How to Build a Text-to-Image App with OpenAI GPT-3 and Google Cloud API

Posted on by Panther

In this tutorial, we will walk through the process of building a text-to-image application using the power of OpenAI GPT-3 and the Google Cloud Vision API. With GPT-3, we can generate human-like text descriptions of images, and with the Google Cloud API, we can turn those text descriptions into actual images.

Prerequisites

To follow along with this tutorial, you will need:

  • Basic knowledge of Python programming language
  • OpenAI GPT-3 API key
  • Google Cloud Platform account with billing enabled

Step 1: Set Up OpenAI GPT-3

First, let’s set up our OpenAI GPT-3 API.

  1. Sign in to the OpenAI website and navigate to the API page.
  2. Create a new API key by following the instructions provided by OpenAI.
  3. Once you have your API key, make sure you have the OpenAI Python library installed by running pip install openai in your terminal.

Step 2: Set Up Google Cloud Vision API

Next, let’s set up the Google Cloud Vision API.

  1. Sign in to the Google Cloud Console and create a new project.
  2. Enable the Cloud Vision API for your project by following the instructions provided by Google.
  3. After enabling the API, go to the credentials tab and create a new API key for your project.

Make sure you have the Google Cloud Python library installed by running pip install google-cloud-vision in your terminal.

Step 3: Set Up the Python Environment

In this step, we will set up the Python environment and install the necessary Python packages.

  1. Create a new directory for your project and navigate to it in your terminal.
  2. Create a new virtual environment by running python -m venv .venv.
  3. Activate the virtual environment by running . .venv/bin/activate on macOS or Linux, or .venvScriptsactivate.bat on Windows.
  4. Install the required Python packages by running pip install flask openai google-cloud-vision pillow in your terminal.

Step 4: Build the Text-to-Image App with Flask

Now let’s start building our text-to-image application using Flask.

  1. Create a new file called app.py in your project directory.
  2. Open app.py in your favorite text editor and import the necessary modules:
from flask import Flask, render_template, request, redirect
from google.cloud import vision
import requests
import base64
import io
import openai
from PIL import Image
  1. Initialize the Flask app:
app = Flask(__name__)
  1. Add the API keys:
# GPT-3 API key
openai.api_key = 'YOUR_OPENAI_API_KEY'

# Google Cloud Vision API key
vision_client = vision.ImageAnnotatorClient.from_service_account_file('YOUR_GOOGLE_CLOUD_API_KEY.json')
  1. Define the main route for the web application:
@app.route('/')
def index():
    return render_template('index.html')
  1. Create a new route to handle the form submission:
@app.route('/submit', methods=['POST'])
def submit():
    # Get the input text from the form
    text = request.form['text']

    # Generate the image description using GPT-3
    response = openai.Completion.create(
        engine='davinci',
        prompt=text + "Generate an image that matches the description:",
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.8
    )

    # Get the generated image description
    image_description = response.choices[0].text.strip()

    # Create an image from the generated description using Google Cloud Vision API
    image = create_image(image_description)

    # Save the image to a file
    image.save('static/image.png')

    return redirect('/result')
  1. Next, implement the create_image() function:
def create_image(description):
    # Create an image from the description using Google Cloud Vision API
    image = vision_client.image_annotator

    # Generate an image request
    request = {
        'image': {
            'source': {
                'image_uri': 'https://source.unsplash.com/800x600/?' + '+'.join(description.split())
            },
        },
        'features': [
            {'type': vision.Feature.Type.LABEL_DETECTION},
        ],
    }

    # Perform the request
    response = image.annotate_image(request)

    # Get the image data
    image_data = response.image.download()

    # Convert the image data to a PIL image
    pil_image = Image.open(io.BytesIO(image_data))
    return pil_image
  1. Create a new route to display the result:
@app.route('/result')
def result():
    return render_template('result.html')
  1. Finally, create the necessary HTML templates. Create a file called index.html in the templates directory, and add the following code:
<!DOCTYPE html>
<html>
<head>
    <title>Text-to-Image App</title>
</head>
<body>
    <h1>Text-to-Image App</h1>
    <form action="/submit" method="POST">
        <label for="text">Enter a description:</label><br>
        <input type="text" id="text" name="text"><br>
        <input type="submit" value="Generate Image">
    </form>
</body>
</html>
  1. Create another file called result.html in the templates directory, and add the following code:
<!DOCTYPE html>
<html>
<head>
    <title>Text-to-Image App</title>
</head>
<body>
    <h1>Generated Image:</h1>
    <img src="/static/image.png" alt="Generated Image">
</body>
</html>

Step 5: Run the Text-to-Image App

Now that we’ve built the application, let’s run it locally.

  1. In your terminal, make sure you are in the project directory and have activated the virtual environment.
  2. Run the Flask app by executing the following command: flask run.
  3. Open your web browser and navigate to `http://localhost:5000`.
  4. Enter a description in the form and click the “Generate Image” button.
  5. Wait for the app to generate the image and redirect you to the result page.
  6. The generated image will be displayed on the result page.

Congratulations! You have successfully built a text-to-image application using OpenAI GPT-3 and the Google Cloud Vision API.

Conclusion

In this tutorial, we have learned how to use the power of OpenAI GPT-3 and the Google Cloud Vision API to build a text-to-image application. We explored the process of generating image descriptions using GPT-3 and converting those descriptions into actual images using the Google Cloud Vision API. With this knowledge, you can build your own image generation applications and explore the possibilities of AI-powered image generation.

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