Create a new directory for your project and navigate into it:<\/li>\n<\/ol>\n$ mkdir text_classifier\n$ cd text_classifier\n<\/code><\/pre>\n\n- Create a new Python virtual environment and activate it:<\/li>\n<\/ol>\n
$ python -m venv venv\n$ source venv\/bin\/activate\n<\/code><\/pre>\n\n- Install Django using pip:<\/li>\n<\/ol>\n
$ pip install django\n<\/code><\/pre>\n\n- Create a new Django project and navigate into it:<\/li>\n<\/ol>\n
$ django-admin startproject text_classifier_project .\n$ cd text_classifier_project\n<\/code><\/pre>\nCreating the Text Classifier App<\/h2>\n
Now that we have set up the project, we will create a new Django app to handle the text classification functionality. In your terminal\/command prompt, execute the following commands:<\/p>\n
\n- Create a new Django app:<\/li>\n<\/ol>\n
$ python manage.py startapp classifier\n<\/code><\/pre>\n\n- Open the
text_classifier_project\/settings.py<\/code> file and add 'classifier',<\/code> to the INSTALLED_APPS<\/code> list.<\/li>\n<\/ol>\nCreating the Text Classification Model<\/h2>\n
In this step, we will create a Django model to store the text classification data. Open the classifier\/models.py<\/code> file and add the following code:<\/p>\nfrom django.db import models\n\nclass Category(models.Model):\n name = models.CharField(max_length=100)\n\n def __str__(self):\n return self.name\n\nclass Text(models.Model):\n content = models.TextField()\n category = models.ForeignKey(Category, on_delete=models.CASCADE)\n\n def __str__(self):\n return self.content\n<\/code><\/pre>\nThis code defines two models: Category<\/code> and Text<\/code>. The Category<\/code> model represents the different categories for classification, and the Text<\/code> model represents individual text instances with their associated category.<\/p>\nNext, run the following commands in the terminal to apply the model changes to the database:<\/p>\n
$ python manage.py makemigrations\n$ python manage.py migrate\n<\/code><\/pre>\nCreating the Text Classification Form<\/h2>\n
Now, we will create a Django form to allow users to enter text for classification. Open the classifier\/forms.py<\/code> file and add the following code:<\/p>\nfrom django import forms\nfrom .models import Text\n\nclass TextForm(forms.ModelForm):\n class Meta:\n model = Text\n fields = ['content', 'category']\n<\/code><\/pre>\nThe TextForm<\/code> class is a Django model form that is automatically created based on the Text<\/code> model. It includes fields for the content and category of the text.<\/p>\nCreating the Text Classification View<\/h2>\n
In this step, we will create a Django view to handle the text classification functionality. Open the classifier\/views.py<\/code> file and add the following code:<\/p>\nfrom django.shortcuts import render\nfrom .forms import TextForm\n\ndef classify_text(request):\n if request.method == 'POST':\n form = TextForm(request.POST)\n if form.is_valid():\n text = form.save(commit=False)\n # TODO: Implement text classification with GPT-3\n text.save()\n return render(request, 'classifier\/classify_text.html', {'form': form, 'classification_result': text.category})\n else:\n form = TextForm()\n return render(request, 'classifier\/classify_text.html', {'form': form})\n<\/code><\/pre>\nThe classify_text<\/code> function is a Django view that handles the text classification functionality. Currently, it just saves the submitted text to the database without performing any classification.<\/p>\nIntegrating OpenAI GPT-3 for Text Classification<\/h2>\n
Now comes the exciting part \u2013 integrating OpenAI GPT-3 into our Django application for text classification. To communicate with GPT-3, we will use the OpenAI Python library, which can be installed using pip:<\/p>\n
$ pip install openai\n<\/code><\/pre>\nOnce installed, you can use the following code to integrate GPT-3 text classification into your classifier\/views.py<\/code> file:<\/p>\nimport openai\n\nopenai.api_key = 'YOUR_OPENAI_API_KEY'\n\ndef classify_text(request):\n if request.method == 'POST':\n form = TextForm(request.POST)\n if form.is_valid():\n text = form.save(commit=False)\n classification_result = classify_with_gpt3(text.content)\n text.category = classification_result\n text.save()\n return render(request, 'classifier\/classify_text.html', {'form': form, 'classification_result': classification_result})\n else:\n form = TextForm()\n return render(request, 'classifier\/classify_text.html', {'form': form})\n\ndef classify_with_gpt3(text):\n response = openai.Completion.create(\n engine='davinci',\n prompt=text,\n max_tokens=1,\n n=1,\n stop=None,\n temperature=0.7,\n top_p=1,\n frequency_penalty=0,\n presence_penalty=0,\n )\n category = response.choices[0].text.strip().lower()\n return category\n<\/code><\/pre>\nMake sure to replace 'YOUR_OPENAI_API_KEY'<\/code> with your actual OpenAI API key. The classify_with_gpt3<\/code> function makes a POST request to the OpenAI API with the GPT-3 engine and your prompt text. It returns the generated text from GPT-3.<\/p>\nNow, when a user submits text for classification, it will be sent to GPT-3, and the generated category will be saved in the database. The generated classification result is passed to the template to display to the user.<\/p>\n
Updating the Text Classification Template<\/h2>\n
Next, we will create the template that displays the text classification form and the classification result to the user. Create a new directory called templates<\/code> inside the classifier<\/code> app directory, and inside that directory, create a new file named classify_text.html<\/code>. Add the following code to the file:<\/p>\n{% extends 'base.html' %}\n\n{% block content %}\n <h2>Classify Text<\/h2>\n <form method=\"post\">\n {% csrf_token %}\n {{ form.as_p }}\n <button type=\"submit\">Classify<\/button>\n <\/form>\n {% if classification_result %}\n <div>\n <h3>Classification Result:<\/h3>\n <p>{{ classification_result }}<\/p>\n <\/div>\n {% endif %}\n{% endblock %}\n<\/code><\/pre>\nThis template extends a base template called base.html<\/code> (which we will create shortly) and displays the text classification form. If a classification result exists, it will be displayed to the user.<\/p>\nCreating the Base Template<\/h2>\n
We will now create the base template that our classify_text.html<\/code> template extends. Create a new file named base.html<\/code> inside the templates<\/code> directory in the root folder of your project. Add the following code to the file:<\/p>\n<!DOCTYPE html>\n<html>\n<head>\n <title>Text Classifier<\/title>\n<\/head>\n<body>\n <header>\n <h1>Text Classifier<\/h1>\n <\/header>\n <main>\n {% block content %}\n {% endblock %}\n <\/main>\n<\/body>\n<\/html>\n<\/code><\/pre>\nThe base template sets up the basic structure for our web application and provides a header and a main content section. The main content section will be populated by the child templates.<\/p>\n
Configuring the URL Routing<\/h2>\n
In the final step, we need to configure the URL routing to connect our views with the appropriate URLs. Open the text_classifier_project\/urls.py<\/code> file and modify it as follows:<\/p>\nfrom django.contrib import admin\nfrom django.urls import path\nfrom classifier.views import classify_text\n\nurlpatterns = [\n path('admin\/', admin.site.urls),\n path('', classify_text, name='classify_text'),\n]\n<\/code><\/pre>\nThis configuration uses the classify_text<\/code> function from our classifier\/views.py<\/code> file as the view for the root URL of our application.<\/p>\nRunning the Development Server<\/h2>\n
That’s it! Your text classifier application is now ready to run. In your terminal or command prompt, navigate to the root folder of your project (text_classifier_project<\/code>) and run the following command to start the development server:<\/p>\n$ python manage.py runserver\n<\/code><\/pre>\nYou should now see output similar to the following:<\/p>\n
Starting development server at http:\/\/127.0.0.1:8000\/\nQuit the server with CONTROL-C.\n<\/code><\/pre>\nOpen your web browser and visit `http:\/\/127.0.0.1:8000\/` to see your text classifier application in action. You can enter some text into the form and click the “Classify” button to see the classification result.<\/p>\n
Conclusion<\/h2>\n
In this tutorial, we explored how to build a text classifier using OpenAI GPT-3 and Django. We created a Django app with models, forms, views, and templates to handle text classification. We integrated OpenAI GPT-3 using the OpenAI Python library to generate classifications for input text. By following this tutorial, you should now have a solid foundation for building your own text classification applications using GPT-3 and Django.<\/p>\n","protected":false},"excerpt":{"rendered":"
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