How I Built My First AI Agent Using Python
How I Built My First AI Agent Using Python
The Beginning: My Curiosity About AI Agents
A few months ago, while scrolling through tech articles on medium and linkedIn, I came across the term AI agents. It sounded like something straight out of a sci-fi movie intelligent systems that could think, learn, and act on their own. I was fascinated but also overwhelmed. How could someone like me, with no background in artificial intelligence, ever understand or build something like that?
But my curiosity got the better of me. I started reading more about AI agents, machine learning, and natural language processing (NLP). I learned that AI agents are essentially software programs that can perform tasks autonomously, like chatbots, virtual assistants, or even game-playing bots. The more I read, the more I wanted to create my own AI agent.
The Turning Point: Discovering Open-Source Models
One day, I stumbled upon Hugging Face, a platform that offers open-source models for NLP tasks. I realized that I didn't need to build an AI model from scratch; I could use pre-trained models like GPT-2 or BERT to create my own AI agent. This was a game-changer for me. I decided to dive in and start coding.
The Project: Building an AI Agent Web Service
After weeks of learning Python and experimenting with small scripts, I finally felt ready to tackle my first AI agent project. I decided to create a web service that could take user input, process it using an AI model, and return a response. Here’s how I did it:
Step 1: Setting Up the Environment
I started by setting up a Python environment and installing the necessary libraries. I used Flask to create the web service and Hugging Face's Transformers library to load the GPT-2 model. Here’s what my requirements.txt file looked like:
Flask==2.0.1 transformers==4.11.3 python-dotenv==0.19.0
Step 2: Writing the Code
I structured my project like this:
ai_agent_service/
├── app.py
├── models/
│ └── model_predictor.py
├── utils/
│ └── logger.py
├── requirements.txt
└── README.md
app.py This is the main file that runs the web service. It uses Flask to create an API endpoint (/predict) where users can send input text and receive a response from the AI agent.
from flask import Flask, request, jsonify
from models.model_predictor import ModelPredictor
from utils.logger import get_logger
app = Flask(__name__)
logger = get_logger(__name__)
model_predictor = ModelPredictor()
@app.route('/predict', methods=['POST'])
def predict():
data = request.json
input_text = data.get('input', '')
logger.info(f"Received input: {input_text}")
prediction = model_predictor.predict(input_text)
return jsonify({'prediction': prediction})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
models/model_predictor.py This file handles loading the GPT-2 model and generating predictions.
from transformers import pipeline
from utils.logger import get_logger
logger = get_logger(__name__)
class ModelPredictor:
def __init__(self):
self.model = pipeline('text-generation', model='gpt-2')
def predict(self, input_text):
logger.info(f"Predicting for input: {input_text}")
return self.model(input_text, max_length=50)[0]['generated_text']
utils/logger.py This file sets up logging to help me debug and monitor the service.
import logging
def get_logger(name):
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(name)
return logger
Step 3: Running the Service
With the code in place, I ran the service using the following command:
python app.py
The service started running on http://localhost:5000, and I could interact with it using a simple curl command:
curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d '{"input": "Tell me a joke"}'
The AI agent responded with something like:
{
"prediction": "Why don't scientists trust atoms? Because they make up everything!"
}
What the Code Does
Web Service: The app.py file creates a RESTful API using Flask. It listens for POST requests at the /predict endpoint.
AI Model: The model_predictor.py file loads the GPT-2 model using the transformers library. It takes user input, generates a response, and returns it.
Logging: The logger.py file sets up logging to track what's happening in the service, making it easier to debug issues.
The Joy of Creation
When I saw the AI agent respond to my input for the first time, I felt an incredible sense of accomplishment. I had gone from knowing nothing about AI agents to building a functional web service that could generate text using a state-of-the-art model. It wasn't perfect, but it was mine.
Final Thoughts
Building this AI agent taught me that you don't need to be an expert to start working with AI. With the right tools and a bit of curiosity, anyone can create something amazing. I'm excited to continue learning and building more projects in the future.
If you're just starter, don't shay away instead dive in. The world of AI is vast and full of opportunities, and you never know where your curiosity might take you.
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