Introduction to the Image Manager with FastAPI

Introduction to the Image Manager

Welcome to the second lesson of our course on building an image generation service with FastAPI . In our previous lesson, we created the PromptManager class to handle the formatting of prompts for our image generation process. Now, we'll build upon that foundation by creating the ImageManager class, which will be responsible for handling the images once they're generated. The ImageManager serves as a crucial component in our application architecture. While the PromptManager prepares the instructions for image generation, the ImageManager takes care of what happens after an image is created. Its primary responsibilities include: Storing generated images along with their associated prompts Converting images to a web-friendly format (base64) Providing access to the collection of stored images When we use the Gemini API, it returns generated image data as a native SDK object. By converting these image objects (or fallback PIL images used for testing) to base64 strings, we can easily embed them in HTML or send them as JSON responses in our API. Additionally, keeping track of which prompt generated which image allows us to maintain a history of generations. Let's dive into building this important piece of our image generation service.

Setting Up the Image Manager Class

To begin, we'll create a new file called image_manager.py in our app/models directory. This file will contain our ImageManager class. First, let's set up the basic structure and understand the imports we'll need: Python import base64 from PIL import Image from io import BytesIO class ImageManager: def __init__(self): self.images = [] Let's break down these imports: base64 : This module provides functions for encoding binary data to ASCII characters and decoding such encodings back to binary data. We'll use it to convert our images to a string format that can be easily transmitted over HTTP. PIL (Python Imaging Library): We import the Image class from PIL, which allows us to open, manipulate, and save many different image file formats. It provides a helpful fallback structure for handling custom images outside of the SDK. BytesIO : This is a class from the io module that implements a file-like interface for reading and writing bytes in memory rather than to a disk file. It's useful for saving our images as a buffer before encoding them. Our ImageManager class starts with a simple constructor that initializes an empty list called images . This list will store dictionaries containing information about each image, including: A unique identifier The prompt that generated the image The base64 -encoded image data This simple data structure allows us to keep track of all generated images and their associated metadata in memory.

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Converting Images to Base64 Format

One of the key functions of our ImageManager class is to convert image objects into a format that can be easily used in web applications. Base64 encoding is perfect for this purpose as it transforms binary data into a string of ASCII characters that can be included directly in HTML or JSON.

Let's implement the image_to_base64 method:

Python

def image_to_base64(self, image):
    try:
        # Handle Google GenAI SDK Image object
        if hasattr(image, 'image_bytes'):
            return base64.b64encode(image.image_bytes).decode("utf-8")
        
        # Fallback for standard PIL Images
        if not isinstance(image, Image.Image):
            raise TypeError("Expected a PIL Image object or Google GenAI Image")

        buffered = BytesIO()
        image.save(buffered, format="PNG")
        return base64.b64encode(buffered.getvalue()).decode("utf-8")
    except Exception as e:
        raise RuntimeError(f"Error processing image: {str(e)}")

def image_to_base64(self, image):
    try:
        # Handle Google GenAI SDK Image object
        if hasattr(image, 'image_bytes'):
            return base64.b64encode(image.image_bytes).decode("utf-8")
        
        # Fallback for standard PIL Images
        if not isinstance(image, Image.Image):
            raise TypeError("Expected a PIL Image object or Google GenAI Image")

        buffered = BytesIO()
        image.save(buffered, format="PNG")
        return base64.b64encode(buffered.getvalue()).decode("utf-8")
    except Exception as e:
        raise RuntimeError(f"Error processing image: {str(e)}")

This method takes either a Google GenAI Image object or a PIL.Image.Image object. Let's walk through what's happening:

We first check if the object has an image_bytes attribute (which the GenAI SDK returns). If so, we encode those raw bytes directly into a base64 string.
If not, we verify that it is a standard PIL Image. We then create a BytesIO buffer, save the PIL image to this buffer in PNG format, and encode the buffer's contents as base64 (decoding it to a UTF-8 string).
If any errors occur during this process, we catch them and raise a more informative RuntimeError.

The error handling is particularly important here because image processing can fail for various reasons, such as corrupted image data or memory issues.