("n🎮 To continue with interactive mode, run:")
print("interactive_mode(agent)")
else:
interactive_mode(agent)We run a short term that processes logical commands, the printed results, and completes live status. We then offer a link connecting where we type environmental language, check status, and then find the answer immediately. Finally, start short and, in Colob, we show how to start working mode with one phone.
In conclusion, we show how ei agent can handle various desktop functions such as the only useful nature. We see how the installation of natural languages is translated into organized activities, which is made of facts of facts, and summarizes the visible dashboard. Through this basis, we put ourselves when we expand the complexity agent, with actual integration, and do Desktop equipment, easily interacted, and easy to use them.
Look Full codes here. Feel free to look our GITHUB page for tutorials, codes and letters of writing. Also, feel free to follow it Sane and don't forget to join ours 100K + ml subreddit Then sign up for Our newspaper.
Asphazzaq is a Markteach Media Inc. According to a View Business and Developer, Asifi is committed to integrating a good social intelligence. His latest attempt is launched by the launch of the chemistrylife plan for an intelligence, MarktechPost, a devastating intimate practice of a machine learning and deep learning issues that are clearly and easily understood. The platform is adhering to more than two million moon visits, indicating its popularity between the audience.
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