Preface🧠

A few days ago (from the writing of this article) I had a great catch-up with one of my friends on the phone about the tech scene right now. For a few moments, we touched on ChatGPT regarding the good, bad, hype and everything in between of it.

After mentioning how frustrating I found the unexpected site crashes due to high global usage, an idea sparked in my mind - “Why depend on the web version when you can create your version of ChatGPT?”💡

What is ChatGPT?🤖

ChatGPT is a chatbot made by OpenAI that can hold conversations with humans. It can perform several tasks like translating text, summarizing articles, writing codes, and many more.

It is powered by OpenAI’s GPT-3 model (a language model that can perform a series of powerful text-based tasks).

Libraries📖

Here are the Python modules I used in creating my custom ChatGPT:

from flask import Flask, request, render_template
import openai
import os
from dotenv import load_dotenv
import logging

• flask - for building web apps

• openai - for accessing OpenAI’s API via Python

• os - for communicating with my machine’s operating system

• dotenv - for accessing the environment variables in my .env file

• logging - for recording data processing events and chat history with my ChatGPT bot

Steps 🚂

A. Environment initiators

# Set up your app environment 
app = Flask(__name__, template_folder='templates')
load_dotenv()
openai.api_key = os.getenv('OPENAI_API_KEY')

The first line above initiates the Flask app. The __name__ variable is used to determine whether the current script is the main program or coming from another module/location. It does this by checking the script’s root path.

The second and third lines read the API key given to me by OpenAI from a secure file I saved it in (called .env).

B. Event loggers

# Set up root root_logger 
root_logger = logging.getLogger(__name__)
root_logger.setLevel(logging.DEBUG)

I create an object for logging called root_logger, and I set the logging severity level to DEBUG, which will enable the root_logger to record logs of every security level (i.e. debug, info, warning, error and critical)

# Set up formatters for logs 
file_handler_log_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s  ')
console_handler_log_formatter = logging.Formatter('%(message)s ')

The first and second lines create objects responsible for recording the logs in a clean and consistent format. But there are distinct differences between the two:

• Line 1 records logs into a file on the machine in a ‘current timestamp - log severity level - message’ format

• Line 2 streams logs onto the console in a user-friendly format i.e. only prints the messages to the console

# Set up file handler object for logging events to file
file_handler = logging.FileHandler('chatgpt_conversation_history.log', mode='w')
file_handler.setFormatter(file_handler_log_formatter)

# Set up console handler object for writing event logs to console in real time
console_handler = logging.StreamHandler()
console_handler.setFormatter(console_handler_log_formatter)

The first two lines are focused on initiating the object that records logs to file.

• Line 1 uses the FileHandler class to give the file_handler object the ability to write logs to a log file titled chatgpt_conversation_history.log . The mode is set to w to indicate the program is writing to a file.

• Line 2 sets the log formatter to file_handler_log_formatter object.

The last two lines are focused on initiating the object that writes the logs directly to the console.

• Line 1 uses the StreamHandler class to give the console_handler object the ability to stream the logs to the console.

• Line 2 sets the log formatter to console_handler_log_formatter object

# Add the file and console handlers 
root_logger.addHandler(file_handler)
root_logger.addHandler(console_handler)

These lines add the handlers developed earlier to my primary logger object called root_logger.

• Line 1 adds the file_handler object to the root_logger.

• Line 2 adds the console_handler object to the root_logger.

C. Web app

# Direct users to home page
@app.route('/')
def render_index_html():
    return render_template('index.html')

The app.route decorator points the user to the home page of the Flask app, which is where the chat box currently lives.

The render_index_html function displays the home page using the index.html page it finds in the templates folder we defined in the beginning via the render_template function.

The index.html file in the templates folder is the skeleton of the web page.

# Activate conversation features with ChatGPT
@app.route('/chat', methods=['POST'])
def render_chat_with_chatgpt():
    user_input = request.form['text']
    root_logger.removeHandler(console_handler)
    root_logger.info(f':: Me (SDW):   {user_input}' )
    root_logger.addHandler(console_handler)
    chatgpt_response = openai.Completion.create(
        model="text-davinci-003",
        prompt=user_input,
        temperature=0.3,
        max_tokens=1000,
        top_p=1.0,
        frequency_penalty=0,
        presence_penalty=0
    )
    root_logger.info(f':: ChatGPT: {chatgpt_response["choices"][0]["text"]}  ')
    root_logger.debug('--------------------------------------------------')
    return chatgpt_response["choices"][0]["text"]

I created the render_chat_with_chatgpt function under the @app.route('/chat', methods=['POST']) decorator to form a fluid conversation with ChatGPT.

I set the methods argument to ‘POST’ because we only want the ‘chat' route to respond to HTTP POST requests expressed as sent messages to the ChatGPT bot. When we send a message to the ‘chat' route, it triggers the render_chat_with_chatgpt function to generate a dynamic response from OpenAI GPT-3’s database.

• The first line inside render_chat_with_chatgpt function is the user_input variable, which is used to pull my input message through request.form['text'].

• The second, third and fourth lines are just a temporary workaround to write the user input prompt without duplicating it in the file logs

• The fifth line contains the chatgpt_response variable which holds ChatGPT’s responses in key-value pairs. It calls on the openai.Completion.create() function which holds a few useful parameters to make it work:

  • model is the GPT-3 model selected. I used text-davinci-003 because it’s one of the best models for text-completion prompts

  • prompt is the input message or query you send to ChatGPT

  • temperature deals with the model’s randomness level in the responses it gives. The higher the temperature, the more random (but interesting) the responses are likely to be. The lower the temperature, the more consistent (and predictable) the responses become. So high temperatures give more unique responses, and lower temperatures give safer and more predictable responses

  • max_tokens is the maximum number of tokens (i.e. words) that can be generated in a single response

  • top_p is used to pick words based on how common or uncommon they are to form sentences with. A lower top_p value means more uncommon words will be selected for generating sentences in the response, and a higher top_p value will use more common words

  • frequency_penalty is a value between -2.0 and 2.0 used to discourage using words appearing in the input prompt frequently, which promotes more verbose responses

  • presence_penalty is a value between -2.0 and 2.0 used to discourage re-using words already in the input prompts, which promotes more original responses

root_logger.info(f':: ChatGPT: {chatgpt_response["choices"][0]["text"]}  ')
root_logger.debug('--------------------------------------------------')
return chatgpt_response["choices"][0]["text"]

ChatGPT’s response is extracted from the JSON payload, which is unnested and then printed into the Flask app’s frontend interface.

D. Loading app

if __name__ == '__main__':
    app.run(debug=True)

Once all the steps in A-C are completed, we can run the app and access it by entering http://localhost:5000/ into my URL.

Demo 🦇

And here is what it looks like after executing the above commands:

Conclusion

It’s important to note ChatGPT still spits out errors and biases in some of the answers it feeds back to us, so it's still a long way off from replacing certain developer roles or acting as a primary source of reliable information. Nevertheless, it’s hard to ignore the fact it can accelerate the software development process by serving as an excellent co-pilot to developers.

Developers with technical expertise and domain knowledge are more equipped at delivering solutions faster to end users with ChatGPT than without it. Here's another way to put it - it does a better job of making good engineers great than making average engineers good. But that’s a topic for another day.

It’s likely one of the emerging threats to search engines like Google because of its ability to condense answers to questions in clear and concise sentences without all the hassle of sifting through several links and articles.

This is just the starter pack in getting my custom version of ChatGPT off the ground in the event of another system crash on the web’s preview version. There’s more work to add on the front-end and UX side that is currently on my to-do list. But this can serve as a backup in case ChatGPT goes down again.

You can find the full code on my GitHub repository by clicking here.

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