Build a Simple OpenAI App in Python

Building of a simple Openai application in Python

Looking to start with artificial intelligence and automation? Building of a simple Openai application in Python It is your clear and practical guide to launch smart Chatbot using Python and Openai’s API. In just a few steps, beginners can move from writing the first code line to the presence of an application supported by GPT-3.5 or GPT-4. This tutorial will walk by preparing your environment, stabilizing the dependencies, writing the code interacting with Openai to deal with requests and address responses. You will build a Chatbot working at full capacity using less than 50 lines of the Python icon.

Main meals

• Prepare your Python environment and create the API Openai key
• Building chatbot work with a brief and readable biton symbol
• Learn how to deal with responses and manage symbols efficiently
• Apply best practices to avoid excessive costs and multiplication

What you need before you start

This is it Openai API Python Tutorial Designed for beginners. If you are new to applications or Python, make sure you have the following:

• Bethon is installed (Edition 3.7 or higher). Download it from the official Python website.
• Icon editor Such as Vs Code, Pycham, or any lightweight text editor
• The basic use of the command line (peripheral or command instructor)
• Openai account with API key

Step by step: Build the first chatbot Openai in Python

1. Preparing a virtual environment

To keep isolating your project dependency, create a virtual environment:

python -m venv openai_app
cd openai_app
source bin/activate  # On Windows: .\Scripts\activate

2. Install the necessary dependencies

Install Openai Python customer with Dotenv package:

pip install openai python-dotenv

the dotenv The package helps you store secrets, such as API keys, safely in a .env file.

3. Prepare your API key

Log in to your Openai dashboard, create API, then store it in A. .env File in your project:

OPENAI_API_KEY="your_api_key_here"

Keep this file and not download it to a public warehouse.

4. Type the scenario Python Chatbot minimal

Keep the following symbol as chatbot.py. This text program allows you to speak with the artificial intelligence model directly from your station:

import os
import openai
from dotenv import load_dotenv

load_dotenv()

openai.api_key = os.getenv("OPENAI_API_KEY")

def ask_openai(prompt, model="gpt-3.5-turbo"):
    try:
        response = openai.ChatCompletion.create(
            model=model,
            messages=[{"role": "user", "content": prompt}]
        )
        answer = response['choices'][0]['message']['content']
        return answer.strip()
    except Exception as e:
        return f"Error: {str(e)}"

while True:
    user_input = input("You: ")
    if user_input.lower() in ["exit", "quit"]:
        break
    reply = ask_openai(user_input)
    print("Bot:", reply)

5. Run Chatbot

Start chatting by implementing the text program at your station:

python chatbot.py

Enter information or claims, and the robot will respond. To stop the program, write exit or quit.

Understand Openai’s response format

API returns the organized JSON object. Important parts include:

• choices[0].message.content: This contains the actual response to the model
• usage: It offers symbolic statistics for this request
• modelIt refers to the form that produced the response

Understanding this structure helps you improve claims and manage the use of the distinctive symbol better. For a broader application to use artificial intelligence to simplify frequent work, see how GPT-4 and Python efficiently automate tasks.

Openai API pricing, price limits, and the distinctive symbol management

The cost of using Openai forms depends on the number of codes that have been addressed. Here is the public pricing:

• GPT-3.5 Turbo: ~ 0.0015 dollars per icons of entering 1K, ~ 0.002 dollars for each output icons 1K
• GPT-4: ~ 0.03 dollars per icons of entering 1K, ~ 0.06 dollars per 1K output icons

When creating an account, you may receive free credits that allow limited use of use without charge. This is especially useful during learning or experience.

Smart practices to control API costs

• Start with the shortest demands and monitor the number of symbols that each call uses
• Set a monthly limit on your bodies page
• API records regularly to determine any excessive use
• Use the GPT-3.5-Turbo for effective solutions and change to GPT-4 only when needed

Treating error for stability

Real world applications should be prepared for network interruption, deadline or errors. Below is a copy of the job that improves reliability with better mistake messages:

def ask_openai(prompt, model="gpt-3.5-turbo"):
    try:
        response = openai.ChatCompletion.create(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            timeout=10
        )
        return response['choices'][0]['message']['content'].strip()
    except openai.error.RateLimitError:
        return "Rate limit exceeded. Try again later."
    except openai.error.AuthenticationError:
        return "Invalid API key. Check your .env file."
    except Exception as e:
        return f"An error occurred: {str(e)}"

GPT-3.5 opposite GPT-4: Home Differences

Downloaded source code

Access to the full Chatbot project here: Openai Simple Chatbot on GitHub.

For visual guide through the process, check this video wandering in YouTube.