- First of all, what is ChatGPT?
ChatGPT is a chatbot designed and trained by OpenAI. It is powered by OpenAI’s GPT-3 model (a language model that can perform a series of powerful text-based tasks like text translations, summarizations, writing code, among others).
- Why create your own version?
I got frustrated over the number of times I got thrown out of the web version due to large number of users accessing it simultaneously
Here are the Python modules I used in creating my own custom ChatGPT:
- flask - for building web apps
- os - for communicating with my machine’s operating system
- openai - for accessing OpenAI API via Python
- dotenv - for accessing the environment variables in my .env file
- logging - for recording data processing events and chat history with my ChatGPT bot
from flask import Flask, request, render_template
import openai
import os
from dotenv import load_dotenv
import logging# 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 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).
# 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 (i.e. streams events to stderr)
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
FileHandlerclass to give thefile_handlerobject the ability to write logs to a log file titledchatgpt_conversation_history.log. The mode is set towto indicate the program is writing to a file. - Line 2 sets the log formatter to
file_handler_log_formatterobject.
The last two lines are focused on initiating the object that writes the logs directly to the console.
- Line 1 uses the
StreamHandlerclass to give theconsole_handlerobject the ability to stream the logs to the console. - Line 2 sets the log formatter to
console_handler_log_formatterobject
# 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_handlerobject to theroot_logger. - Line 2 adds the
console_handlerobject to theroot_logger.
@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.
@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_chatgptfunction is theuser_inputvariable, which is used to pull my input message throughrequest.form['text']. - The second, third and fourth lines is just a temporary workaround to write the user input prompt without duplicating it in the file logs
- The fifth line contains the
chatgpt_responsevariable which holds ChatGPT’s responses in key-value pairs. It calls on theopenai.Completion.create()function which holds a few useful parameters to making it work:- model is the GPT-3 model selected. I used
text-davinci-003because 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, lower temperatures give safer and predictable responses
- max_tokens is the maximum number of tokens (expressed in words) 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
- model is the GPT-3 model selected. I used
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 response payload, which is unnested from a nested dictionary and then printed into the Flask app’s frontend interface.
if __name__ == '__main__':
app.run(debug=True)Once all the previous steps in A-C are completed, we can run the app in a dev location. I can access the app using http://localhost:5000/ in my browser once I run my script.
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.
Understanding what tasks ChatGPT shines in will increase your productivity rate as an engineer. ChatGPT is a great co-pilot, but it’s still far from becoming a tool that replaces many developer jobs. But that’s a topic for another day.
Here is the link to my GitHub page that contains the full repository for the code shared: