How to Build a Text Analyzer with OpenAI GPT-3 and Django

Posted on by Panther

In this tutorial, we will learn how to build a text analyzer using OpenAI GPT-3 and Django. The text analyzer will take an input text and analyze it to provide various insights, such as sentiment analysis, key phrase extraction, and entity recognition.

OpenAI GPT-3 is a powerful language model that can generate human-like text. Django is a popular Python web framework that allows us to build web applications quickly and easily.

To get started, make sure you have Python and Django installed on your machine. You will also need an OpenAI API key to access GPT-3. You can sign up for an API key on the OpenAI website.

Setting up the Django project

First, let’s create a new Django project and set up the necessary dependencies.

  1. Open a terminal and navigate to the directory where you want to create your project.
  2. Run the following command to create a new Django project:
$ django-admin startproject text_analyzer
  1. Change into the project directory:
$ cd text_analyzer
  1. Create a virtual environment for your project:
$ python -m venv env
  1. Activate the virtual environment:
$ source env/bin/activate
  1. Install the required dependencies:
$ pip install django openai
  1. Create a new Django app within the project:
$ python manage.py startapp analyzer

Creating the text analyzer views

Next, let’s create the views that will handle the text analyzer functionality.

  1. Open the analyzer/views.py file and replace the existing code with the following:
from django.shortcuts import render
from openai import api_key, GPT

gpt = GPT(api_key=api_key)

def home(request):
    return render(request, 'analyzer/home.html')

def analyze(request):
    if request.method == 'POST':
        text = request.POST.get('text', '')
        response = gpt.send_text(text)
        return render(request, 'analyzer/analyze.html', {'response': response})
  1. Create a new directory called templates inside the analyzer directory:
$ mkdir analyzer/templates
  1. Create the home.html template file inside the templates/analyzer directory:
<!-- analyzer/templates/analyzer/home.html -->
<!DOCTYPE html>
<html>
<head>
    <title>Text Analyzer</title>
</head>
<body>
    <h1>Text Analyzer</h1>
    <form method="POST" action="{% url 'analyze' %}">
        {% csrf_token %}
        <textarea name="text"></textarea>
        <button type="submit">Analyze</button>
    </form>
</body>
</html>
  1. Create the analyze.html template file inside the templates/analyzer directory:
<!-- analyzer/templates/analyzer/analyze.html -->
<!DOCTYPE html>
<html>
<head>
    <title>Text Analyzer - Analysis Results</title>
</head>
<body>
    <h1>Analysis Results</h1>
    <h2>Sentiment</h2>
    <p>{{ response.sentiment }}</p>
    <h2>Key Phrases</h2>
    <ul>
        {% for phrase in response.key_phrases %}
            <li>{{ phrase }}</li>
        {% empty %}
            <li>No key phrases found</li>
        {% endfor %}
    </ul>
    <h2>Entities</h2>
    <ul>
        {% for entity in response.entities %}
            <li>{{ entity }}</li>
        {% empty %}
            <li>No entities found</li>
        {% endfor %}
    </ul>
</body>
</html>

Configuring the Django project

Before we can run the application, we need to configure the Django project.

  1. Open the text_analyzer/settings.py file and add 'analyzer' to the INSTALLED_APPS list:
INSTALLED_APPS = [
    ...
    'analyzer',
]
  1. Add the analyzer URL patterns to the urls.py file:
from django.urls import path
from analyzer import views

urlpatterns = [
    path('', views.home, name='home'),
    path('analyze/', views.analyze, name='analyze'),
]

Running the text analyzer

Now we are ready to run the text analyzer.

  1. Make sure you are in the text_analyzer directory and activate the virtual environment:
$ source env/bin/activate
  1. Apply the database migrations:
$ python manage.py migrate
  1. Start the development server:
$ python manage.py runserver
  1. Open your web browser and navigate to `http://localhost:8000` to access the text analyzer.

You should see a form where you can enter the text to analyze. Once you submit the form, the analysis results will be displayed on a new page.

Enhancing the text analyzer

We have built a basic text analyzer using OpenAI GPT-3 and Django, but there are many ways to enhance its capabilities.

  • Text summarization: Use GPT-3 to summarize the given text into a shorter, more concise form.
  • Question answering: Implement a feature where users can ask questions about the text and get answers generated by GPT-3.
  • Topic modeling: Analyze the given text to identify the main topics or themes present.
  • Language translation: Use GPT-3 to translate the given text into different languages.

These are just a few ideas to get you started. With GPT-3’s powerful language generation capabilities, you can explore various ways to improve and expand the functionality of the text analyzer.

Conclusion

In this tutorial, we learned how to build a text analyzer using OpenAI GPT-3 and Django. We set up a Django project, created the necessary views and templates, and configured the project to run the text analyzer.

By integrating OpenAI GPT-3 with Django, we were able to create a powerful language analysis tool that can provide valuable insights about any given text. With the ability to generate human-like responses, GPT-3 allows us to build sophisticated applications that can understand and analyze text data.

Remember to experiment and explore different ways to enhance the text analyzer’s functionality. OpenAI GPT-3 offers a rich set of language generation capabilities that can be leveraged to create even more powerful applications.

Happy analyzing!

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