How to Implement ChatGPT in Django

Integrating ChatGPT into your Django application allows you to create dynamic and interactive chat interfaces. Here are detailed instructions on how to integrate ChatGPT into Django apps.

How to Implement ChatGPT in Django Picture 1

By following the steps below, you can use ChatGPT in your Django project and provide your users with an engaging chat experience. Experiment with different prompts, improve the user interface, and explore other possibilities to enhance the functionality of your chat app.

Steps to implement ChatGPT in Django app using API

Integrating ChatGPT into Django web apps allows you to build interactive chat interfaces and improve the vibrant chat experience for your users. Basically, you have to pay through the following steps:

  1. Set up a Django project.
  2. Defines the Django viewport, which handles the chat feature.
  3. Configure Django's URL routing, to connect the chat window to a specific URL pattern.
  4. User interface design.
  5. Test and run the application.

Implement GPT in Django Application

Basic Setup

Install Django using pip, create a new Django project. Make changes in setting.py, refer to the template directory in TEMPLATES.

How to Implement ChatGPT in Django Picture 2

Directory structure

How to Implement ChatGPT in Django Picture 3

views.py : Create chat window

This code contains two functions: query_view and get_completion.

  1. query_view is the function that handles the HTTP query made to the endpoint. When it receives a POST request, it extracts the prompt from the request data, calls the get_completion function to generate a completion result based on the prompt, and returns the completion result as a JSON response. For GET requests, it shows the template 'index.html'.
  2. get_completion is the function responsible for generating completion based on given prompt using OpenAI API. It makes a request to the OpenAI API completion endpoint, specifying prompts, max tokens, stuff, temperature, and other parameters. It retrieves the generated completion from the API response and returns it as function output.

For example:

from django.shortcuts import render from django.http import JsonResponse import openai openai.api_key = 'YOUR_API_KEY' def get_completion(prompt): print(prompt) query = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=1024, n=1, stop=None, temperature=0.5, ) response = query.choices[0].text print(response) return response def query_view(request): if request.method == 'POST': prompt = request.POST.get('prompt') response = get_completion(prompt) return JsonResponse({'response': response}) return render(request, 'index.html')

 

urls.py : Set URL

This code sets up two URL patterns: one for the admin page and one for the root URL, but the other link is combined with the query_view function in the views module.

from django.contrib import admin from django.urls import path from . import views urlpatterns = [ path('admin/', admin.site.urls), path('', views.query_view, name='query'), ]

template/index.py : Create user interface

Create HTML templates for chat interfaces using HTML, CSS, and Bootstrap, and set up the JavaScript and AJAS needed to handle user interaction.

 Query 

ChatGPT Clone

 {% csrf_token %} Prompt: 
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