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Introduction

Mastering PaliGemma opens up a world of possibilities for working with both images and text. PaliGemma is an advanced vision-language model developed by Google that excels in tasks such as image captioning, visual question answering, and object detection. By combining the power of the SigLIP image encoder with the Gemma text decoder, it delivers robust multimodal capabilities for industries like content creation and medical imaging. In this article, we’ll explore how PaliGemma transforms the way we interact with visual and textual data, making it an indispensable tool for developers and AI enthusiasts.

Prerequisites for PaliGemma

Alright, so you’ve probably heard of PaliGemma, this amazing tool that can handle both images and text, right? But before you jump into the fun stuff like image captioning, answering questions about images, or even doing object detection, there are a few things you’ll need to set up first.

Let’s start with some basic machine learning knowledge. If you already know your way around machine learning, especially when it comes to vision-language models (VLMs), you’re already on the right track. VLMs are pretty advanced because they combine both images and text to understand things. They process visual data and turn it into text, and understanding how these models work will help you make the most of PaliGemma.

Next up, let’s talk about programming skills—and not just any programming skills, but specifically Python. If you’ve ever worked with machine learning before, then Python is your best friend. You’ll be using it to work with machine learning libraries and models like PaliGemma, so it’s definitely something you’ll want to be comfortable with. If you’re already good with coding, debugging, and understanding how machine learning models are built, you’re in great shape. If not, it’s time to brush up on Python to get comfortable with all the techy stuff.

Now, let’s talk about dependencies. PaliGemma doesn’t run on its own—it needs a couple of key libraries to work. The two most important ones are PyTorch (which does all the heavy lifting for deep learning) and Hugging Face Transformers (this makes it super easy to work with pre-trained models). So, before you start exploring PaliGemma, you’ll need to install and update these libraries. It’s like setting up your toolkit before a big project.

When it comes to performance, if you want to get the most out of PaliGemma, a GPU-enabled system is definitely the way to go. Sure, you can run PaliGemma on a CPU, but trust me, a GPU will speed things up a ton—especially if you’re working with large datasets or fine-tuning the model for specific tasks. It’s kind of like trying to race a car on foot. Obviously, the car wins! The GPU will give you that extra boost.

Lastly, let’s talk about the dataset. To really get PaliGemma working, you need access to a vision-language dataset for testing or fine-tuning. This typically means a bunch of images paired with their descriptions—ideal for tasks like image captioning or visual question answering. If you’ve got a dataset ready, you’re already on your way!

Get all these prerequisites set up, and you’ll be ready to unlock the full power of PaliGemma. Whether you’re diving into content creation, medical imaging, or working on interactive AI systems, having everything in place will ensure you’re prepared to tackle those complex vision and language-based tasks.

Advances in Vision-Language Models (2021)

What is PaliGemma?

Imagine a world where images and text come together perfectly, where a machine can look at a picture and not only recognize it but also understand it, describe it, and even answer questions about it. That’s exactly what PaliGemma does. It’s a cutting-edge, open-source vision-language model—think of it as a smart AI that sees images and understands language, all at once.

So, how does it work? Well, PaliGemma takes inspiration from another model called PaLI-3, but it adds its own twist by combining the SigLIP vision model with the Gemma language model. These two components are the core of PaliGemma, and together, they unlock some seriously cool abilities. We’re talking about things like image captioning, where the model generates descriptions for pictures; visual question answering (VQA), where you can ask questions about an image and get answers; text recognition within images, and even tasks like object detection and segmentation. Basically, it can do anything that involves understanding both images and text.

What’s even more amazing is that PaliGemma comes with both pretrained and fine-tuned checkpoints. These are like ready-to-go versions of the model, and you can choose the one that fits your needs. These checkpoints are open-sourced in different resolutions, so you can jump right in without needing to start from scratch. Whether you’re working with images for a simple project or something more complex, PaliGemma has the flexibility to handle it.

At the heart of PaliGemma is the SigLIP-So400m image encoder. Now, this isn’t just any encoder. It’s a state-of-the-art (SOTA) model that can handle both images and text at the same time. It processes visual data and translates it into something that makes sense in the language of text. It’s like having a translator that understands both images and words at once—pretty cool, right?

On the flip side, the Gemma-2B model acts as the text decoder. It takes all the visual information that SigLIP processes and turns it into clear, meaningful language. This is where PaliGemma really shines. You get the perfect mix: SigLIP’s ability to see and understand images, and Gemma’s ability to generate meaningful text. Together, these models create a smooth, seamless experience for processing and generating multimodal data.

And here’s the real kicker: all of this is highly customizable. By combining SigLIP’s image encoding with Gemma’s text decoding, PaliGemma becomes a super flexible tool that can be easily adjusted for specific tasks like image captioning or referring segmentation. This is huge for developers and researchers. It opens up a whole world of possibilities for working with multimodal data—whether it’s creating interactive AI systems, building content creation tools, or diving deep into areas like medical imaging.

In short, PaliGemma brings together the power of image recognition and language generation in a way that hasn’t been this accessible before. Whether you’re a developer, a researcher, or just someone curious about AI, this model is here to push the boundaries of what’s possible with images and text.

PaliGemma: A Vision-Language Model for Multimodal AI (2025)

Source Overview of PaliGemma Model Releases

Let me tell you about PaliGemma and all the different ways you can use it for your projects. Imagine you’re working on something complicated that involves both images and text, and you need a model that can adjust to what you need. That’s where PaliGemma comes in, offering a variety of checkpoints—think of these as pre-built versions of the model, each fine-tuned for different tasks. Whether you’re just starting with basic tasks or diving deep into research, PaliGemma has your back. Let’s break it down:

Mix Checkpoints

These are the all-arounders. Mix Checkpoints are pretrained models that have been fine-tuned on a mix of tasks. If you’re just getting started or need something flexible for general-purpose work, these checkpoints are perfect. They let you feed in free-text prompts, making them super versatile for a wide range of tasks. However, they’re designed mainly for research, not production. But don’t worry, that’s a small price to pay for flexibility!

FT (Fine-Tuned) Checkpoints

Now, if you want to tackle more specific tasks, you’ll want to look at the FT (Fine-Tuned) Checkpoints. These models have been specially fine-tuned on academic benchmarks, making them perfect for certain jobs. Need a model that does image captioning perfectly or excels at object detection? These FT checkpoints are your best choice. Just keep in mind, they’re also research-focused and best for more specific tasks. Plus, they come in different resolutions, so you can pick the one that fits your needs.

Model Resolutions

Speaking of resolutions, let’s talk about the different options available for PaliGemma models. It’s kind of like picking the right camera for a photoshoot—you want the resolution that fits the task. Here are your options:

• 224×224 resolution: This is your go-to. It’s great for most tasks, offering a balance between performance and efficiency. Think of it as your all-purpose model.
• 448×448 resolution: Now we’re adding more detail. If you need to get into tasks that require more detailed image analysis, this resolution has you covered. More pixels, more precision.
• 896×896 resolution: This is for the big leagues. If you need fine-grained object recognition or even text extraction, this is the resolution you’ll need. But just like a high-performance car, it requires a lot more from your system.

Model Precisions

But wait, there’s more. You also need to consider model precisions. It’s kind of like choosing the right fuel for your machine—you’ve got different options depending on how much power (and memory) you need:

• bfloat16: This is the sweet spot, offering a good balance between performance and precision. It’s ideal for most tasks, where you don’t need to push things too far but still want solid results.
• float16: Want to save on memory while keeping decent performance? This precision is like a lean, mean computing machine.
• float32: This is your high-precision option, designed for tasks that need maximum accuracy. But be warned, it comes at the cost of needing more computational power—it’s like running a marathon with a bunch of extra gear.

Repository Structure

Now, let’s talk about how the repositories are set up. Every repository is like a well-organized toolbox, sorted by resolution and task-specific use cases. Here’s how it works:

• Each repository contains three versions (or revisions) for each precision type: float32, bfloat16, and float16.
• The main branch will have the float32 checkpoints, which are the highest precision.
• There are separate branches for bfloat16 and float16, giving you flexibility depending on your system and needs.

Compatibility

You also have flexibility in how you use PaliGemma. Whether you prefer working with ? Transformers or the original JAX implementation, there are separate repositories for each. This means that no matter what your setup is, you can integrate PaliGemma smoothly into your workflow.

Memory Considerations

One thing to keep in mind is that higher-resolution models, like the 448×448 and 896×896 versions, need a lot more memory. While these models will give you the detailed analysis you need for complex tasks like OCR (Optical Character Recognition), the quality improvement might not be huge for every task. For most use cases, the 224×224 resolution is your best bet—it provides a nice balance between quality and memory requirements without overloading your system.

The Bottom Line

So, there you have it. PaliGemma’s wide range of checkpoints, resolutions, and precisions lets you choose the right model for what you need. Whether you’re a researcher needing fine-tuned models for specific tasks or just looking for something flexible for general work, PaliGemma offers both power and adaptability. From content creation to medical imaging and even interactive AI systems, this model can do it all.

For more details, check the official research paper.PaliGemma: Versatile AI Models for a Range of Applications

Try out PaliGemma

Alright, let’s dive into the magic of PaliGemma! In this section, we’ll walk through how to use Hugging Face Transformers to run inference with PaliGemma. It’s easier than you think, and we’ll start by installing the libraries you’ll need to get everything up and running.

Step 1: Install the Necessary Libraries

First thing’s first, we need to install the libraries that will make this all happen. This ensures we’re working with the latest versions of the Transformers library and all the other dependencies. Ready? Let’s get started:

$ pip install -q -U accelerate bitsandbytes git+https://github.com/huggingface/transformers.git

Step 2: Accept the Gemma License

Before we can actually use PaliGemma, we need to get permission. Sounds serious, right? Well, it’s simple. You need to accept the Gemma license first. Just head over to the repository to request access. If you’ve already accepted the license, you’re good to go! Once that’s sorted, log in to the Hugging Face Hub with the following command:

from huggingface_hub import notebook_login
notebook_login()

Once you log in with your access token, you’re ready to start working with PaliGemma.

Step 3: Loading the Model

Now comes the fun part—loading the PaliGemma model. We’ll import the libraries and load the pre-trained model. At the same time, we need to figure out which device to run it on, whether it’s a GPU (fingers crossed!) or CPU. We’ll also load the model with the torch.bfloat16 data type to strike that perfect balance between performance and precision.

from transformers import AutoTokenizer, PaliGemmaForConditionalGeneration, PaliGemmaProcessor
import torch
device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)
model_id = “google/paligemma-3b-mix-224”
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16)
processor = PaliGemmaProcessor.from_pretrained(model_id)

Step 4: Processing the Input

Once everything is set up, we can start feeding in our data. The processor will take care of both the image and text inputs. It’s like the bridge between your data and PaliGemma, ensuring everything is prepped and ready for the model. Here’s how you do it:

inputs = processor(text=input_text, images=input_image, padding=”longest”, do_convert_rgb=True, return_tensors=”pt”).to(“cuda”)
model.to(device)
inputs = inputs.to(dtype=model.dtype)

Step 5: Generating the Output

With everything ready, it’s time for PaliGemma to work its magic. We’ll use the model to generate a text-based response based on the image and text we input. We use torch.no_grad() to ensure that no gradients are calculated, which is ideal for inference tasks where we’re only interested in the output.

with torch.no_grad():
output = model.generate(**inputs, max_length=496)
print(processor.decode(output[0], skip_special_tokens=True))

Here’s a fun example of what you might see as output:

How many dogs are there in the image? 1

Step 6: Loading the Model in 4-bit Precision

Now, let’s talk about 4-bit precision. Why? Well, if you want to run things faster and more efficiently, using lower precision can save you a lot of memory and computing power. This means you can run larger models or take on more complex tasks without overwhelming your system. To use 4-bit precision, we need to initialize the BitsAndBytesConfig like this:

from transformers import BitsAndBytesConfig
import torch
nf4_config = BitsAndBytesConfig(
   load_in_4bit=True, # Specifies that we want to load the model in 4-bit precision
   bnb_4bit_quant_type=”nf4″, # Defines the quantization type
   bnb_4bit_use_double_quant=True, # Allows for double quantization, optimizing memory
   bnb_4bit_compute_dtype=torch.bfloat16 # Specifies the data type for computation, which is bfloat16 for a balance of precision and performance
)

Step 7: Reloading the Model with 4-bit Configuration

Once we’ve got the configuration, we can reload the PaliGemma model, this time with the 4-bit precision. This ensures we’re saving resources but still getting solid performance. Here’s how to do it:

device = “cuda”
model_id = “google/paligemma-3b-mix-224”
model = PaliGemmaForConditionalGeneration.from_pretrained(
   model_id, torch_dtype=torch.bfloat16, quantization_config=nf4_config, device_map={“”: 0}
)
processor = PaliGemmaProcessor.from_pretrained(model_id)

Step 8: Generating the Output with 4-bit Precision

Now, we can generate the output again, this time with the 4-bit configuration for optimized memory and computational usage. It’s all about efficiency, baby!

with torch.no_grad():
output = model.generate(**inputs, max_length=496)
print(processor.decode(output[0], skip_special_tokens=True))

You’ll get the same awesome results, but now you’ve saved some resources. Here’s another example of the output:

How many dogs are there in the image? 1

The Takeaway

Using 4-bit precision allows you to optimize performance without sacrificing much in the way of accuracy. This is especially helpful when you’re running larger models or dealing with more complex tasks. By tweaking the precision settings, you can make PaliGemma work for you in an even more efficient way. Whether you’re diving into large datasets, fine-tuning the model, or working with intricate tasks, this flexibility lets you handle it all without stressing your system.

For more information on Hugging Face Transformers, check out the official documentation.
Always make sure to work with the latest versions of libraries for improved performance and compatibility.
Hugging Face Transformers Documentation

Load the Model in 4-bit

So, you’re looking to get the most out of the PaliGemma model, but you don’t want to overload your system with high computational demands? Here’s the trick: use 4-bit or 8-bit precision. By lowering the precision, you can make the model run faster and save a ton of memory, especially when you’re dealing with large models or systems that aren’t quite equipped to handle high-end performance. Let’s walk through how to make this magic happen.

Step 1: Initialize the BitsAndBytesConfig

First, we need to prepare the BitsAndBytesConfig. Think of this as your model’s instructions manual, telling it how to use 4-bit precision. This is where you configure things like the quantization type and other settings to make the model run efficiently at a reduced precision. Check out this simple code that initializes it:

from transformers import BitsAndBytesConfig
import torch

nf4_config = BitsAndBytesConfig(
    load_in_4bit=True,    # Specifies that we want to load the model in 4-bit precision
    bnb_4bit_quant_type=”nf4″,    # Defines the quantization type
    bnb_4bit_use_double_quant=True,    # Allows for double quantization, optimizing memory
    bnb_4bit_compute_dtype=torch.bfloat16    # Specifies the data type for computation, which is bfloat16 for a balance of precision and performance
)

By setting this up, you’re making sure that PaliGemma works efficiently, without eating up too much memory, while still delivering solid performance. This is crucial, especially when you’re working on tasks that require heavy computation.

Step 2: Reload the Model with the 4-bit Configuration

With the configuration in place, it’s time to reload the PaliGemma model with the 4-bit precision setup. We’re going to load PaliGemmaForConditionalGeneration and its associated PaliGemmaProcessor from the pretrained model repository. Plus, we’ll make sure it runs on your GPU if you’ve got one available. Here’s the code that makes it happen:

from transformers import AutoTokenizer, PaliGemmaForConditionalGeneration, PaliGemmaProcessor
import torch

device = “cuda”    # Ensure that the model is loaded onto the GPU if available
model_id = “google/paligemma-3b-mix-224”    # Specify the model identifier
model = PaliGemmaForConditionalGeneration.from_pretrained(
    model_id,    # Load the model with bfloat16 precision
    torch_dtype=torch.bfloat16,    # Load the model with bfloat16 precision
    quantization_config=nf4_config,    # Apply the 4-bit precision configuration
    device_map={“”: 0}    # Specify the device to load the model onto (in this case, GPU 0)
)
processor = PaliGemmaProcessor.from_pretrained(model_id)

What’s happening here? You’ve got the model loading up with 4-bit precision and ready to work on your GPU, ensuring everything runs smoothly. If you’re on a CPU, it will just default to that, but if you’ve got a GPU, the model will take advantage of that extra power to speed things up.

Step 3: Generate Output with the Model

Now that the model is loaded and ready to go, it’s time to put it to work. We’ll process your inputs, which might include both text and images, and let the model generate a response. Here’s how you can generate output with PaliGemma:

with torch.no_grad():    # Disable gradient computation during inference to save memory
    output = model.generate(**inputs, max_length=496)    # Generate the model output
    print(processor.decode(output[0], skip_special_tokens=True))    # Decode the output and print the result

This block of code will give you the answer to a question about the image, based on the model’s inference. For example, let’s say you ask, “How many dogs are there in the image?” Here’s the kind of response you’d get:

Example Output:

How many dogs are there in the image? 1

By running this with 4-bit precision, you’re making the whole process much more efficient. You’re saving memory, which means you can handle larger datasets and more complex tasks without worrying about your system getting bogged down.

The Power of 4-bit Precision

Using 4-bit precision models isn’t just about saving space—it’s about making things faster and more accessible. While you’re cutting down on the memory usage and computational load, you’re still getting solid performance. For many applications, this balance is just perfect. Whether you’re tackling complex projects or just testing things out, loading PaliGemma in 4-bit is a great way to optimize your resources.

By using 4-bit precision, you’re not just working smarter; you’re also making PaliGemma work faster and more efficiently. Whether you’re diving into content creation, medical imaging, or building interactive AI systems, you’ll find that this small tweak gives you big advantages.

Efficient Quantization of Neural Networks (2020)

Using PaliGemma for Inference: Key Steps

Imagine you’re working on a project where you need to get a machine to understand both text and images. Whether it’s answering questions about pictures or generating captions, PaliGemma is the tool you need. But how does it work its magic? Let’s take a journey through the steps of using PaliGemma for inference—what goes on under the hood, and how text and images are processed to generate the answers you’re looking for.

Step 1: Tokenizing the Input Text

The first step in this process is all about getting the input text ready for PaliGemma to work with. Text in its raw form can be a bit messy for a machine to understand, so we need to tokenize it. In simple terms, tokenization breaks down the text into smaller, manageable pieces (tokens). Here’s what happens during this process:

• A special <bos> (beginning-of-sequence) token is added at the start of the text. This is like a little flag that says, “Hey, this is where the sequence begins.”
• Then, a newline token n is added to the end of the text. This one’s important because it’s part of the model’s training input. It helps maintain the consistency and structure of the data, just like keeping chapters neatly labeled in a book.

With the text tokenized and ready, we can move on to the next step.

Step 2: Adding Image Tokens

Now that the text is prepared, we need to do the same for the image. But instead of just tossing the image into the model, we have to tell PaliGemma how to associate the image with the text. Enter image tokens.

The tokenized text gets a little extra: a number of <image> tokens are added. These tokens are like placeholders for the image content. They help the model connect the dots between the visual data and the text. The number of image tokens varies depending on the resolution of the image. Here’s how it breaks down:

• 224×224 resolution: 256 <image> tokens (calculated as 224/14 * 224/14).
• 448×448 resolution: 1024 <image> tokens.
• 896×896 resolution: 4096 <image> tokens.

Step 3: Memory Considerations

Okay, here’s the thing: while adding image tokens is important, larger images can really increase the memory requirements. Bigger images mean more tokens, and more tokens require more memory. While this is awesome for detailed tasks like Optical Character Recognition (OCR), the quality improvement for most tasks might be pretty small. So, before opting for higher resolution, it’s a good idea to test your specific tasks to see if the extra memory is worth it.

Step 4: Generating Token Embeddings

Once both the text and image tokens are ready, we pass the whole thing through the model’s text embeddings layer. This step transforms the tokens into something the model can really work with—high-dimensional token embeddings. These embeddings are like the model’s way of understanding the meaning of the text and image data combined.

The result? 2048-dimensional token embeddings that represent the semantic meaning of both the text and the image. It’s like turning the text and image into a secret code that the model can crack!

Step 5: Processing the Image

Now that the tokens are ready, it’s time to process the image. But first, we need to resize it. Think of resizing as a fit-to-frame operation—making sure the image is the right size for the model to handle. We use bicubic resampling to shrink the image while keeping its quality intact.

Once the image is resized, it passes through the SigLIP Image Encoder, which turns the image into 1152-dimensional image embeddings for each patch of the image. These are then projected to 2048 dimensions to align perfectly with the text token embeddings. This ensures the model can process both text and images together as one seamless input.

Step 6: Combining Image and Text Embeddings

Here comes the fun part. The text embeddings and image embeddings are now ready, and it’s time to combine them. By merging the two, we’re telling the model: “Here’s the complete picture—text and image, hand in hand.” This combined input is what the model uses for autoregressive text generation. In simple terms, the model will generate the next part of the text one step at a time, considering both the image and the text.

Step 7: Autoregressive Text Generation

What’s autoregressive text generation? It’s when the model generates each token in a sequence, using the previous ones as context. Imagine writing a story where each sentence builds upon the last. That’s how the model works, using all the previous tokens to predict what comes next. Here, full block attention is used, so the model pays attention to all the input data, including the image, text, <bos>, prompt, and n token.

To make sure everything stays in order, a causal attention mask ensures the model only uses the earlier tokens to generate the text, not any of the future ones.

Step 8: Simplified Inference

The best part? You don’t have to manually manage all of this complexity. PaliGemma handles the hard stuff—tokenization, embedding generation, and attention masking—automatically. All you need to do is call on the Transformers API, and PaliGemma will handle the rest. It’s like having an advanced assistant that takes care of all the technical stuff while you focus on your task.

With the API, you can easily use PaliGemma to perform complex tasks like image captioning, visual question answering, and much more. It’s powerful, intuitive, and ready to roll whenever you need it.

And there you have it! With just a few steps, PaliGemma works its magic, transforming both images and text into something the model can understand and respond to. Ready to give it a try?

PaliGemma: A Multi-modal Transformer for Text and Image Processing (2025)

Applications

Imagine you have a powerful tool that can not only understand images but also text, and seamlessly combine the two. That’s exactly what PaliGemma, a vision-language model, does. It’s like a translator between pictures and words, making it possible to answer questions about images, generate captions, and even help automate tasks that involve both visual and textual data. Let’s take a walk through some of the fascinating ways PaliGemma can be used across industries.

Image Captioning

One of the most exciting applications of vision-language models is image captioning. Picture this: you upload a photo, and instead of manually writing a caption, the model automatically generates a detailed, descriptive caption for you. This is a game-changer, especially for making content more accessible. For visually impaired individuals, this ability can significantly enhance their experience. But that’s not all—it also improves how we interact with content on platforms like social media or e-commerce sites, where a description can make a huge difference in the user experience.

Visual Question Answering (VQA)

Then there’s Visual Question Answering (VQA). Ever looked at a picture and wondered about something in it? Well, with PaliGemma, you can ask questions like, “What color is the car in the image?” or “How many people are in the picture?” And it will provide you with an answer, all based on the visual data. This makes search engines smarter, helps virtual assistants understand your queries better, and brings an interactive dimension to education. It’s like having a conversation with the image itself!

Image-Text Retrieval

Imagine searching for an image online by typing a description. Now, you don’t need to go through hundreds of images—PaliGemma does the work for you. With image-text retrieval, you can search for images using descriptive text, and the model will bring back relevant results. This functionality is fantastic for content discovery and searching in multimedia databases, especially when you need to find that perfect picture to match a keyword or theme.

Interactive Chatbots

Now, chatbots are becoming a lot more intelligent, thanks to vision-language models. With PaliGemma, chatbots are no longer just text-based; they understand both text and images. This makes them smarter and more engaging, providing responses that take into account visual content. Imagine asking a chatbot about a product, and it not only gives you text-based information but also uses an image to enhance the experience. This makes for a much more personalized and contextually relevant user experience.

Content Creation

Let’s say you’re a content creator or marketer. Instead of manually writing descriptions, PaliGemma can analyze images and automatically generate captions, summaries, or even full stories. This is a huge time-saver for industries like marketing, storytelling, and anything that requires quick content creation. Whether you’re creating blog posts, social media captions, or product descriptions, this model can help keep things moving efficiently.

Artificial Agents

Ever wondered how robots or virtual agents can understand their environment? With PaliGemma, these agents can interpret both text and visual data in real-time. Imagine a robot navigating your home, analyzing objects, and making decisions about its surroundings. This ability is game-changing in fields like robotics, autonomous vehicles, and smart homes. These agents can perform tasks, make real-time decisions, and operate much more intelligently by integrating visual and textual data.

Medical Imaging

In healthcare, PaliGemma can help interpret medical images like X-rays or MRIs. By combining these images with clinical notes or reports, the model assists radiologists and medical professionals in making more accurate diagnoses and treatment plans. This integration helps streamline workflows, improves accuracy, and ultimately makes medical decision-making faster and more reliable.

Fashion and Retail

When it comes to shopping, personalization is key. PaliGemma takes your visual preferences into account and provides personalized product recommendations based on both your past choices and textual descriptions. This is a huge win for fashion and retail industries, enhancing the shopping experience and improving conversion rates. You know that feeling when a store just knows what you want? This is how it happens.

Optical Character Recognition (OCR)

You’ve probably heard of OCR (Optical Character Recognition)—it’s the technology that lets you extract text from images. But implementing it can get tricky, especially when dealing with poor-quality images or distorted text. That’s where PaliGemma shines. By using advanced image recognition and text generation techniques, it handles OCR challenges with ease. Whether you’re digitizing old documents or invoices, PaliGemma can make this process smoother and more accurate.

Educational Tools

Now, let’s talk about education. Imagine interactive learning materials where text and images are combined to help students learn more effectively. With PaliGemma, students can engage with content that mixes visual aids with textual explanations, quizzes, and exercises. Whether it’s for primary education or online learning platforms, this model provides a more dynamic and engaging way to absorb knowledge.

Expanding Potential Applications

The possibilities with vision-language models like PaliGemma are endless. As technology evolves, so too do the applications. Researchers and developers are continuously discovering new ways to integrate these models across industries—whether it’s in entertainment, artificial intelligence, or beyond. The future holds exciting opportunities, and we’re only scratching the surface of what PaliGemma can do.

As PaliGemma continues to evolve, it’s clear that it’s not just changing the way we interact with images and text but revolutionizing how industries approach tasks that require a blend of the two. Whether you’re in content creation, healthcare, or interactive AI, this model is setting the stage for a new era of intelligent, multimodal systems.

PaliGemma: Vision-Language Model

Conclusion

In conclusion, PaliGemma is a powerful and versatile vision-language model that merges visual and textual data to revolutionize tasks such as image captioning, object detection, and visual question answering. By leveraging the SigLIP image encoder and the Gemma text decoder, PaliGemma delivers advanced multimodal capabilities that are transforming industries like content creation, medical imaging, and AI systems. As this technology evolves, we can expect even more innovative applications, further driving progress in fields that require seamless integration of images and text. Stay ahead of the curve by mastering PaliGemma and harnessing its potential to elevate your AI projects.For those looking to push the boundaries of what’s possible with multimodal models, PaliGemma is a tool that holds immense promise for the future.

Unlock GLM 4.1V Vision-Language Model for Image Processing and OCR (2025)

Introduction

“Object detection is an essential task in modern AI, and with DETR (Detection Transformer), leveraging deep learning and transformer architecture, this process becomes more efficient than ever. By removing traditional components like anchor boxes and non-maximum suppression, DETR streamlines the detection pipeline while improving accuracy and flexibility. This article explores how DETR’s unique design—combining CNN feature extraction and transformer-based encoding and decoding—provides reliable, real-time object predictions across industries like autonomous vehicles, retail, and healthcare.”

What is Object Detection?

Picture this: you’re in a busy city, with cars zooming by, people strolling on the sidewalks, and dogs chasing after tennis balls. Now, imagine trying to keep track of all that movement, figure out where the cars are, spot the pedestrians, and make sure that dog doesn’t run into traffic. That’s pretty much what object detection does, but in digital images and videos. It’s like the eyes of a self-driving car or a security camera—it spots and locates objects in a sea of visual data.

Object detection is all about finding things like people, cars, animals, or buildings in images or video feeds. It’s like when your smartphone can spot faces in your photos. Or when an app tells you what’s in the frame of a picture. Whether it’s a self-driving car recognizing a pedestrian about to cross the road or a security camera catching an intruder, this technology is everywhere. It powers things like autonomous driving, helping cars find lanes, recognize other vehicles, and avoid hitting pedestrians. And it doesn’t end there—it’s also used in video surveillance, where cameras watch entire environments, and in image search engines that help you find exactly what you’re looking for by scanning pictures.

Now, let’s get into how this magic happens behind the scenes. The key tech behind object detection includes machine learning and deep learning—two ways that computers learn to identify objects without needing anyone to spell it out for them. In machine learning, we train systems with labeled datasets, like showing it thousands of pictures of cats and dogs, so it can tell the difference. It learns by looking at patterns in these images, like how a dog’s ears are shaped differently than a cat’s. The cool part? The system gets better over time, just like you get better at recognizing faces the more you see them.

But here’s where it gets interesting—deep learning takes things to the next level. It uses neural networks (basically, layers of artificial neurons that mimic how our brain works) to improve the detection process. With deep learning, we add more layers to this network, making it even smarter. Instead of just recognizing basic patterns, it starts to understand more complex details about objects. So, when the model looks at an image, it’s not just saying, “That’s a car.” It’s figuring out what kind of car it is, where it is in the image, and whether it’s moving or still. This is super helpful when analyzing medical images for signs of disease or when robots need to navigate spaces, where accuracy is critical.

Thanks to deep learning and CNNs (Convolutional Neural Networks), object detection has become way more accurate and reliable. These technologies have opened up new possibilities in fields like robotics, where robots need to recognize objects around them, and healthcare, where doctors use imaging systems to spot tumors or other problems. The more data these systems process, the better they get at detecting objects in ways we never thought possible.

So, whether you’re looking at a transformer model like DETR (Detection Transformer) or diving into the details of a CNN, object detection is changing industries with its ability to “see” and understand the world like we do. It’s like having a smart assistant that can scan the environment, recognize things, and even make decisions based on what it sees—pretty cool, right?

Nature article on deep learning advancements

How Does Object Detection Work

Imagine you’re standing on a busy street—cars speeding by, people crossing the road, and dogs chasing after tennis balls. Your brain quickly processes all that activity, figuring out what’s what. Now, picture a computer trying to do the same thing with a photo or video. That’s where object detection steps in, a technology that helps machines see and understand images just like we do.

But here’s the thing: object detection isn’t a one-step process. It’s like solving a puzzle, piece by piece. It involves several stages, each helping the system break down the image and figure out what’s in it. These stages include feature extraction, object proposal generation, object classification, and bounding box regression. Each step is crucial in helping the system figure out not just what objects are there, but exactly where they are in the image or video. Let’s break down how each step works:

Feature Extraction

First, the machine needs to understand the basic building blocks of the image. This is where feature extraction comes in. The process starts with a Convolutional Neural Network (CNN), a type of deep learning model that’s great at spotting key patterns in images. It’s like when you look at a photo and quickly notice the edges of a car or the curves of someone’s face. The CNN does something similar—it learns to recognize edges, shapes, textures, and other visual details that help distinguish one object from another. The cool part? The CNN doesn’t need anyone to tell it exactly what to look for. It learns from tons of images, getting better at recognizing things over time. It’s kind of like how the more you practice identifying objects, the better you get at spotting them!

Object Proposal Generation

Once the CNN has picked out the key features, the next step is figuring out where the objects might be. This is where object proposal generation comes in. The system needs to suggest areas in the image that could contain something interesting. Think of it like a detective marking spots on a map where clues might be hidden. One technique used here is selective search, which carefully scans the image and looks for areas that are likely to hold objects. The goal is to narrow the focus, so the system doesn’t have to analyze the entire image at once. By isolating potential object regions, it speeds up the process and cuts out unnecessary noise.

Object Classification

Now that we have the possible object areas, it’s time to figure out what each of them actually is. Is that spot in the image a car? A person? Or maybe a dog running across the street? This is object classification, and it’s usually done using machine learning algorithms, like Support Vector Machines (SVMs). These classifiers have been trained to recognize different types of objects. Once the system identifies a region, it compares the features it sees in that region with what it’s learned to recognize. If it finds a match, it labels that region—whether it’s a person, a car, or something else.

Bounding Box Regression

Once the object is classified, it’s time to fine-tune the box around it. Bounding box regression helps with this. It adjusts the initial box around the object to make sure it fits perfectly—no more, no less. Think of it like drawing a box around a car in a photo: you want the box to cover the whole car without cutting off any part of it. The regression model learns to adjust the box’s size and position, ensuring the object is fully captured. This makes future detections more accurate.

Putting It All Together

When all these steps come together, the system can detect and locate multiple objects within an image or video. The ability to spot, classify, and perfectly box in objects is what makes object detection so important. It’s used in everything, from autonomous vehicles, where self-driving cars need to spot pedestrians and other cars, to security systems that can automatically watch environments for suspicious activity. It’s even used in image search engines, where it helps categorize and find images based on their visual content.

Thanks to advancements in machine learning and deep learning, these object detection systems are becoming faster and more accurate. As these technologies get even better, they’re helping create smarter systems, from robots in warehouses to AI-powered health diagnostics. It’s an exciting field that’s making the digital world much more understandable and navigable—just like how we use our eyes to understand the world around us.

Deep Learning for Object Detection

DETR: A Transformer-Based Revolution

Picture this: you’re driving through a busy city, and your car is weaving through traffic with ease, dodging pedestrians, other vehicles, and even cyclists, all thanks to object detection. But what if I told you the tech behind this system is evolving in a way that makes everything simpler and smarter? Enter DETR (Detection Transformer)—a groundbreaking model in deep learning that’s changing how we handle object detection and panoptic segmentation. Unlike traditional systems, which rely on multiple manual steps, DETR brings something much more powerful to the table: the transformer architecture.

Let’s break it down: DETR is an end-to-end trainable deep learning model, specifically designed for object detection. What does that mean? Well, when you feed it an image, it doesn’t just process it in pieces, using one step for feature extraction and another for classification—it does everything all at once. The result? You get bounding boxes and class labels for every object in the image, without all the clutter and complexity of traditional systems.

Here’s the beauty of it: instead of relying on a bunch of hand-crafted components for tasks like feature extraction or object proposal generation, DETR integrates them into one smooth, streamlined network. This makes everything simpler, easier to manage, and—most importantly—faster. No more juggling between different parts of the pipeline. With transformers at its core, DETR simplifies the complexities of object detection while boosting performance.

Now, let’s talk about what makes DETR stand out. Traditional object detection systems, like YOLO or Faster R-CNN, often rely on things like anchor boxes and Non-Maximum Suppression (NMS) to detect objects. You’ve probably heard of anchor boxes before—those predefined boxes of different shapes and sizes that help the system figure out where objects might be in the image. They help the model predict the object’s location and size. But here’s the catch: these boxes need to be manually adjusted, and if you don’t get it right, they can mess up the accuracy, especially for smaller objects. It’s a bit like trying to fit a square peg into a round hole—you’ve got to get it just right, and it’s tricky and often inconsistent.

Then, there’s NMS—this process removes duplicate boxes around the same object. It picks the one with the highest confidence and throws out the rest. While that sounds good in theory, NMS brings its own set of problems. Setting the right threshold for confidence isn’t easy, and if you get it wrong, it could mess up the final detection. It’s like setting your alarm too early in the morning—you could wake up before it’s really time, or even worse, not wake up at all.

Now, here’s where DETR flips the script. It completely does away with both anchor boxes and NMS. Instead of working with a set of predefined boxes, DETR uses a set-based global loss function for object detection. This means that instead of adjusting anchor boxes or using NMS to filter out duplicate boxes, DETR detects objects all at once, in parallel. This ensures that each object is detected only once and helps the system work more efficiently and accurately. You don’t have to worry about fine-tuning anchor boxes or getting NMS thresholds right. It’s like cutting out all the unnecessary steps and letting the system do its magic on its own.

By switching to this set-based approach, DETR also reduces the need for task-specific engineering, simplifying the model and making it easier to use. The big benefit here is that DETR doesn’t rely on manual adjustments. It’s all automated, so you don’t have to keep tweaking the system for every new image. Plus, since transformers handle all the predictions at once, DETR reduces the complexity of traditional systems even more. Sure, the lack of anchor boxes might make it harder to detect really small objects, but this trade-off is more than made up for by the fact that you no longer need to adjust NMS thresholds. It’s a win-win.

And here’s the kicker: DETR’s end-to-end trainability means it’s not just faster—it’s also more efficient. It’s designed to train on large datasets without needing manual intervention every step of the way. That’s huge because it makes the model more accessible and flexible. Whether you’re using it in autonomous vehicles, where real-time detection is crucial, or in medical imaging, where accuracy and speed can make a huge difference, DETR’s simplicity and power are hard to beat.

In the world of object detection, DETR is a major leap forward. By using transformers to streamline the process, it’s making detection not only faster but smarter. The idea of simplifying complex steps into one unified process opens up new possibilities for faster, more reliable object detection across many industries. Whether you’re training self-driving cars or developing smart surveillance systems, DETR is a game-changer in how we understand and use deep learning.

Detection Transformers (DETR): End-to-End Object Detection with Transformers

Novel Architecture and Potential Applications

Let’s take a stroll through the world of DETR (Detection Transformer), where things are about to get a whole lot easier. The heart of this groundbreaking model is its architecture, which has a cool trick up its sleeve: attention mechanisms. Now, you might be wondering—what does that mean? Well, here’s the magic: these mechanisms help the model focus on specific parts of an image when making a prediction. It’s like when you’re in a crowded room and you can only focus on one conversation at a time. This focus not only boosts the accuracy of object detection but also makes it much easier to understand why the model made that decision. And let’s be honest—understanding what the model is focusing on helps us improve it, spotting any potential biases and making it work even better.

The real game-changer here is that DETR uses transformer technology, which was originally created for natural language processing (NLP). That’s right! Transformers, which we usually associate with language models, are now stepping into the world of computer vision and completely changing the way we detect objects in images. This new approach adds transparency, which is a huge win for researchers and developers. No more guessing why the system detected a dog or a car. With the model’s attention-based predictions, you get a clear view of how it’s working behind the scenes, making it much easier to trust.

But DETR isn’t just all talk. It’s got some real-world applications across various industries, and it’s making a huge impact in areas where object detection used to be a tricky and error-prone task. Let’s check out where DETR is already making waves:

• Autonomous Vehicles: Imagine you’re in a self-driving car, cruising down the road. The car needs to understand the environment in real-time—pedestrians crossing the street, cars changing lanes, traffic signs, and more. This is where DETR shines. Its end-to-end design reduces the need for manual engineering, which is a huge benefit in the fast-moving world of self-driving cars. The transformer-based encoder-decoder architecture lets the system understand the relationships between different objects in the image, helping the car make quick, accurate decisions. Whether it’s recognizing a stop sign or avoiding a pedestrian, DETR ensures these vehicles can navigate complex environments safely and precisely.
• Retail Industry: Things move fast in retail—inventory changes, products get rearranged, and customer traffic is unpredictable. DETR can handle it all. Its set-based loss function allows it to detect a fixed number of objects in real-time, even when the number of objects changes. This makes it perfect for managing real-time inventory and surveillance. It can track products on shelves, monitor stock levels, and help businesses keep everything in check. This level of automation means better customer service and smoother operations, and with object detection working in the background, retail stores can run more efficiently.
• Medical Imaging: Now, let’s move into healthcare. Detecting anomalies in medical images can be tricky, especially when trying to identify multiple instances of the same issue or spotting subtle variations. This is where DETR’s architecture really shines. Traditional object detection models often struggle with these tasks because they rely on predefined anchor boxes and bounding boxes. But DETR is different. By getting rid of these anchor boxes, it can better identify and classify anomalies in medical scans, like spotting tumors or other health issues. This makes DETR a powerful tool for doctors, improving diagnostic accuracy and leading to better patient outcomes.
• Domestic Robots: Picture a robot in your home—maybe it’s cleaning up or fetching a snack from the kitchen. The challenge for robots in everyday environments is that the number of objects and their positions are always changing. But with DETR, this unpredictability is no problem. The model can classify and recognize objects in real-time, making it perfect for tasks like cleaning or helping with household chores. It allows robots to interact more effectively with their environment, adapting to new objects or changes without skipping a beat. Whether it’s moving obstacles or just cleaning the floor, DETR makes sure the robot’s actions are accurate and efficient.

The beauty of DETR is in its ability to simplify the object detection process while bringing accuracy and flexibility to industries from self-driving cars to healthcare and home robotics. Its transformer architecture and use of attention mechanisms not only make the system easier to understand, but also help developers and researchers trust the model’s predictions. It’s an exciting time in the world of deep learning, and DETR is showing us that the future of object detection is here—and it’s more capable than ever before.

For further details, you can explore the full study on DETR.
DETR: A New Paradigm for Object Detection

Set-Based Loss in DETR for Accurate and Reliable Object Detection

Imagine you’re in charge of organizing a giant pile of photos, and your job is to match each photo with a label—a car, a person, a dog, or a street sign. Sounds simple, right? But here’s the catch: each label only fits one photo, and some photos might not match any label at all. So, how do you make sure you’re matching things up correctly every time? Well, DETR (Detection Transformer) has a smart solution for this, using something called a set-based loss function. This clever feature helps DETR make super-accurate predictions, ensuring the right labels match the right objects in an image. Let’s break it down.

First off, the set-based loss function makes sure that each predicted bounding box—basically the box drawn around a detected object—matches exactly one real box (the “correct” box in the image). So, each object has to be paired with the right label, and DETR makes sure no object is left out or misidentified. It’s like playing a matching game where each piece only fits one spot, and if you try to force it into the wrong one, the system won’t allow it.

To get this perfect match, the system uses a cost matrix, a mathematical tool that measures how well the predicted boxes align with the true ones. The cost matrix looks at several factors, like whether the object was classified correctly and how well the predicted box fits the object’s shape and position. The more accurate the match, the lower the cost. But here’s the cool part: DETR doesn’t just pick any match—it optimizes the process using the Hungarian algorithm.

You might be thinking, “What’s that?” Well, the Hungarian algorithm is like a pro at making sure the matchups are as accurate as possible. It minimizes the “cost,” meaning it pairs each prediction with the real box that makes the most sense. It checks everything—how well the object is classified and how closely the box fits the object. If the algorithm can’t find a good match for a predicted box, it’s marked as “no object.” Even in these cases, the system learns from the mismatch, getting better at making predictions next time.

Once all the potential matches are evaluated, the individual classification losses (how wrong the model was in predicting the object) and bounding box losses (how far off the predicted box was from the true one) are added together. This final set-based loss is used as feedback to guide the model toward making more accurate detections in the future. So, it’s like a self-correcting mechanism that gets better with every pass.

But here’s where DETR really stands out: by evaluating the entire set of predicted objects in parallel, the model doesn’t just focus on one object at a time. It looks at everything at once, making sure all objects in the image are detected with accuracy and consistency. It’s like making sure all the pieces of a puzzle fit perfectly—DETR doesn’t settle for just a few good matches; it aims for the entire image to be correctly classified.

This global evaluation approach is a game-changer, letting DETR make predictions that are not just accurate but also contextually consistent. Every prediction is carefully paired with its corresponding ground truth, making sure objects are both identified and located properly. So, when it comes to real-world applications—whether it’s autonomous driving, surveillance systems, or even medical imaging—DETR’s ability to provide accurate, reliable detections is key to its success.

In summary, the set-based loss function is what makes DETR such a powerhouse in object detection. By using bipartite matching and the Hungarian algorithm, it ensures each prediction is uniquely matched to its ground truth, improving both the accuracy and consistency of the model. This robust mechanism makes DETR incredibly reliable, enabling it to handle even the most complex environments with ease. Whether you’re dealing with a busy street scene or scanning medical scans, DETR’s innovative approach makes object detection as accurate and efficient as ever.

DETR: End-to-End Object Detection with Transformers

Overview of DETR Architecture for Object Detection

Imagine you’re a detective, trying to make sense of a chaotic scene. Cars whizzing by, people walking around, and a stray dog darting across the street. You need to quickly figure out what’s happening, but you don’t have the time to examine every tiny detail. Now, picture DETR (Detection Transformer) as your trusted assistant—someone who can quickly analyze the scene and tell you exactly what’s going on.

What makes DETR so powerful is its architecture, which takes a completely different approach to object detection than traditional models. Instead of relying on a mix of complex manual processes to extract features and analyze the image, DETR uses transformer architecture to automatically learn everything. No more fussing with task-specific engineering—it’s a smooth, efficient model that does the hard work for you.

The first step in the DETR process is similar to how our eyes work. The image is fed into the Convolutional Backbone, which is a CNN (Convolutional Neural Network). This part of the system scans the image for key features—edges, shapes, textures, and so on—just like how you might instantly spot a red car or a person standing on the sidewalk. Once the CNN has extracted those features, it passes them to the next step: the Transformer Encoder. Think of the encoder like the brain’s first reaction to the image. It doesn’t just look at the raw features; it starts figuring out how everything is connected, how objects relate to one another, and how they interact in space. It’s like solving a puzzle and understanding how the pieces fit together.

Now, things get a bit more exciting. The next step is the Transformer Decoder. This is where the real magic happens. The decoder receives a set of learned position embeddings, also known as object queries. These queries are like searchlights, guiding the decoder to specific parts of the image that might have an object. This helps the model focus on different areas of the image and refine its predictions. And here’s the cool part: the decoder’s output goes into a shared feed-forward network (FFN), which makes the final decision. It predicts the object’s class and its bounding box. So, if it detects a car, it says, “Here’s the car, and here’s exactly where it is in the image.” If it’s not sure, it says, “No object here.”

One of DETR’s most powerful features is how it uses object queries. These queries are learned during training and allow the model to focus on specific areas of the image, making its predictions more accurate. Imagine trying to solve a puzzle with pieces that don’t always fit the same way. Object queries help the decoder zoom in on the exact part of the image that matches each object, making the predictions more reliable. It’s like having a super-focused radar that locks in on what’s important.

But that’s not all. The next big innovation in DETR is how it uses multi-head self-attention. Self-attention lets the model focus on multiple parts of the image at once, which is super helpful when objects are scattered, overlapping, or complex. Instead of just focusing on one part of the image, DETR uses several attention heads to analyze different views of the same image at the same time. This multi-view approach helps DETR understand the complex relationships between objects in the scene. Think of it like a team of detectives each taking a different angle on the case, then coming together to share their findings for a complete understanding.

By using this self-attention mechanism and multi-head attention, DETR automates the entire object detection process. It doesn’t just extract features, predict objects, and label them. It does all of this in parallel with one unified approach, making it faster and more accurate than older methods. Whether you’re dealing with fast-moving cars on the street or complex medical images, DETR’s efficiency makes it the perfect solution for any deep learning task that requires quick, accurate detection.

So, in short, DETR uses transformers to optimize every step of the object detection process—from the first feature extraction to the final detection. With its self-attention and object queries, DETR is able to understand images more deeply, resulting in faster and more accurate predictions. Whether it’s used for autonomous vehicles, medical imaging, or anything in between, DETR’s innovative design makes it a game-changer in the world of computer vision.

For more details, refer to the paper: DETR: End-to-End Object Detection with Transformers.

Using the DETR Model for Object Detection with Hugging Face Transformers

Imagine you’re looking at a picture, and your task is to figure out what’s in it—cars, people, animals, maybe even traffic signs—all at once. Seems like a big job, right? Well, that’s where DETR (Detection Transformer) comes in. Powered by transformer technology, DETR is a game-changer in the world of object detection. It takes the guesswork out and makes identifying objects in images smoother than ever before. Rather than manually piecing together several steps like traditional methods, DETR handles everything in one go, using a smart mix of deep learning and transformers.

Here’s how it works: The DETR model, specifically the facebook/detr-resnet-50 version, combines a ResNet-50 CNN backbone with a transformer encoder-decoder setup. So, what does that mean in simple terms? Well, DETR can take an image, analyze it smartly, and figure out exactly what’s in the image—whether it’s a person, a car, or a dog. The system learns from a huge dataset called COCO (Common Objects in Context), which contains tons of labeled images with everything from people to animals to vehicles. By learning from such a diverse range, DETR becomes an expert at detecting real-world objects in any image.

Let’s break down some code to see how all this works. Imagine you want to put this model to work. First, you’ll need to load the necessary libraries—like Hugging Face Transformers, torch, and PIL (Python Imaging Library). These tools help handle image data, load the model, and let the model do its job of detecting objects in real-time.

from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import requests
url = “http://images.cocodataset.org/val2017/000000039769.jpg”
image = Image.open(requests.get(url, stream=True).raw)
# you can specify the revision tag if you don’t want the timm dependency
processor = DetrImageProcessor.from_pretrained(“facebook/detr-resnet-50″, revision=”no_timm”)
model = DetrForObjectDetection.from_pretrained(“facebook/detr-resnet-50″, revision=”no_timm”)
inputs = processor(images=image, return_tensors=”pt”)
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
for score, label, box in zip(results[“scores”], results[“labels”], results[“boxes”]):
    box = [round(i, 2) for i in box.tolist()]
    print(f”Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at location {box}”)

Code Breakdown:

• Library Imports: First, we import the libraries we need, like Hugging Face Transformers and torch, plus PIL for working with images and requests for getting the image online.
• Loading the Image: We pull the image from a URL using requests and open it using PIL.
• Loading the Pre-Trained Model: DETR comes pre-trained and ready to detect objects, so we load it with DetrImageProcessor and DetrForObjectDetection. The revision=”no_timm” helps avoid issues with specific library versions.
• Preprocessing the Image: We pass the image through DetrImageProcessor, which converts it into the format the model can understand, basically turning it into a tensor (the structured data format).
• Model Inference: Next, the image is passed through the DETR model, where it makes predictions, including bounding boxes and labels for each object it detects.
• Post-Processing: The post_process_object_detection function cleans up the results, ensuring the model only shows detections with high confidence (greater than 90% accuracy).
• Displaying Results: Finally, we loop through the detected objects and print out their labels (like “car” or “person”), confidence scores, and the bounding box coordinates.

What Does the Output Look Like?

Once you run this code, you’ll get output like this:

Detected car with confidence 0.96 at location [120.43, 45.12, 230.67, 145.34]
Detected person with confidence 0.92 at location [50.12, 60.35, 100.89, 180.45]

Here, the model has detected both a car and a person in the image, with confidence levels of 96% for the car and 92% for the person. The bounding box coordinates tell you exactly where the objects are located in the image.

Wrapping It Up:

What makes DETR so powerful is that it simplifies the object detection process, which usually involves a lot of complex steps. By using transformers, DETR doesn’t need a bunch of manually-tuned components. It processes the whole image at once, detecting objects in parallel instead of one by one. This makes it faster and more efficient than older methods. With its built-in self-attention and ability to detect objects from all sorts of categories, DETR is a game-changer in object detection.

So, whether you’re working with autonomous vehicles, surveillance systems, or medical imaging, the DETR model helps you accurately detect objects with minimal effort, using the latest in deep learning and transformer architecture. It’s like having a powerful tool that knows exactly what to look for and where to find it, with all the details you need to make smart, reliable decisions.

DETR: End-to-End Object Detection with Transformers (2020)

Conclusion

In conclusion, DETR (Detection Transformer) represents a significant advancement in object detection, combining deep learning and transformer architecture to streamline and improve accuracy. By eliminating the need for manual tuning of components like anchor boxes and non-maximum suppression, DETR simplifies traditional detection pipelines while offering precise, real-time predictions. Its unique set-based loss function and the use of attention mechanisms allow for end-to-end training, making it an effective tool for industries such as autonomous vehicles, retail, and healthcare. As the technology continues to evolve, we can expect even greater accuracy and efficiency in object detection, opening up new possibilities across various sectors.Snippet: DETR leverages transformer and deep learning toSomething went wrong while generating the response. If this issue persists please contact us through our help center at help.openai.com.Retry

RF-DETR: Real-Time Object Detection with Speed and Accuracy (2025)

Introduction

Integrating tools like Resend, Postmark, Google Sheets, and GenAI is a powerful way to automate and streamline your email-based receipt processing workflow. By using the Resend API, you can effortlessly send confirmation emails to users, summarizing receipt details and providing links to attachments and Google Spreadsheets. This article walks you through the final steps of setting up your system, from creating a Resend account to securing API keys, and even testing the entire process. With these integrations, you’ll ensure your receipt processing system runs smoothly and efficiently, offering seamless user experiences.

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Step 2: Update Your Environment Variables

Okay, now that we’ve got that shiny Resend API key from Step 1, it’s time to keep it safe and sound. You don’t want to be the person who leaves sensitive info lying around, right? That’s where environment variables come in—they’re like a safe hidden somewhere that only your app can open, keeping your keys secure while still letting your app use them.

For this Resend integration, we need to store two key pieces of info as environment variables:

• RESEND_API_KEY: This is the magical key we grabbed in Step 1, the one that lets your app talk to Resend and send those confirmation emails.
• RESEND_EMAIL_FROM: This is the email address that will show up as the sender for your confirmation emails. It should be an email address you’ve already verified with Resend, so everything gets sent properly.

Here’s how to add these environment variables:

1. First, head over to your Caasify Cloud Server dashboard. This is where all your deployed apps live.
2. Next, find your Flask app in the list and click on it to open the settings.
3. Once you’re in the settings section, look for the Environment Variables tab. This is where the magic happens!
4. Now, you’ll need to add these two variables:

• Key: RESEND_API_KEY | Value: Paste the API key you copied earlier from Step 1.
• Key: RESEND_EMAIL_FROM | Value: Enter the email address you’ve verified with Resend for sending confirmation emails.

Don’t forget to hit save after adding those variables! It’s like locking your door after hiding the treasure—super important.

Once those variables are safely stored, your Flask app will be ready to authenticate with Resend and start sending those all-important confirmation emails. With this step done, the next move will be to update your app to use these settings and start processing emails like a pro.

12-Factor App Methodology

Step 3: Install the Resend Python Library

Alright, let’s move on to the next step! It’s time to grab the Resend Python library—the helpful companion that’s going to make your app communicate smoothly with the Resend API. You might be wondering, “Why not just handle everything manually?” Well, here’s the thing: the Resend Python library takes care of all the hard work for you. It saves you from having to mess around with the nitty-gritty details of raw HTTP requests—stuff like creating headers, formatting requests, and dealing with those weird errors that pop up from the server. The library does all of that heavy lifting, so your code stays clean and simple.

With this library, you don’t need to be a coding pro to send transactional emails. It’s a total game-changer when it comes to keeping everything organized and easy to manage. Think of it like a smooth highway that lets you bypass all the usual roadblocks you’d face when working with an API directly.

To install the Resend Python library, all you need to do is open your terminal and type this magic command:

$ pip install resend

Once you press enter, your terminal will automatically download and install the library. It’s like giving your app a superpower at the click of a button. When the installation is done, you’ll be ready to integrate the library into your Python code. And here’s the best part—you won’t have to worry about annoying details like connecting to Resend, handling authentication, or formatting requests. This library takes care of it all. With the Resend Python library in place, you can focus on the fun parts of building your app while it handles the email-sending for you.

For more details on Python installation, refer to the Python Installation Guide.

Step 4: Update requirements.txt

Now, let’s move on to something that’s a bit like making sure you’ve got all the ingredients ready for a perfect recipe—updating your requirements.txt file. This file is a real lifesaver when it comes to managing all the libraries your Python app needs to run smoothly. Think of it like a shopping list for your code. It’s where you list all the packages your app depends on, and the best part is, it helps make sure your app runs consistently across different machines without any surprises down the road.

Instead of manually adding each library to the file (because, let’s be real, who has the time for that?), we’re going to use the super handy pip freeze command. This little tool grabs a snapshot of all the installed libraries and their exact versions. It’s kind of like doing a quick inventory of your pantry before you cook, making sure you have all the ingredients in the right amounts.

Here’s what you need to do: open up your terminal and run this command:

$ pip freeze > requirements.txt

What does this command do? It’s simple, but really powerful. It creates a requirements.txt file (or updates the one you already have) with a list of all the packages installed in your environment, including Resend and any other dependencies you’ve added so far. The best part? It includes the exact versions of each package. That way, when you deploy your app or set it up somewhere else, you don’t have to worry about mismatched versions or compatibility issues—it’s all locked down and ready to go.

Now, whenever you (or anyone else) wants to run the app in the future, all they need to do is run this command:

$ pip install -r requirements.txt

This will automatically install all the required dependencies listed in your requirements.txt file, setting up your environment just like it was before. It’s a stress-free, easy way to make sure your app has everything it needs to run smoothly. So go ahead, update that file, and rest easy knowing your app is good to go!

For more on Python dependency management with pip, check out this guide: Python Dependency Management with pip

Step 4: Update requirements.txt

Alright, here comes the next part of our journey—time to update the requirements.txt file. Think of this file like the instruction manual for your Python app. If your app were a car, requirements.txt would be the guide telling you exactly which parts (or libraries) you need to keep the engine running smoothly. And just like you wouldn’t skip adding important parts when building a car, you can’t skip essential libraries when setting up your app.

Instead of spending hours tracking down every package and typing it out manually (no one’s got time for that), we’re going to let the pip freeze command do all the hard work for us. It’s like hitting the “auto-fill” button on your shopping list—it grabs all the libraries you’ve installed and their exact versions, and adds them to the list. This way, you won’t have to stress about which version of Resend or Google Sheets API you’re using.

So, here’s what you need to do: open your terminal and run this command:

$ pip freeze > requirements.txt

Here’s the cool part: this command will either create a new requirements.txt file or overwrite the old one, and it will automatically fill it with a list of every library you’ve installed so far, including Resend, Postmark, and anything else your app depends on. But the best part? It doesn’t just add the libraries—it locks in the exact versions of each one. Why does this matter? Well, it stops any unexpected issues when you deploy your app or set it up somewhere new. No more worrying about things breaking because the versions don’t match.

Now that your requirements.txt file is all set, the next time you (or anyone else) want to set up the app, all you need to do is run this command:

$ pip install -r requirements.txt

This will install all the dependencies listed in your requirements.txt file, setting up your environment exactly how it was before, without any guesswork. And just like that, you’re all set to keep building your app—smooth sailing ahead!

Understanding the pip freeze command

Step 5: Deploy to Caasify

Alright, here we are—the moment we’ve all been waiting for. It’s time to get that updated Flask app out into the world! Once you’ve made all the changes to your app, the next big step is to deploy it to the cloud. This is when the magic happens, turning everything you’ve done locally into something live and accessible to everyone. Luckily, we’ll be using Caasify, which makes the deployment process super simple and pain-free. It takes care of most of the heavy lifting, leaving you with more time to focus on building cool features.

So, how do we get this show on the road? Let’s break it down step by step:

Push Your Updated Code to GitHub

Now that your Flask app is polished and ready to go, the first thing you need to do is commit all your changes and push them to GitHub. This is like uploading your latest work so that Caasify can take over and deploy it to the cloud. You can easily do this by running the following commands in your terminal:

$ git add .
$ git commit -m “Add Resend integration for confirmation emails”
$ git push origin main

What happens next? As soon as the code is pushed to the main branch of your GitHub repo, Caasify will automatically start the deployment process. You can just sit back and relax while it works its magic.

Monitor the Deployment

While your app is being deployed, you can keep an eye on everything by checking out the Deployments section in your app’s dashboard. This is where you’ll see real-time updates on the status of your deployment. It’s like watching a progress bar fill up, but with more detailed info, so you know exactly what’s happening at every moment. Plus, if anything goes wrong, you’ll be alerted right away. Pretty handy, right?

Verify Your Deployment

Once the deployment finishes, it’s time to test it out. Head over to your app’s public URL and make sure everything’s working as expected. Check the confirmation email process (because that’s what we’re here for!), see if the app is still responsive, and confirm that nothing’s broken. If all goes well, you can breathe easy—your deployment was a success!

Check Runtime Logs

If things aren’t looking quite right or you notice something’s off, don’t panic—just head to the Runtime Logs section in your dashboard. These logs give you a detailed look at your app’s activity, showing exactly what’s going on behind the scenes. If there are any errors related to Resend or anything else in your app, this is the place to investigate. Whether it’s a failed email delivery or a sneaky connection issue, the logs will help you figure it out and fix it fast.

By following these simple steps, you’ll have successfully deployed your updated Flask app to Caasify, and you’ll know exactly where to look if something goes wrong. With your app live and fully functional, it’s ready to start sending those confirmation emails and doing its job!

For further reference on deploying Flask, check out this Deploying Flask to Heroku (2025) guide.

Step 5: Test the Entire Workflow

Now that everything is set up, it’s time to put it to the test. Think of your app as a finely tuned machine, and testing is the moment when you make sure all the gears are turning smoothly. You want to be sure everything is running as expected—from processing the email body to saving receipt details in Google Sheets, and of course, sending that confirmation email back to the sender. The goal is to make sure your app handles the whole workflow seamlessly, with no hiccups along the way. So, let’s break it down step by step:

• Send a Test Email: First, we’re going to send a test email to your app through your email processing service—let’s say Postmark. The email should have both a text body and an attachment, just like a real receipt would. If you’re not sure how to set up Postmark to forward emails to your app, don’t worry! We’ve covered that before. Basically, you want to make sure that Postmark is properly forwarding the email to your app so it can process it. This is the first step in making sure your app is receiving and handling the data correctly.
• Check Postmark Activity JSON: Once you’ve sent the test email, head over to the Postmark dashboard and go to the Activity tab. You’ll see the email you just sent listed there. Double-check that the JSON payload includes both the text body and the Base64-encoded attachment data. Why does this matter? Well, this step confirms that Postmark is correctly sending the email data to your app. Without this, your app won’t be able to process the email properly, and you might miss some key details.
• Monitor the Logs: Next, check the runtime logs in your Caasify Cloud Server dashboard. This is where you’ll see exactly what’s happening behind the scenes as your app processes the email. In the logs, look for entries that show the receipt details were successfully extracted and that the attachments were uploaded to your cloud storage. If something’s not working as expected, these logs are your best friend—they’ll help you debug and figure out what went wrong.
• Verify Spaces Upload: Once the email is processed and the attachments are uploaded to your cloud storage, it’s time for a quick confirmation. Head over to Caasify Cloud Storage and check the relevant bucket where your files are stored. If the upload went well, you should see the attachments listed there, and the links to those files will be available. This ensures everything, from processing the email to storing the files, is working smoothly.
• Check Google Sheets: Now, let’s make sure your app has logged the receipt data in Google Sheets. Open the Google Sheet, and you should see a new row added with the relevant receipt details: vendor, amount, currency, and date. You’ll also find the URLs for the uploaded attachments in the last column, separated by commas. This confirms that the data was correctly saved, making it easy to manage and access.
• Verify the Confirmation Email: The last step is to check the confirmation email sent to the original sender. Open up the sender’s inbox and make sure the email has been received. This email should contain the extracted receipt details—vendor, amount, currency, and date—along with links to the uploaded attachments in cloud storage. You should also see a link to the Google Spreadsheet where the receipt data is stored. This final step ensures that the confirmation process is working as it should, giving users the feedback they need to know their receipt was processed successfully.

By following these steps, you’ll have thoroughly tested the entire email receipt processing workflow. From receiving the email to sending the confirmation, you’ll have made sure everything works as expected, and your app will be good to go. You’ll be confident that your app is fully operational, and your users will get the smooth experience they expect!

RFC 5321: Simple Mail Transfer Protocol

Troubleshooting

Alright, so everything seems set up, but things aren’t quite working like they should. Don’t stress—it happens to all of us! The good news is troubleshooting is usually pretty straightforward, and we can fix it step by step. Think of it like when your Wi-Fi goes down, and you just reset the router a few times until it magically starts working again. We’re going to do the same for your app’s workflow.

First things first, let’s take a look at the Resend dashboard. This is where you can check for any errors that might have popped up during the confirmation email process. Head over to the Resend dashboard and see what it says. If there’s an issue, this will be your first clue. The dashboard should tell you if the emails were successfully sent or if something failed. If there’s a failure, it will even provide detailed error messages to help you fix the problem.

Next, let’s double-check the environment variables. You definitely don’t want to skip this step—trust me, it can be sneaky. Make sure your RESEND_API_KEY (that’s the key that lets your app talk to Resend) and RESEND_EMAIL_FROM (the verified sender email address) are set up correctly in your environment. These two variables should be configured in your Caasify Cloud Server dashboard. If these aren’t set right, it’s like trying to send a letter without a return address—nothing’s going to reach its destination.

If everything looks good with the environment variables but it’s still not working, let’s dig into the Caasify runtime logs. These logs are your best friend when it comes to debugging. You’ll find them in the Logs tab of your Caasify dashboard. These logs will tell you exactly what’s happening under the hood and, more importantly, where things might be going wrong. The logs can point out issues like problems with the Resend or Postmark integrations, or even issues with uploading attachments. Review them carefully—they’re like a detective’s magnifying glass, helping you zoom in on the source of the problem.

Lastly, we need to check out Postmark’s Activity tab. Remember, Postmark is the service that forwards your emails to your app, so if things aren’t working, it’s a good idea to check in with it. Head over to the Activity tab and confirm that the test email you sent earlier was properly forwarded to your Flask app. If something went wrong, Postmark will show error messages here, which will help you figure out if there was an issue with the email forwarding or the way the attachment data was sent.

By following these troubleshooting steps, you’ll be able to find the issue and get your email receipt processing workflow back on track. You’ve got all the tools to fix things and make sure your app is running smoothly again. Think of it like putting together a puzzle—step by step, you’ll find the missing piece and complete the picture!

For further guidance, you can refer to the official Postmark Email Delivery Guide.

Conclusion

In conclusion, integrating Resend, Postmark, Google Sheets, and GenAI into your email-based receipt processing service ensures a seamless workflow for users, from receipt data extraction to sending confirmation emails. By following the outlined steps—creating a Resend account, securely storing API keys, and updating environment variables—you can automate and streamline the process with ease. The comprehensive testing ensures everything functions smoothly, providing users with immediate feedback on their receipt processing. Looking ahead, as these tools continue to evolve, you’ll find even more powerful ways to automate processes and enhance user experiences. Whether you’re improving business workflows or building new systems, leveraging tools like Resend, Postmark, and Google Sheets will be crucial for efficient, scalable solutions.

RAG vs MCP Integration for AI Systems: Key Differences & Benefits (2025)

Introduction

The sed command in Linux is an essential tool for efficient text manipulation and automation. Whether you’re a system administrator or a developer, mastering sed allows you to automate tasks like searching, replacing, inserting, and deleting text without the need for manual editing. With powerful features like regular expressions and in-place editing, sed is perfect for stream editing and batch processing in Linux environments. In this guide, we’ll dive into the basic syntax, common use cases, and advanced techniques that will help you leverage sed to its fullest potential.

1: What is the sed Command?

Imagine you’re burning the midnight oil, tweaking configuration files on your Linux server. You’ve got a ton of text that needs some attention, but who has the time to open each file in a text editor? This is where the sed command swoops in, like a magic wand for text editing—no need to manually open files or click around. Instead, sed lets you make changes directly to files from the command line, one line at a time. This makes it perfect for shell scripting and system administration, where automating tasks is a must. Whether you’re looking for specific lines, replacing words, or inserting and deleting text, sed makes all of it super easy and fast, without needing a graphical editor. So, next time you’re staring down a mountain of text, remember that sed is the tool that’ll get it done quickly and smoothly.

2: Key Features of sed

• Pattern matching and replacement
• In-place file editing
• Text filtering and manipulation
• Support for regular expressions
• Multiline operations

These features make sed extremely flexible and powerful for managing text. Let’s break it down a bit:

• Pattern matching and replacement: This is sed’s bread and butter. You can search for specific text patterns and swap them out with anything you need.
• In-place file editing: This feature is like magic—changes are made directly to the file you’re working on, no need to save a new output file.
• Text filtering and manipulation: Sometimes, you just want to pull out the good stuff and leave the rest behind. sed handles that with ease.
• Regular expressions: Oh yes, sed loves regular expressions. This means you can dive deep into complex patterns and manipulate them like a pro.
• Multiline operations: There are times when you need to handle more than one line of text, and sed’s got you covered here too, letting you process multiple lines at once.

3: Basic Syntax of the sed Command

Alright, here’s the thing: the syntax of sed isn’t as complicated as it looks. It’s built around three simple parts that tell sed how to do its job: command options, a script with instructions, and the file you want to edit. Think of it like a recipe: you have your ingredients (the options), your cooking instructions (the script), and your cooking pot (the file).

• Command options: These are like the settings on a coffee machine, telling sed how to act. For example, the -i option is your “edit the file right now” button.
• Script: The script is where you get specific. Inside the quotes, you’ll tell sed exactly what you want to do—replace, delete, insert text, and so on.
• Input file: The input file is the actual text you’re editing. It can be one file or a bunch. If no file is provided, sed can even read from standard input.

Here’s the syntax in action:

$ sed [options] ‘script’ file

In this example:

• sed is your trusty editor.
• [options] tell sed how to behave.
• 'script' contains the editing commands.
• file is the file you’re editing.

For example, let’s say you want to replace the first occurrence of “hello” with “world” in sample.txt. The command would look like this:

$ sed ‘s/hello/world/’ sample.txt

What’s happening here is sed searches through sample.txt, finds “hello,” and replaces it with “world.” Pretty simple, right?

4: Commonly Used Options in sed

Now that you’ve got the basic syntax down, let’s dive into some of the most commonly used options with sed. Think of these as the cool features that make your editing experience smoother:

• -i (In-place editing): Forget about creating a new file to save changes. This option lets you modify the file directly.

  $ sed -i ‘s/old/new/’ file.txt

• -n (Suppress automatic printing): By default, sed shows you everything it’s processing. But what if you only care about specific lines? Use -n to suppress output and only show what you want.

  $ sed -n ‘/pattern/p’ file.txt

• -e (Execute multiple commands): Sometimes, you need to do a few things at once. The -e option lets you chain sed commands together.

  $ sed -e ‘s/old/new/’ -e ‘/pattern/d’ file.txt

• -f (Read commands from a file): If you’ve got a bunch of sed commands to run, you can store them in a file and reference it, keeping your workspace neat and tidy.

  $ sed -f script.sed file.txt

• -r and -E (Use extended regular expressions): These options let you use extended regular expressions, so you can handle more complex patterns and replacements.

  $ sed -r ‘s/old/new/’ file.txt

  $ sed -E ‘s/old/new/’ file.txt

• -z (Separate lines with a NUL character): This option is useful when you’re dealing with files that contain some quirky characters.

  $ sed -z ‘s/old/new/’ file.txt

• -l (Specify line length): Want to control how many characters show up per line? The -l option does just that.

  $ sed -l 100 ‘l’ file.txt

• -b (Binary mode): When working with binary data, this option makes sure carriage return characters aren’t stripped out.

  $ sed -b ‘s/old/new/’ file.txt

5: Most Common Use Cases of sed

Sed really shines when it comes to everyday text manipulation. Whether you’re editing configuration files or cleaning up log files, sed makes the process smooth and fast. Let’s look at some common use cases with examples.

Creating a Sample File

Before you start, let’s create a sample file called file1.txt. Run this command to create it:

$ cat > file1.txt

Then copy and paste the following text into file1.txt:

Linux is a family of free and open-source operating systems based on the Linux kernel. Operating systems based on Linux are known as Linux distributions or distros. Examples include Debian, Ubuntu, Fedora, CentOS, Gentoo, Arch Linux, and many others.

Search and Replace

Now, let’s replace the first occurrence of “Linux” with “Unix” in file1.txt. You can do this with:

$ sed ‘s/Linux/Unix/’ file1.txt

By default, sed only replaces the first occurrence in each line. The result will look like this:

Unix is a family of free and open-source operating systems based on the Linux kernel. Operating systems based on Unix are known as Linux distributions or distros. Examples include Debian, Ubuntu, Fedora, CentOS, Gentoo, Arch Unix, and many others.

Replace Globally in Each Line

Want to replace all occurrences of “Linux” with “Unix” in each line? Just use the global substitute flag (/g):

$ sed ‘s/Linux/Unix/g’ file1.txt

This command replaces all instances, and the output will look like this:

Unix is a family of free and open-source operating systems based on the Unix kernel. Operating systems based on Unix are known as Unix distributions or distros. Examples include Debian, Ubuntu, Fedora, CentOS, Gentoo, Arch Unix, and many others.

In-Place Editing

If you want to make the change directly to the file and save it right there, use the -i option:

$ sed -i ‘s/Linux/Unix/’ file1.txt

Delete Specific Lines

Let’s say you want to delete the second line. You can use this:

$ sed ‘2d’ file1.txt

This removes the second line, and the result will be:

Unix is a family of free and open-source operating systems based on the Linux kernel. Examples include Debian, Ubuntu, Fedora, CentOS, Gentoo, Arch Unix, and many others.

Print Specific Lines

Sometimes you only want to print certain lines. Use the -n option and the p command to specify the lines you want:

$ sed -n ‘1,2p’ file1.txt

This will print lines 1 and 2:

Unix is a family of free and open-source operating systems based on the Unix kernel. Operating systems based on Unix are known as Unix distributions or distros.

Delete Lines Matching a Pattern

If you need to delete all lines containing the word “kernel,” use:

$ sed ‘/kernel/d’ file1.txt

The result will be:

Operating systems based on Unix are known as Unix distributions or distros. Examples include Debian, Ubuntu, Fedora, CentOS, Gentoo, Arch Unix, and many others.

Substitute with a Backup File

If you want to replace “Unix” with “Linux” but also keep a backup of the original file, use:

$ sed -i.bak ‘s/Unix/Linux/g’ file1.txt

This will create a backup file called file1.txt.bak, while also updating the original file as needed.

Each of these examples shows just how powerful sed can be for handling text in Linux. Whether you’re editing configuration files or automating text processing tasks, sed has your back!

GNU sed Manual

Conclusion

In conclusion, mastering the sed command in Linux is essential for anyone working with text manipulation and automation. By leveraging sed’s powerful features, such as regular expressions and in-place editing, you can streamline tasks like searching, replacing, and deleting text within files—all from the command line. Whether you’re automating text-processing in system administration or shell scripting, sed offers a robust solution for efficient, line-by-line modifications. As Linux environments continue to evolve, understanding and utilizing sed’s advanced techniques will keep you ahead in managing batch processing tasks. Keep exploring, as sed remains an invaluable tool for any developer or sysadmin looking to enhance their workflow.

Master Linux Permissions: Set chmod, chown, sgid, suid, sticky bit

Introduction

Building a Puppeteer web scraper with Docker and App Platform allows developers to efficiently automate data extraction while ensuring scalability and flexibility. Whether you’re working with race results or public domain books, this setup provides a powerful solution for web scraping tasks. In this article, we’ll explore how to create a web application that scrapes data using Puppeteer in a Docker container, all deployed seamlessly on App Platform. With a focus on best practices like rate limiting and bot identification, you’ll learn how to optimize your web scraping applications for performance and reliability.

Race Time Insights Tool

As an ultra marathon enthusiast, I’ve had my fair share of challenges. One of the toughest questions I often find myself asking is: how do I estimate my finish time for a race that I’ve never attempted before? It’s a question that’s bothered me for quite some time, and naturally, I turned to my coach for some insight. His suggestion was simple yet brilliant—look at runners who have completed both a race I’ve done and the race I’m targeting. By finding patterns in their performance across both events, I could get a better idea of my own potential finish times.

The idea sounded good in theory, but here’s the thing: manually going through race results from multiple sources would take forever. It would be a huge pain to gather all that data and then make meaningful comparisons. That’s when I decided to build something that could automate the whole process—something that would save me (and other runners) a lot of time and energy. And so, Race Time Insights was born.

This tool automatically compares race results by finding athletes who’ve participated in both races. All you have to do is input the URLs of two races, and the application scrapes race results from platforms like UltraSignup and Pacific Multisports. It then shows how other athletes performed across both events, giving you valuable insights.

Building this tool was a huge eye-opener for me—it really made me appreciate how powerful Caasify’s App Platform is. I was able to use Puppeteer with headless Chrome in Docker containers to focus on solving the problem for runners, while App Platform took care of all the behind-the-scenes infrastructure. The result? A tool that’s scalable, efficient, and helps the running community make better, data-driven decisions about their race goals.

But after finishing Race Time Insights, I thought: why not share what I learned with other developers? I wanted to create a guide on how they could use the same technologies—Puppeteer, Docker containers, and Caasify App Platform—to build their own tools. The challenge? When you work with external data, you’ve got to be mindful of things like rate limiting and sticking to terms of service.

That’s when I turned to Project Gutenberg. It’s a treasure chest of public domain books, and because its terms of service are super clear, it was the perfect example for demonstrating these technologies. In this post, I’ll show you how to build a book search application using Puppeteer inside a Docker container, deployed on App Platform, while following best practices for external data access.

Project Gutenberg Book Search

I’ve built and shared a web application that scrapes book information from Project Gutenberg responsibly. The app lets you search through thousands of public domain books, view detailed info about each one, and download them in different formats. What’s really exciting about this project is that it shows how you can do web scraping the right way—respecting the source data, following best practices, and still providing tons of value to users.

Being a Good Digital Citizen

When you build a web scraper, there’s a right way to do it and a wrong way. You need to respect both the technical and legal boundaries. Project Gutenberg is a perfect example of doing it right because:

• It has clear terms of service
• It provides robots.txt guidelines
• Its content is fully in the public domain
• It encourages more accessibility to its resources

When building our scraper, we followed several best practices to make sure we were doing things the right way:

Rate Limiting

For this demo, I set up a simple rate limiter that makes sure there’s at least one second between requests:

// A naive rate limiting implementation
const rateLimiter = {
lastRequest: 0,
minDelay: 1000, // 1 second between requests
async wait() {
const now = Date.now();
const timeToWait = Math.max(0, this.lastRequest + this.minDelay – now);
if (timeToWait > 0) {
await new Promise(resolve => setTimeout(resolve, timeToWait));
}
this.lastRequest = Date.now();
}
};

This approach is simplified just for demonstration. It assumes the app runs in a single instance and stores state in memory, which wouldn’t be ideal for larger-scale use. If I wanted to scale this, I’d probably use Redis for distributed rate limiting or set up a queue-based system for better performance. We use this rate limiter before every request to Project Gutenberg:

async searchBooks(query, page = 1) {
await this.initialize();
await rateLimiter.wait(); // Enforce rate limit
// … rest of search logic
}

async getBookDetails(bookUrl) {
await this.initialize();
await rateLimiter.wait(); // Enforce rate limit
// … rest of details logic
}

Clear Bot Identification

It’s important to let website administrators know who is accessing their site and why. This kind of transparency helps build trust and avoids issues later on. With a custom User-Agent, we can clearly identify our bot:

await browserPage.setUserAgent(‘GutenbergScraper/1.0 (Educational Project)’);

This helps administrators monitor and analyze bot traffic separately from human users, and it could even result in better support for legitimate scrapers.

Efficient Resource Management

Running Chrome in a headless environment can use a lot of memory, especially when running multiple instances. To prevent memory leaks and ensure the app runs smoothly, we make sure to properly close each browser page once we’re done with it:

try {
// … scraping logic
} finally {
await browserPage.close(); // Free up memory and system resources
}

By following these practices, we make sure our scraper is effective and respectful of the resources it accesses. This is especially important when working with valuable public resources like Project Gutenberg.

Web Scraping in the Cloud

The application relies on modern cloud architecture and containerization through Caasify’s App Platform. This approach strikes the perfect balance between making development easier and keeping the app reliable in production.

The Power of App Platform

Caasify’s App Platform makes deployment a breeze by handling all the usual heavy lifting:

• Web server configuration
• SSL certificate management
• Security updates
• Load balancing
• Resource monitoring

With App Platform handling the infrastructure, we can focus on just the application code.

Headless Chrome in a Container

The core of our scraping functionality is Puppeteer, which lets us control Chrome programmatically. Here’s how we set up and use Puppeteer in our app:

const puppeteer = require(‘puppeteer’); class BookService {
constructor() {
this.baseUrl = ‘https://www.gutenberg.org’;
this.browser = null;
} async initialize() {
if (!this.browser) {
// Add environment info logging for debugging
console.log(‘Environment details:’, {
PUPPETEER_EXECUTABLE_PATH: process.env.PUPPETEER_EXECUTABLE_PATH,
CHROME_PATH: process.env.CHROME_PATH,
NODE_ENV: process.env.NODE_ENV
}); const options = {
headless: ‘new’,
args: [
‘–no-sandbox’,
‘–disable-setuid-sandbox’,
‘–disable-dev-shm-usage’,
‘–disable-gpu’,
‘–disable-extensions’,
‘–disable-software-rasterizer’,
‘–window-size=1280,800’,
‘–user-agent=GutenbergScraper/1.0 (+https://github.com/wadewegner/doappplat-puppeteer-sample) Chromium/120.0.0.0’
],
executablePath: process.env.PUPPETEER_EXECUTABLE_PATH || ‘/usr/bin/chromium-browser’,
defaultViewport: { width: 1280, height: 800 }
}; this.browser = await puppeteer.launch(options);
}
}
}

This setup lets us:

• Run Chrome in headless mode (no GUI needed)
• Execute JavaScript in the context of web pages
• Safely manage browser resources
• Work reliably in a containerized environment

The setup also includes some key configurations for running in a containerized environment:

• Proper Chrome Arguments: Important flags like –no-sandbox and –disable-dev-shm-usage for working in containers.
• Environment-aware Path: It uses the right Chrome binary path from environment variables.
• Resource Management: It adjusts viewport sizes and disables unnecessary features.
• Professional Bot Identity: It uses a clear user agent and HTTP headers to identify the scraper.
• Error Handling: It makes sure to clean up properly to avoid memory leaks.

While Puppeteer makes controlling Chrome a breeze, running it in a container requires careful setup to ensure all the necessary dependencies and configurations are in place. Let’s dive into how we set this up in our Docker environment.

Docker: Ensuring Consistent Environments

One of the hardest things about deploying web scrapers is making sure they work the same in both development and production. Your scraper might run perfectly on your local machine, but then fail in the cloud because of missing dependencies or different system configurations. This is where Docker comes in.

Docker helps by packaging everything the application needs—from Node.js to Chrome—into one container that runs the same way on any machine. This guarantees that the scraper behaves the same whether you’re running it locally or on Caasify’s Cloud.

Here’s how we set up our Docker environment:

FROM node:18-alpine
# Install Chromium and dependencies
RUN apk add –no-cache
chromium
nss
freetype
harfbuzz
ca-certificates
ttf-freefont
dumb-init # Set environment variables
ENV PUPPETEER_SKIP_CHROMIUM_DOWNLOAD=true
PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser
PUPPETEER_DISABLE_DEV_SHM_USAGE=true

The Alpine-based image keeps our container lightweight while including all the necessary dependencies. When you run this container—whether on your laptop or in Caasify’s Cloud—you get the exact same environment with the correct versions and configurations needed for running headless Chrome.

Development to Deployment

Now, let’s walk through getting this project up and running.

Local Development

First, fork the example repository to your GitHub account. This gives you your own copy to work with and deploy from. Then clone your fork locally:

# Clone your fork
git clone https://github.com/YOUR-USERNAME/doappplat-puppeteer-sample.git
cd doappplat-puppeteer-sample

Then, build and run with Docker:

# Build and run with Docker
docker build -t gutenberg-scraper .
docker run -p 8080:8080 gutenberg-scraper

Understanding the Code

The application is structured around three main components:

• Book Service: Handles web scraping and data extraction
• Express Server: Manages routes and renders templates
• Frontend Views: Clean, responsive UI using Bootstrap

Deployment to Caasify

Now that you have your fork of the repository, deploying to Caasify’s Cloud is easy:

• Create a new Cloud application
• Connect to your forked repo
• On resources, delete the second resource (that isn’t a Dockerfile); this is auto-generated by the platform and not needed
• Deploy by clicking Create Resources

The application will be automatically built and deployed, with App Platform handling all the infrastructure details.

Conclusion

In conclusion, building a Puppeteer web scraper with Docker and App Platform offers a powerful and scalable solution for modern web scraping needs. Whether you’re developing an application to estimate ultra marathon race times or scraping public domain books from Project Gutenberg, this setup ensures efficiency, flexibility, and best practices like rate limiting and bot identification. By leveraging Docker containers and deploying on App Platform, developers can create reliable, cloud-based scraping solutions that meet the demands of today’s data-driven applications. Looking ahead, as web scraping continues to evolve, integrating advanced technologies and cloud platforms will be key to streamlining data collection and enhancing automation workflows.For more insights into efficient web scraping and deployment, consider exploring how Puppeteer, Docker, and App Platform can shape the future of automated data extraction.

Docker system prune: how to clean up unused resources (2025)

Introduction

Converting a Vue.js app into a Progressive Web App (PWA) is a powerful way to enhance its functionality, offering offline access and improved performance. With tools like Vite, Vue, and service workers, you can seamlessly integrate PWA capabilities. In this tutorial, we’ll guide you through the process of transforming your single-page application into a PWA, including setting up a service worker and utilizing IndexedDB for efficient offline data storage. By following these steps, you’ll provide users with a smooth and reliable experience, even without an internet connection.

Step 1 – Setting up the project

Alright, let’s get started! The first thing we’re going to do is clone and set up the sample project on your local development environment. Now, this isn’t just any ordinary app. The project uses Vite and Vue.js, which makes for a much faster and smoother development experience. If you’ve used Tailwind CSS before, you’ll love how it’s integrated here to make the interface responsive and sleek, giving the app a polished, modern feel. These technologies work together like a dream team—ensuring that your workflow is efficient and productive.

To start, you’ll need to clone an existing Vue app that was created just for this tutorial. It’s called the What’s New app. Sounds like something you’d read in a magazine, right? It’s a news aggregator that pulls data from the News API, neatly sorting everything into categories like headlines, general news, and even a personalized feed tailored just for you. The goal is to build on this app by adding exciting new features, like turning it into a Progressive Web App (PWA) and giving it offline capabilities. Cool, right?

Let’s go step by step. First, head over to GitHub and find the What’s New app. Once you’re there, click on the Fork button in the top-right corner. This is an important step—it’ll create your own copy of the repository, so you can make changes and track them without affecting the original project.

Once you’ve forked the repo, open up your command line (CLI), and run the following command to clone the repository to your machine:

$ git clone https://github.com/{your-github-username}/whats-new.git

Just make sure to replace {your-github-username} with your actual GitHub username. That will download the project to your local machine so you can start working on it.

Next, go into the project directory by running:

$ cd whats-new

Now, let’s check out the starter code branch to make sure you’re working with the initial setup. You can do this by running:

$ git checkout do/starter-code
If you haven’t registered for the News API and grabbed your API key yet, this is your reminder to do so! Go ahead and sign up, get your API key, and we’ll be ready to start pulling in real news data for the app.

Once you have your API key, open your favorite code editor (I’m a fan of VS Code, but use whatever works for you!), and locate the .env.example file in the project folder. Copy that file and rename it to .env. Inside the .env file, you’ll find a placeholder for VITE_NEWS_API_KEY. Replace that with your actual News API key, and make sure to save your changes!

Now, let’s install the necessary project dependencies. Open your terminal and run:

$ npm install

This will install everything the app needs to run smoothly. Once that’s done, you can fire up the app by running:

$ npm run dev

If all goes well, you should see something like this in the terminal:

[email protected] dev > vite VITE v5.0.10 ready in 3639 ms
➜ Local: http://localhost:5173/
➜ Network: use –host to expose
➜ press h + enter to show help

Now open your browser and navigate to http://localhost:5173/, and you’ll be welcomed with the What’s New homepage, showing the news items fetched from the News API. Congratulations! You’ve just set up your project, and now you’re ready to start adding features and making the app even better!

Progressive Web Apps (PWA) Overview

Step 2 – Creating a web app manifest configuration

So, here’s the deal—when you’re building a Progressive Web App (PWA), there’s one key thing you absolutely need to set up: the web application manifest. Think of it as your app’s identity card on the web. It’s a simple JSON file that holds all the essential details about your app, like its name, icons, and display settings. This is what the browser uses to know how to handle your app when it’s installed on a user’s device. Without it, your PWA will have no idea what to do once it’s installed.

Now, here’s the catch: for the manifest to be legit and actually work, it needs to include four key pieces of information—these are the four “keys” that tell the browser exactly how your app should behave. So, what are these keys? Glad you asked!

• name – This is the full name of your app, usually what shows up when someone installs it on their device. It’s like the title of your app!
• icons – A set of images or icons that represent your app across different devices and screen sizes. You can think of these as the digital version of your app’s face.
• start_url – This is the URL that opens when someone launches the app. Essentially, it’s the app’s starting point.
• display – This defines how the app should be shown on the device, whether in fullscreen mode, standalone, or with a minimal UI. It’s all about how your app looks when it’s opened.

Now, sure, you could create this manifest file manually. But I bet you’d rather keep things simple, right? That’s why we’re going to use a PWA Manifest Generator tool to make everything a lot easier. It’ll generate the manifest and icons for you automatically. No headaches, just smooth sailing.

Here’s how to do it:

• Launch the PWA Manifest Generator Tool: Head over to the PWA Manifest Generator tool. This is where all the magic happens. The tool will take care of creating the manifest and generating all the icons your app will need.
• Configure the Manifest: Once you’ve opened the tool, fill in a few basic details like the name of your app, theme color, background color, and any other info you want to include. You can also tweak how you want the app to appear when installed, like adjusting the display settings or orientation to match your vision.
• Upload the Icon: Next, you’ll be prompted to upload your app’s icon. You can find it in the What’s New project folder as a file called app-icon-image.png. Just upload that file, and the tool will automatically create all the different icon sizes for you—super easy!
• Generate the Manifest: Once you’ve filled out all the fields and uploaded the icon, click the Generate Manifest button. The tool will process everything you’ve entered and give you a zipped folder. This folder will contain the manifest file and all the icons you need.
• Download and Extract the Files: Download the zipped folder, and once it’s on your computer, unzip it. Inside, you’ll find the manifest.webmanifest file and several icon files, each labeled with its size (like icon-192x192.png, icon-512x512.png, etc.). You’ll want to copy these icon files into your app’s /public directory so they’re ready to go when the app is installed.
• Edit the Manifest File: Open the manifest.webmanifest file in your favorite text editor (I like VS Code, but use whatever works best for you). You’ll see a JSON object with all the configuration settings for your app, something like this:

{
“name”: “What’s New”,
“short_name”: “What’s New”,
“description”: “A news aggregator app fetching data from the News API.”,
“icons”: [
{
“src”: “/icon-192×192.png”,
“sizes”: “192×192”,
“type”: “image/png”
},
{
“src”: “/icon-512×512.png”,
“sizes”: “512×512”,
“type”: “image/png”
}
],
“start_url”: “/”,
“display”: “standalone”,
“background_color”: “#ffffff”,
“theme_color”: “#0F172A”
}

This file contains all the vital information you need—like the app name, icons, start URL, and how the app should display when installed. It’s like your app’s digital resume!

Save and Use the Manifest: Once you’ve reviewed the manifest and made any adjustments you need, go ahead and save the file. This JSON object will be your reference in the next steps when you integrate it into the app’s functionality.

And just like that, you’ve successfully created and set up your web app manifest! This is a huge milestone in getting your PWA up and running. Now, when users install your app, they’ll get a smooth, professional experience, and that’s exactly the kind of vibe you want to create!

For more details, refer to the Web App Manifest Specification.

Step 3 – Generating the web app manifest and service worker

Alright, here we go! You’ve made some solid progress on your app, and now it’s time to get it closer to being a fully functional Progressive Web App (PWA). So let’s roll up our sleeves and dive into creating the web application manifest and setting up the service worker.

You’re going to use a Vite plugin called vite-plugin-pwa to make it happen. Think of it like your magic tool that helps you easily add PWA features to your Vite-based app. Before you get started, though, you’ll need to install the plugin first. Don’t worry, it’s simple, and I’ll guide you through it.

Installing the Plugin:

Open your terminal and run this command:

$ npm install -D vite-plugin-pwa

Once it’s installed, the next thing you’ll need to do is configure the plugin in your project. You’ll have to make a quick edit to your vite.config.js file. Don’t worry, it’s a simple tweak.

Here’s what you need to do:

import { defineConfig } from ‘vite’;
import vue from ‘@vitejs/plugin-vue’;
import { VitePWA } from ‘vite-plugin-pwa’;
//&nbsphttps://vitejs.dev/config/
export default defineConfig({
  plugins: [
    vue(),
    VitePWA({})
  ],
});

This part of the code sets up vite-plugin-pwa in your project, and that’s what allows it to start working. But hold up—there’s more to do! You still need to tell the plugin what your PWA should look like. This is where you configure the manifest—the file that gives your app its name, icons, and how it should behave when launched.

Updating the vite.config.js for the Manifest:

Now, we need to add a little extra to the config file. Specifically, you’ll add a manifest key inside the VitePWA() function. This is where you’ll link it to the manifest JSON object you created earlier.

Here’s the updated vite.config.js file:

export default defineConfig({
  plugins: [
    vue(),
    VitePWA({
      manifest: {
           “theme_color”: “#0F172A”,
           “background_color”: “#f5f8fa”,
           “display”: “standalone”,
           “scope”: “/”,
           “start_url”: “/”,
           “name”: “What’s New – Vue News Aggregator Site”,
           “short_name”: “What’s New”,
           “description”: “A news aggregator pulling news items from News API.”,
           “icons”: [
               { “src”: “/icon-192×192.png”, “sizes”: “192×192”, “type”: “image/png” },
               { “src”: “/icon-256×256.png”, “sizes”: “256×256”, “type”: “image/png” },
               { “src”: “/icon-384×384.png”, “sizes”: “384×384”, “type”: “image/png” },
               { “src”: “/icon-512×512.png”, “sizes”: “512×512”, “type”: “image/png” }
           ] 
      }
     })
  ],
});

By adding this, you’ve told the plugin exactly what to do with your PWA manifest. It’s got everything: the app’s name, description, icons, and how it should appear when it’s launched (like in standalone mode). Now your app will know exactly how to show up when users install it.

What Happens Next:

Once you save those changes, here’s what happens every time you build your app: the plugin will automatically generate the web app manifest for the browser and place it in your app’s entry point (/). On top of that, it will also create a service worker. This is the behind-the-scenes helper that manages caching and offline behavior for your app.

Now, how do you check if everything is working? Let’s do a quick inspection of your browser to make sure everything is set up properly.

Verifying Your PWA:

To make sure your app has turned into a PWA, you need to check a few things in your browser’s developer tools. Here’s how:

Opening Developer Tools:

If you’re using Chrome, open your developer tools by pressing:

• CTRL + SHIFT + I on Windows
• OPTION + CMD + I on Mac

Checking the Manifest:

In the Application tab, look for the Manifest section in the sidebar. Click on it, and voilà! You should see the details you added in the manifest—like the app’s name, icon, and start URL.

But here’s the thing: If you’re still in development mode, this might show up empty. Don’t stress, though—that’s normal. The plugin only generates the manifest when the app is built in production mode.

Verifying the Service Worker:

Next, let’s check the Service Workers section. If you’re still in development mode, you won’t see anything here. But once the app is in production, this section will show the service worker the plugin created.

Checking Cache Storage:

Now, head over to the Storage section and look under Cache Storage. In development mode, this will be empty. But once your app is in production, you should see some cached files here, ready to help your app work offline.

Look for the Install Button:

You should also spot the install button in the browser’s toolbar. This little icon allows users to install your app. But here’s the thing: It won’t show up in development mode. You’ll need to build your app in production mode for this button to appear.

Building and Previewing the App:

Let’s take it to the next level. To fully test your PWA, you need to build the app and preview it in production mode. Here’s how:

$ npm run build
$ npm run preview

What happens here? npm run build compiles your project, and npm run preview serves the built application. Once you preview the app, you should see:

• The manifest configuration
• Cached files in Cache Storage
• The install button in the toolbar!

Using devOptions for Dev Mode:

But hey, I get it—rebuilding the app every time you make a change can be a bit of a hassle. So here’s a cool trick: The vite-plugin-pwa package has a devOptions feature that lets you preview your app as a PWA while you’re still in development mode. How awesome is that?

To enable this, all you need to do is add this to your vite.config.js:

export default defineConfig({
  plugins: [
    vue(),
    VitePWA({
      devOptions: { enabled: true },
      manifest: { … }
    })
  ],
});

Once you save these changes and restart your dev server, you’ll see some extra logs in the console, showing that the service worker is being generated and cached. The sw.js file and related files are now created, and registerSW.js will make sure the service worker gets registered as soon as the app loads in the browser.

After the app loads, you can check the Manifest, Cache Storage, and Service Workers sections to confirm that everything is set up right. The active service worker will now show up in the Service Workers section of your developer tools.

And just like that, your app is one step closer to being a fully functional PWA. From the manifest to the service worker, you’ve set everything up. Now your app is ready to be installed and used offline—just like a native mobile app!

Progressive Web Apps on MDN

Step 4 – Handling the caching of application files and assets

You’ve made some awesome progress, and now your app is officially a Progressive Web App (PWA). But here’s the thing—it’s still not quite ready to work offline. If you open up the Network tab in your browser’s developer tools, switch it to offline mode, and refresh the page, you’re probably going to see a blank screen. That’s because the app still depends on an internet connection. But no worries, we’re about to fix that and get it working offline.

The Mission: Cache Your App’s Files and Assets

The problem is simple: your app hasn’t been set up to cache its files and assets yet. Without that, there’s no way for your app to work offline. So, we’re going to make a small but essential change in the Vite PWA plugin configuration to enable caching.

Let’s get into the code!

Modifying the Vite Configuration:

You’ll need to update your vite.config.js file by adding some key info to ensure your app caches the right assets. Here’s the code you’ll be working with:

export default defineConfig({
  plugins: [
    vue(),
    VitePWA({
      devOptions: {…},
      includeAssets: [“**/*”],
      manifest: {…},
      workbox: {
        globPatterns: [“**/*.{js,css,html,png}”]
      }
    ])
});

Explanation of the Code:

Let me break this down for you:

• includeAssets: This tells the plugin to include all the static files from the public folder in the service worker’s precache. These could be files like your app’s favicon, SVG images, and font files. These are important because your app needs them cached in order to work offline.
• globPatterns (under workbox): This is where you specify which file types should be cached by the service worker. In this case, we’re saying: “Hey, cache all the .js, .css, .html, and .png files because these are the main files your app needs to run smoothly.”

Once you add these changes, your app will have all the necessary files and assets it needs to function offline. The service worker will do the job of caching everything for you.

Testing the Offline Functionality:

Okay, you’ve made the changes—now we need to see them in action, right? Let’s rebuild the app and test it in production mode.

If you’re still running the app in development mode, you’ll need to build it first. Run this command to compile your app:

$ npm run build

Once the build is complete, you can preview the production version by running:

$ npm run preview

This will create a production build of your app, allowing you to test the offline functionality more accurately.

Why Does This Process Differ in Development Mode?

Here’s the thing: the development build works a little differently from the production build. In development mode, the app runs in memory and doesn’t save files to disk. So, there’s no output in a dist folder like you get in production. This means the service worker doesn’t have anything to cache in development mode. It can only cache basic files like index.html and registerSW.js.

So, if you try running your app offline in development mode, you’re probably going to see a blank screen since none of the app’s resources are cached.

Verifying Caching in Production Mode:

Here’s where it gets fun. After you run the production build, you can confirm that caching is working by checking the sw.js file in both the dev-dist and dist directories.

To test it:

• Run the production build and open your browser.
• Open Developer Tools and go to the Network tab.
• Set the network to offline mode.
• Refresh the page.

If everything is working, the app should load without issues, even without an internet connection!

Checking the Cached Files:

To check if the service worker is caching everything it should, go to the Application tab in the developer tools. Click on Cache Storage, and you should see an item labeled workbox-precache-*. This will show all the files the service worker has cached. These files should match the contents of your app’s dist folder.

Comparing Caching in Development and Production:

Now, let’s compare how caching behaves in development mode versus production mode. Here are some key differences:

• In production mode: You’ll see the full list of cached files, including your JavaScript, CSS, HTML, and image files. These are the files your app needs to work offline.
• In development mode: The cache is either empty or only contains basic files like index.html and registerSW.js. This happens because development builds don’t save files to disk like production builds do.

Key differences to note:

• Number of entries: In production mode, you’ll see all the files your app needs. In development mode, not so much.
• Port numbers: These will differ depending on whether you’re running in development or production mode.
• File names: In production mode, the file names in the cache should match exactly what’s in your dist folder. In development mode, they might not align because the files aren’t being cached.

Wrapping It Up:

So here’s where we are: you’ve successfully configured your app to cache the essential files and assets, so it can work offline. With the service worker in place and the right files cached, users can now use your app even without an active internet connection. You’re one step closer to a fully functional PWA!

But wait, there’s more to do. Right now, your app still pulls live data from an API, and that data isn’t cached. In the next step, you’ll learn how to integrate IndexedDB to store and cache that dynamic content as well. Your PWA will be even more powerful than before!

For more information, check out the Service Workers Overview.

Step 5 – Caching application data using IndexedDB

Imagine this: you’ve built an awesome Progressive Web App (PWA) that works perfectly when connected to the internet. But now, it’s time to take things up a notch and make it work offline. Sure, you could stop here, but where’s the fun in that? Let’s make it even better. That’s where IndexedDB comes in.

What is IndexedDB?

IndexedDB is like a super-powered storage vault for your web apps. It’s a browser API that stores structured data and binary files (like images or videos) directly in the browser. Think of it like a local database that lives inside the browser, so you can store and pull data even when there’s no internet connection.

In simple terms, IndexedDB lets you keep your app running offline by storing the data it needs. It organizes data in a key-value pair format (kind of like JavaScript objects), which means it’s neat, easy to use, and fast to retrieve.

Step 1: Updating the App to Retrieve Data from an API

Here’s where we’re at: the app currently pulls data from local variables. But to make it offline-ready, we need to fetch real data from an API and store it in IndexedDB for future use.

Here’s what you need to do:

• Open the NewsItems.vue file and follow these steps:

Remove the Hardcoded Data:

Get rid of that old test data variable (testNewsItemsData). It’s not needed anymore because we’re about to fetch real data from the API.

Modify the getCustomizedTabNewsItems Function:

Find the function getCustomizedTabNewsItems inside the <script setup> tag. You’ll see a block of code like this:

const getCustomizedTabNewsItems = () => {
//…
if (definedCustomizations) {
//…
/** TODO: Remove the line below after setting up your API KEY and delete this comment */
newsItems.value = [
{
source: { id: ‘buzzfeed’, name: ‘Buzzfeed’ },
//…
},
];
/** TODO: Uncomment after setting up your API KEY */
// const { fetchedNewsItems, getNewsItems } = useNewsItems(requestUrl);
// nextTick(async () => {
// await getNewsItems();
// newsItems.value = fetchedNewsItems.value;
// });
}
};

Uncomment the Code to Fetch API Data:

After setting up your API key, uncomment the code that fetches the real-time data. This will allow your app to get news from the News API instead of using static placeholders.

Update the Logic for Custom News Categories:

Replace the hardcoded test data with API data:

if (props.tab.id === APPLICATION_TABS[2].id && props.retrieveCustomCuratedContent) {
// …
} else if (props.tab.id === APPLICATION_TABS[2].id && !props.retrieveCustomCuratedContent) {
// …
} else {
// Get news items for other tabs…
/** TODO: Remove the line below after setting up your API KEY and delete this comment */
newsItems.value = testNewsItemsData;
/** TODO: Uncomment after setting up your API KEY */
// const { fetchedNewsItems, getNewsItems } = useNewsItems(requestUrl);
// nextTick(async () => {
// await getNewsItems();
// newsItems.value = fetchedNewsItems.value;
// });
}

Open the SourceToggleTokens.vue file and uncomment the necessary code that allows the app to fetch news from different sources.

Step 2: Installing and Setting Up IndexedDB with idb

Now for the fun part: IndexedDB. But before we jump in, we need some help. That’s where idb comes in. It’s a lightweight wrapper around IndexedDB that makes it way easier to work with.

Run the following command to install idb using npm:

$ npm install idb

This package helps you interact with IndexedDB without all the complicated stuff.

Step 3: Creating the useIDB Composable

Next, let’s create a new composable file called useIDB.js. This file will handle all the magic of interacting with IndexedDB.

Here’s the basic setup:

import { openDB } from ‘idb’;
import { ref } from ‘vue’;const versionNumber = ref(1);const useIDB = () => {
const db = ref(null); const getDB = async (version, objectStoreName, keyPath) => {
versionNumber.value += 1;
db.value = await openDB(‘whats-new’, version, {
upgrade(db, oldVersion) {
if (version === 1 && oldVersion === 0) {
db.createObjectStore(objectStoreName, { keyPath });
}
if (version > 1) {
if (!db.objectStoreNames.contains(objectStoreName)) {
db.createObjectStore(objectStoreName, { keyPath });
}
}
},
});
}; return { db, versionNumber, getDB };
};export default useIDB;

What’s Going on Here?

• db: This is a reference to your IndexedDB database.
• getDB: This function opens or creates an IndexedDB database. It requires a version, object store name, and a key path (a unique identifier for each record).
• versionNumber: This keeps track of the database version. It ensures that the database is updated correctly when changes are made.

Step 4: Storing and Retrieving Data from IndexedDB

Now, let’s get to the fun part. You can store and retrieve data from IndexedDB.

Here’s how you’ll update the getNewsItems function in useNewsItems.js to store the fetched data in IndexedDB:

async function getNewsItems() {
const { db, getDB, versionNumber, getDataFromObjectStore } = useIDB(); try {
const apiResponse = await fetch(url.value, {
headers: {
‘X-Api-Key’: import.meta.env.VITE_NEWS_API_KEY,
},
}); const data = await apiResponse.json();
// Store fetched items in IndexedDB
await getDB(versionNumber.value, url.value, ‘url’);
data.articles.forEach(async (article) => {
await db.value.put(url.value, article);
});
} catch (error) {
if (error instanceof TypeError && error.message.includes(‘Failed to fetch’)) {
const cachedItems = await getDataFromObjectStore(url.value);
fetchedNewsItems.value = cachedItems;
}
}
}

How It Works:

• If the API fetch is successful, the articles are stored in IndexedDB.
• If the fetch fails (e.g., no internet), the app pulls the data from IndexedDB, so it keeps working offline.

Step 5: Testing Your Changes

Let’s make sure everything is working smoothly. Rebuild the app:

$ npm run build
$ npm run preview

Once the build is ready, open the app and go to the IndexedDB section in your browser’s developer tools. You should see your data stored there!

To test offline functionality, set the network throttling to offline in the Network tab and refresh the page. If everything’s set up right, you should see the news items loading from IndexedDB, even without an internet connection.

Congrats! You’ve just added offline functionality to your PWA, making it able to cache dynamic data from the News API using IndexedDB. Now, whether users are online or offline, they’ll still be able to enjoy your app. Nice work!

IndexedDB API Documentation

Conclusion

In conclusion, transforming a Vue.js app into a fully functional Progressive Web App (PWA) with Vite, a service worker, and IndexedDB significantly enhances the user experience by enabling offline access and improved performance. By following the steps outlined in this tutorial, you’ve learned how to configure your app’s manifest, set up a service worker, and utilize IndexedDB for caching both static and dynamic data. This setup ensures that users can seamlessly interact with your app, even without an internet connection.Looking ahead, as web technologies continue to evolve, the integration of PWAs will play an even more critical role in creating fast, reliable, and engaging web experiences. Stay up-to-date with emerging trends in PWA development to keep your app at the forefront of web innovation.

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