How to create ChatGPT with Django and HTMX in 4 minutes 🦾

We'll re-create ChatGPT in 4 steps - and under 4 minutes.

After this tutorial, you'll know how to enrich all your Django apps with the best AI text generation tool available as of writing 🤖

How our final product will look:

The video below follows the written guide. We'll use Django, HTMX, and GPT3.5 or 4. Let's go. 💫

For a full online demo, see the Circumeo link at the end 🎪

1. Setup django and packages:

We'll assume that we've already installed Django and a virtual environment.

django-admin startproject core
python manage.py startapp chatbot_app
pip install requests

Add chatbot_app to the end of your INSTALLED_APPS in chatbot_project/settings.py:

INSTALLED_APPS = [
  	...
    'chatbot_app',
  ]

Create a model to store your chat messages in the database:
in chatbot_app/models.py:

from django.db import models


class Message(models.Model):
		user_message = models.TextField()
		bot_message = models.TextField()
		timestamp = models.DateTimeField(auto_now_add=True)

Run your migrations to update your database in the terminal:

python manage.py makemigrations
python manage.py migrate

Create a view to handle the chat

In chatbot_app/views.py:

from django.shortcuts import render
from .models import Message

def chat_view(request):
    if request.method == "POST":
        user_message = request.POST.get('message')
        bot_message = "Hello!"  # We will replace this line.
        Message.objects.create(user_message=user_message, bot_message=bot_message)
    messages = Message.objects.all()
    return render(request, 'chat.html', {'messages': messages})

2. Add HTMX to the frontend

In chatbot_app/templates/chat.html:

<!doctype html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <title>AI Chatbot</title>
    <script
      src="https://unpkg.com/htmx.org@1.9.4"
      integrity="sha384-zUfuhFKKZCbHTY6aRR46gxiqszMk5tcHjsVFxnUo8VMus4kHGVdIYVbOYYNlKmHV"
      crossorigin="anonymous"
    ></script>
    <style>
      .input-fields {
        display: flex;
        flex-direction: row;
      }

      /* Style for the loading spinner */
      .my-indicator {
        display: none;
        border: 2px solid #f3f3f3;
        border-top: 2px solid #3498db;
        border-radius: 50%;
        width: 20px;
        height: 20px;
        animation: spin 0.5s linear infinite;
      }
      .htmx-request .my-indicator {
        display: inline-block;
      }
      @keyframes spin {
        0% {
          transform: rotate(0deg);
        }
        100% {
          transform: rotate(360deg);
        }
      }
      .user-message {
        color: #b83eff;
        padding: 5px;
      }
      .bot-message {
        padding: 5px;
        color: darkblue;
      }
      input[type='text'] {
        width: 50%;
      }
    </style>
  </head>
  <body>
    <div id="container">
      <div id="chatbox">
        {% for message in messages %}
        <div>User: {{ message.user_message }}</div>
        <div>Bot: {{ message.bot_message }}</div>
        {% endfor %}
      </div>

      <form
        hx-post="{% url 'chat_view' %}"
        hx-target="#container"
        hx-swap="innerHTML"
      >
        {% csrf_token %}
        <div class="my-indicator"></div>
        <div class="input-fields">
          <input type="text" name="message" />
          <button type="submit">Send</button>
        </div>
      </form>
    </div>
  </body>
</html>

3. Connect remaining Django elements

In chatbot_app/urls.py:

from django.urls import path
from .views import chat_view

urlpatterns = [
    path('', chat_view, name='chat_view'),
]

In core/urls.py:

from django.contrib import admin
from django.urls import path, include

urlpatterns = [
    path('admin/', admin.site.urls),
    path('chat/', include('chatbot_app.urls')),
]

Run the server to check.

Visit http://127.0.0.1:8000/chat/ to check your work.

python manage.py runserver

4. Add AI

Modify chatbot_app/views.py
Replace YOUR_OPENAI_API_KEY with your api key.
Prepend your key with "Bearer". (If your api key is '1234', your Authorization value will be "Bearer 1234")

from django.shortcuts import render
from .models import Message
import requests


def chat_view(request):
    if request.method == "POST":
        user_message = request.POST.get('message')
        bot_message = get_ai_response(user_message)
        Message.objects.create(user_message=user_message, bot_message=bot_message)
    messages = Message.objects.all()
    return render(request, 'chat.html', {'messages': messages})


def get_ai_response(user_input: str) -> str:
    # Set up the API endpoint and headers
    endpoint = "https://api.openai.com/v1/chat/completions"
    headers = {
        "Authorization": "Bearer <YOUR_OPENAI_API_KEY>",
        "Content-Type": "application/json",
    }

    # Data payload
    messages = get_existing_messages()
    messages.append({"role": "user", "content": f"{user_input}"})
    data = {
        "model": "gpt-3.5-turbo",
        "messages": messages,
        "temperature": 0.7
    }
    response = requests.post(endpoint, headers=headers, json=data)
    response_data = response.json()
    print(f'{response_data = }')
    ai_message = response_data['choices'][0]['message']['content']
    return ai_message


def get_existing_messages() -> list:
    """
    Get all messages from the database and format them for the API.
    """
    formatted_messages = []

    for message in Message.objects.values('user_message', 'bot_message'):
        formatted_messages.append({"role": "user", "content": message['user_message']})
        formatted_messages.append({"role": "assistant", "content": message['bot_message']})

    return formatted_messages

You should now see something like this

Finished (Now you can use LLMs)

We're done 🎉 Your app now offers the minimal functionality that ChatGPT provides, albeit without the custom prompt that the ChatGPT users.

However, you could add a prompt that is even more useful for you instead. Here is OpenAI's best practices guide to using GPT.

Full online demo 🎪:

Here's a full demo of the app using Circumeo. To do this:

Visit the project fork page and click the "Create Fork" button.
Migrations will run and the app will launch in about 10 seconds.
Add an environment variable with the key of OPENAI_API_KEY and the value of your api key in the 'Variables' tab.
Click deploy