Introduction
In this tutorial, you'll learn how to create a basic task automation system using AI that can perform simple actions like ordering food or booking rides. This builds upon the concept demonstrated by Google's Gemini AI, which can now automate tasks on smartphones. We'll create a simplified version that demonstrates the core principles of AI-powered task automation using Python and web APIs.
Prerequisites
- Basic Python knowledge
- Python 3.7 or higher installed
- Access to a smartphone with internet connectivity
- Basic understanding of REST APIs
- Development environment (IDE or text editor)
Step-by-step instructions
Step 1: Set up your development environment
Install required Python packages
First, we need to install the necessary Python libraries for our automation system. Open your terminal or command prompt and run:
pip install requests python-dotenvThis installs the requests library for making HTTP requests and python-dotenv for managing environment variables.
Step 2: Create your project structure
Initialize your project directory
Create a new directory for your project and set up the following structure:
task_automation/
├── main.py
├── config.py
├── tasks/
│ ├── __init__.py
│ ├── food_ordering.py
│ └── ride_booking.py
└── .envThis structure organizes our code logically, separating concerns into different modules.
Step 3: Configure environment variables
Create your .env file
Create a .env file in your project root with placeholder API keys:
FOOD_API_KEY=your_food_api_key_here
RIDE_API_KEY=your_ride_api_key_here
USER_LOCATION=40.7128,-74.0060This approach keeps sensitive information out of your code and makes it easy to switch between different environments.
Step 4: Implement basic task automation framework
Create the main automation logic
In main.py, implement the core automation logic:
import os
from dotenv import load_dotenv
from tasks.food_ordering import order_food
from tasks.ride_booking import book_ride
load_dotenv()
def main():
print("AI Task Automation System")
print("========================")
# Simulate user input
task = input("What would you like me to do? (food/ride): ").lower()
if task == "food":
order_food()
elif task == "ride":
book_ride()
else:
print("I don't understand that task.")
if __name__ == "__main__":
main()This sets up the main entry point that will route user requests to appropriate automation functions.
Step 5: Implement food ordering automation
Create the food ordering module
In tasks/food_ordering.py, create the food ordering automation:
import requests
import os
from dotenv import load_dotenv
load_dotenv()
def order_food():
print("Starting food ordering process...")
# Get user preferences
restaurant = input("Which restaurant? ")
items = input("What would you like to order? ")
# Simulate API call to food delivery service
api_key = os.getenv("FOOD_API_KEY")
# This is a simplified example - real implementation would connect to actual APIs
print(f"Ordering {items} from {restaurant}...")
print("Order placed successfully!")
# In a real implementation, you would make actual API calls here
# Example: requests.post("https://api.foodservice.com/order",
# headers={"Authorization": f"Bearer {api_key}"},
# json={"restaurant": restaurant, "items": items})This simulates the process of ordering food, showing how an AI assistant would gather user preferences and send them to appropriate services.
Step 6: Implement ride booking automation
Create the ride booking module
In tasks/ride_booking.py, implement the ride booking automation:
import requests
import os
from dotenv import load_dotenv
load_dotenv()
def book_ride():
print("Starting ride booking process...")
# Get user preferences
destination = input("Where would you like to go? ")
ride_type = input("What type of ride? (uber/lyft) ")
# Simulate API call to ride service
api_key = os.getenv("RIDE_API_KEY")
print(f"Booking {ride_type} to {destination}...")
print("Ride booked successfully!")
# In a real implementation, you would make actual API calls here
# Example: requests.post("https://api.rideservice.com/book",
# headers={"Authorization": f"Bearer {api_key}"},
# json={"destination": destination, "ride_type": ride_type})This demonstrates how ride booking automation would work, collecting destination and ride type information from the user.
Step 7: Test your automation system
Run your automation system
Run your main script to test the automation system:
python main.pyWhen prompted, try ordering food or booking a ride to see how the system routes your requests to the appropriate automation functions.
Step 8: Extend with AI integration
Add natural language processing capabilities
To make this more like the Gemini AI system, you can integrate with an AI service like OpenAI's GPT:
import openai
# Add to your config.py
openai.api_key = os.getenv("OPENAI_API_KEY")
def interpret_user_request(user_input):
response = openai.Completion.create(
engine="text-davinci-003",
prompt=f"Interpret this request: {user_input}. Should I order food or book a ride?",
max_tokens=50
)
return response.choices[0].text.strip()This shows how an AI system would interpret natural language requests and determine the appropriate action to take.
Summary
In this tutorial, you've learned how to create a basic task automation system that can handle simple tasks like ordering food or booking rides. While this is a simplified implementation, it demonstrates the core principles behind AI assistants like Google's Gemini that can automate daily tasks. The system uses modular design, environment variables for security, and simulates API interactions that would connect to real services. In a production environment, you would replace the simulated API calls with actual integrations to services like Uber, DoorDash, or other delivery platforms.
This foundation can be extended with more sophisticated AI capabilities, natural language processing, and integration with smartphone APIs to create truly assistant-like experiences.
