Generating a Simple Image with Gemini's Nano Banana and Python | CodeSignal Learn

Generating a Simple Image with Gemini's Nano Banana and Python | CodeSignal Learn

Introduction to Gemini's Image Generation and Python

Welcome to the first lesson of our course, "Creating Images with Gemini's Nano Banana and Python" . In this course, you will explore the fascinating world of AI-driven image generation using Google's Gemini API and its native image generation models, known as Nano Banana . Our journey begins with understanding how to set up the environment and generate a simple image. We'll also touch upon Python, a lightweight web framework that will help us integrate and display our generated images. This foundational lesson will set the stage for more advanced topics in subsequent units. A quick note on naming: Google's older standalone Imagen models have been deprecated, and image generation is now unified directly inside the Gemini models. Throughout this course we will use gemini-3.1-flash-image (also called Nano Banana 2), which is optimized for fast, high-volume generation. A higher-end sibling, gemini-3-pro-image (Nano Banana Pro), exists for professional asset production, and you can swap the model string at any point to experiment with it.

Setting Up the Environment

Before we dive into generating images, it's crucial to set up our environment correctly. First, ensure you have access to the Gemini API by retrieving your API key. This key is essential for authenticating your requests to the API. You can set this key as an environment variable named GOOGLE_API_KEY . On CodeSignal, many libraries come pre-installed, but it's good practice to know how to install them on your own device. For this lesson, you'll need the google-genai library for accessing the Gemini API and the PIL library for image processing. You can install these using pip : Shell pip install google-genai pillow

Configuring the Gemini API Client

With the environment set up, the next step is to configure the Gemini API client. This involves initializing the client with your API key. The API key is retrieved from the environment variable GOOGLE_API_KEY . If the key is not found, the script will raise an error, prompting you to set it before proceeding. Here's how you can initialize the client: Python from google import genai from google.genai import types import os # Retrieve API key from system environment variable api_key = os.getenv("GOOGLE_API_KEY") if not api_key: raise ValueError("GOOGLE_API_KEY not found in environment variables. Set it before running the script.") base_url = os.getenv("GOOGLE_BASE_URL") if not base_url: raise ValueError("GOOGLE_BASE_URL not found in environment variables. Set it before running the script.") # Initialize the Gemini client client = genai.Client(api_key=api_key, http_options=types.HttpOptions(base_url=base_url,),) This setup ensures that your application can securely communicate with the Gemini API.

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Generating a Simple Image

Processing and Displaying the Generated Image

Once the image is generated, the next step is to process and display it. The response from a Gemini image model is a list of parts . Some parts may contain text (for example, a short description the model decides to include), and the part we care about contains the image as binary data inside inline_data . We loop over the parts, find the one carrying image bytes, and process it using the PIL library. Here's how you can handle the image data and save it to a file: from PIL import Image from io import BytesIO import os if response and response.parts: for part in response.parts: # The image data lives in the part's inline_data if part.inline_data is not None: image = Image.open(BytesIO(part.inline_data.data)) output_path = os.path.join("static", "images", "generated_image.png") os.makedirs(os.path.dirname(output_path), exist_ok=True) image.save(output_path) else: print("Error: Empty response or no images generated") This code snippet iterates over the response parts, opens the image carried by inline_data, and saves it to the specified directory. The PIL library is instrumental in handling image data efficiently. In this example, the BytesIO class plays a critical role in processing the image. The API returns the image data as raw bytes inside part.inline_data.data, and BytesIO allows us to treat this raw binary data as if it were a file. By passing the byte stream to Image.open(), the PIL library can read and manipulate the image directly from memory. This approach avoids the need to write the raw data to disk before processing, resulting in faster and more memory-efficient image handling. Here how the generated image will look like:

Python

from google.genai import types

prompt = "A serene sunset over a mountain range"

response = client.models.generate_content(
    model="gemini-3.1-flash-image",
    contents=[prompt],
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"],  # Ask the model for an image
        image_config=types.ImageConfig(
            aspect_ratio="16:9",  # Set aspect ratio
        ),
    ),
)

Python

from PIL import Image
from io import BytesIO
import os

if response and response.parts:
    for part in response.parts:
        # The image data lives in the part's inline_data
        if part.inline_data is not None:
            image = Image.open(BytesIO(part.inline_data.data))
            output_path = os.path.join("static", "images", "generated_image.png")
            os.makedirs(os.path.dirname(output_path), exist_ok=True)
            image.save(output_path)
else:
    print("Error: Empty response or no images generated")