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Python lab designed to teach how to build a ChatGPT-like interface using OpenAI's API

To start with, go to

https://platform.openai.com/docs/quickstart/step-2-setup-your-api-key⁠

to get an API KEY.

⁠

Here's a Python lab designed to teach how to build a ChatGPT-like interface using OpenAI's API, along with explanations and implementation workflows:

Python Lab: Creating Your Own ChatGPT-Style Conversational AI

Learning Objectives

Understand the core concepts of OpenAI's API and models.

Implement a Python-based chat interface that leverages ChatGPT's capabilities.

Explore techniques for refining conversation flow and response quality.

Prerequisites

Basic Python knowledge.

Familiarity with APIs.

An OpenAI API key (obtainable from your OpenAI account).

Setup

Install the OpenAI Library:

pip install openai

Set Your API Key:

import openai openai.api_key = "YOUR_API_KEY"

Lab Structure

Part 1: The Basic Chat Loop

def get_chatgpt_response(prompt): response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] ) return response.choices[0].message["content"]

while True: user_input = input("You: ") if user_input.lower() == "exit": break response = get_chatgpt_response(user_input) print("ChatGPT: " + response)

Explanation

The get_chatgpt_response function handles the core interaction with the OpenAI API, using the gpt-3.5-turbo model. The message formatting is crucial for providing context to the model.

The while loop keeps the conversation going until the user types "exit".

Part 2: Enhancing the Conversation

System Instructions: Refine the initial message to the model (the system role) to shape its behavior. For example:Python{"role": "system", "content": "You are a friendly and creative assistant who can answer questions and help with tasks."}

Conversation History: Store and pass previous messages in the messages list to give the model context and improve response coherence.

Error Handling: Add try...except blocks to catch potential API errors (rate limits, invalid requests, etc.).

Part 3: Advanced Features (Optional)

Function Calling: Allow the model to trigger external functions to perform actions (e.g., search the web, calculate, etc.).

Fine-tuning: Customize the model on your own dataset to make it more specific to your needs.

Token Management: Keep track of token usage to stay within your API limits.

Graphical Interface: Create a more user-friendly interface with libraries like Tkinter, PyQt, etc.

Example Conversation

You: Tell me a joke about programmers. ChatGPT: Why did the programmer quit his job? Because he didn't get arrays!

You: That's funny! Can you write a Python function to sort a list of numbers?

ChatGPT: Absolutely! Here's a Python function to sort a list of numbers using the built-in `sorted()` function:

```

python def sort_numbers(numbers): return sorted(numbers)

You: exit

**Important Notes** *

**API Usage and Costs:** Be mindful of OpenAI's API usage policies and costs. *

**Experimentation:** Try different prompts and system instructions to see how the model's behavior changes. *

**Ethical Considerations:** Always consider the ethical implications of AI and ensure responsible use.

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