Tag: Programming

Introduction

Prompting and GPT-4
Main argument: Prompting is programming for the masses

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The recent release of GPT-4 has taken the tech world by storm, providing powerful AI-driven solutions that transform how we work and interact with technology.

Recommended: GPT-4 is Out! A New Language Model on Steroids

One such groundbreaking innovation is “Prompting”, a term that refers to AI-assisted code completion and generation. As more people become familiar with this concept, it’s becoming increasingly evident that Prompting is not just a novel feature, but a game-changing revolution in programming.

In this article, we will explore the idea that Prompting is, in fact, programming for the masses.

By examining the evolution of programming technologies over the years and the impact of Prompting on the programming landscape, we will demonstrate how this new paradigm democratizes programming and opens up new opportunities for people from all walks of life.

So, without further ado, let’s dive into the world of Prompting and discover how it reshapes the future of programming.

Evolution of Programming Technologies

Evolution of programming technologies, from punch cards to AI-assisted prompting
Each abstraction layer 10x’d the number of programmers and broadened the spectrum of activities considered programming

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To fully understand the significance of Prompting and its role as programming for the masses, it’s essential to take a step back and examine the evolution of programming technologies over the years.

I’ll give you a completely personal view on the history of programming language evolution going from manipulating “0”s and “1”s towards natural language programming:

• [1940s-1950s] Punch Cards
• [1950s-1970s] Assembly Languages
• [1970s-1990s] Low-level C
• [1980s-2000s] Higher-level C++, Java
• [1990s-2020s] Intuitive Python
• [2000s-2020s] Smart IDEs + Code Generation
• [2010s-2020s] Machine Learning Frameworks
• [2020s+] Prompting and AI-driven Code Assistance

Since the early days of computing, several key milestones have been in developing programming languages and tools.

Each new abstraction layer has made programming more accessible, enabling more people to participate in the field and broadening the range of activities that can be considered programming.

Here’s my rough estimate of the number of programmers in each “age”:

Skill	Approximate Number of People
Punch Cards	10,000
Assembly	100,000
C Programming	1,000,000
C++ or Java	10,000,000
Python	100,000,000
Automatically Generate Code	200,000,000
Prompting (GPT-based coding)	2,000,000,000

The journey began in the 1940s and 1960s with punch cards, which allowed programmers to encode instructions for early computers. Assembler languages soon followed, providing mnemonic codes that represented machine instructions, making programming more human-readable.

The 1970s and 1980s saw the introduction of low-level C, which allowed for greater abstraction and more flexibility in programming. Higher-level languages like C++ and Java emerged in the 1980s and 2000s, further simplifying the programming process and opening up new possibilities for software development.

With its intuitive and beginner-friendly syntax, Python came into the picture in the 2000s and 2010s, making programming even more accessible to a wider audience.

The introduction of smart IDEs and code generation tools in the 2010s and 2020s further streamlined the programming process, allowing developers to work more efficiently and effectively.

With the advent of Prompting in the 2020s, we’re witnessing the next major leap in the evolution of programming technologies. By leveraging AI-driven code completion and generation, Prompting is breaking down barriers and enabling even non-programmers to participate in creating and customizing software applications.

This new layer of abstraction is set to change the programming landscape profoundly, expanding the reach of programming like never before.

The total addressable market (TAM) of programming is not in the millions, tens of millions, or even hundreds of millions. Prompting has paved the road for the billions!

Comparing Prompting to Googling

Prompting vs Googling as essential skills in the tech world
Prompting goes beyond just being a skill to learn, but a paradigm shift in programming

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As Prompting gains traction, many in the tech industry are drawing parallels between it and Googling, the now-ubiquitous skill of searching for information online.

Indeed, both skills have become increasingly important in our digital age, and learning to use them effectively can greatly enhance one’s ability to solve problems, access knowledge, and innovate. However, likening Prompting to Googling does not fully capture the transformative power of this new technology.

While Googling is an essential skill that enables users to find answers to questions and access a wealth of information at their fingertips, Prompting represents a more profound shift in the programming world.

Rather than merely being another skill to learn, Prompting is a paradigm shift that transforms how we approach programming. It effectively democratizes the process, allowing individuals with little or no programming experience to create, modify, and deploy software applications.

Everybody with an idea can now spin up an app easily and effectively. More importantly, everybody can create an app unique to their needs.

In essence, Prompting does for programming what Googling does for information retrieval. It simplifies and streamlines the process, making it more accessible and intuitive for a broader audience. This, in turn, fosters innovation and creativity, as more people can engage in programming and contribute their ideas to the world of technology.

By breaking down barriers and empowering individuals from all walks of life, Prompting is redefining the nature of programming and expanding its reach to encompass a greater range of activities and participants.

The Impact of Prompting on the Programming Landscape

Democratization of programming and the expansion of the total addressable market (TAM) for programmers
Prompting lowers the barrier to entry for programming, allowing more people to participate and innovate

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As Prompting continues to revolutionize the way we approach programming, its impact on the programming landscape is becoming increasingly evident.

One of the most significant changes brought about by Prompting is the democratization of programming, which has led to an expansion of the total addressable market (TAM) for programmers. With the introduction of this new layer of abstraction, a wider range of individuals can now participate in programming, regardless of their background or prior experience.

Prompting lowers the barrier to entry for programming by simplifying complex tasks and providing AI-driven code completion and generation.

For example, the following gives me a Python script to calculate the ROI of investing in broad index funds:

This enables even non-programmers to create software applications with relative ease, allowing them to bring their ideas to life without being limited by a lack of technical expertise. As a result, we can expect to see a surge of new innovations, as more people gain the ability to contribute their unique perspectives and skills to the world of technology.

But this is not all – instead of writing a Python program that does it, you can simply ask ChatGPT to do it!

 Prompt: Give me a table of investment results when investing $10,000 for 40 years at a 9% annual ROI!

So not only has traditional programming become easier and more accessible, it is often not needed because ChatGPT can do the actual work.

Recommended: ChatGPT at the Heart – Building a Movie Recommendation Python Web App in 2023

The rise of Prompting also has implications for education and workforce development. As programming becomes more accessible to the masses, the demand for coding education will likely increase, with more people seeking to learn programming skills to stay competitive in the job market.

This could lead to a shift in the way programming is taught, with a greater emphasis on using AI-driven tools like Prompting, alongside traditional programming languages and techniques.

Furthermore, the growing prevalence of Prompting may also change the way companies hire and develop talent. With programming becoming more accessible, companies may place less emphasis on formal coding education and experience, instead focusing on an individual’s ability to leverage AI-assisted tools like Prompting to solve problems and innovate.

This could lead to a more diverse and inclusive tech industry, as individuals from various backgrounds can contribute their talents and ideas.

The Future of Prompting and its Implications

Future developments and improvements in Prompting technology
Potential impact on education, job markets, and the tech industry

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As Prompting technology continues to evolve and improve, we can expect its impact on the programming landscape to become even more profound.

Future developments in AI-driven code completion and generation tools may lead to even greater levels of abstraction, further simplifying the programming process and enabling more people to engage with technology in new and exciting ways.

One potential growth area is integrating Prompting tools with other technologies, such as

• augmented and virtual reality,
• IoT devices,
• spreadsheets,
• games, and
• voice assistants.

This could give rise to new forms of interaction and collaboration, enabling people to create and modify software applications in more intuitive and immersive ways.

With Microsoft’s ChatGPT Bing integration, we already see how massive billion-dollar industries such as search engines now have “ChatGPT at the heart”.

Another possibility is the development of more advanced and specialized Prompting tools tailored to specific industries or use cases.

This could lead to greater customization and personalization in software development, as individuals can leverage AI-driven tools to create bespoke applications that cater to their unique needs and preferences.

You can now start a massive business from your garage, leveraging infinite artificial intelligence to create insane value.

As Prompting becomes increasingly prevalent, it may also drive changes in how programming languages and frameworks are designed. Language creators may focus on developing more AI-friendly languages, allowing seamless integration with Prompting tools and enabling developers to work more efficiently and effectively.

Ultimately, the rise of Prompting holds the potential to reshape the entire tech industry, from education and workforce development to how we design and interact with technology. By democratizing programming and making it accessible to a wider audience, Prompting is ushering in a new era of innovation and creativity, empowering individuals from all walks of life to contribute their ideas and talents to the world of technology.

Getting Started with Prompting

7 Effective Prompting Tricks for ChatGPT
Explore Prompting and leverage it to enhance their coding skills and productivity

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As the programming world continues to evolve, individuals interested in technology must keep up with the latest advancements and learn how to harness the power of AI-driven tools like Prompting.

To help you get started, we’d like to introduce a comprehensive blog tutorial that can guide you through the process:

Recommended: 7 Effective Prompting Tricks for ChatGPT

For your convenience, I summarized the article using simple prompting:

By learning to leverage the power of AI-assisted code completion and generation, you’ll be well on your way to enhancing your coding skills and boosting your productivity.

I encourage you to explore the world of Prompting and experiment with how it can help you create, modify, and deploy software applications. As you familiarize yourself with this cutting-edge technology, you’ll be well-equipped to stay ahead of the curve and make your mark in the rapidly evolving programming landscape.

Conclusion

As you’ve explored throughout this article, Prompting represents a fundamental paradigm shift in the programming landscape, ushering in a new era of programming for the masses.

By leveraging AI-driven code completion and generation tools, Prompting is democratizing programming, making it more accessible and intuitive for a wider range of individuals.

You’ve learned how the evolution of programming technologies has paved the way for this paradigm shift, with each new layer of abstraction increasing the number of programmers and broadening the scope of what can be considered programming. The rise of Prompting is set to further expand the reach of programming, empowering more people to engage with technology and contribute their unique ideas and talents.

As you embark on your journey into the world of Prompting, remember that this technology holds the potential to reshape not only the way you work but also the entire tech industry. By embracing the change and learning to harness the power of AI-driven tools like Prompting, you’ll be well-positioned to thrive in this new era of programming for the masses.

Action! So, go forth, explore the potential of Prompting, and become part of the next revolution in programming. The future is bright, and the possibilities are endless!

Definitely download the prompting cheat sheet I created here:

Recommended: Free ChatGPT Prompting Cheat Sheet (PDF)

ChatGPT is a powerful AI conversation model that can assist you in generating various types of text content. But to get the best results, you need to give clear and specific prompts.

Here are 7 prompting strategies that can help you get the most out of ChatGPT:

Trick #1 – Define ChatGPT’s Role

ChatGPT can play different roles, such as a tour guide, philosopher, or translator. To get the desired result, give ChatGPT a prompt that specifies the role it needs to play.

Example:

"I want you to act as a tour guide. I’ll write you my location, and you’ll suggest a place for me to visit near my location."

I also created a fun way to role-play a mastermind group with your personal heroes. You can check it out in this article — I promise it’ll be worth your time:

Recommended: What Would Jesus Say? Creating a ChatGPT Mastermind with Jesus, Gandhi, Musk, and Gates

Trick #2 -Define Target Group and Communication Channel

To avoid tedious rewriting, give ChatGPT as much information as possible about the target group and the communication channel. Tell the AI how to address the reader and for which channel the text is intended.

Example:

"I need a script for a TikTok about the opportunities and risks of ChatGPT. Use short sentences. Address the audience directly. Use gender-neutral language."

Trick #3 -Chained Prompting

Break up complex tasks into several intermediate steps, hoping the AI will generate a more concrete, customized, and better result.

Example:

"Write an article about ChatGPT. First give me the outline, which consists of a headline, a teaser, and several subheadings. ... (possibly wait for generated output) ... Now write five key messages for each subheading. Add five keywords to the key messages for each subheading."

I think this is one of the best hacks of prompt engineering. Trial. Error. Iteration. You’ll often get your desired output quickly and efficiently and get better at it in no time!

Trick #4 -Create Content Variations

Prepare the same content for different channels such as LinkedIn, Twitter, or Facebook. The text should be adapted to the tone and formatting of the target channel.

Example:

"Formulate the generated text as a LinkedIn post. Keep in mind that the maximum length is 3000 characters. Structure the main points of the text into a bulleted list. Start with an exciting teaser sentence and end with a call to action for more engagement."

Trick #5 -Format Output

ChatGPT replies in plain text by default, but it can handle formatting in the Markdown markup language, such as headings, bold or italic text, ordered or unordered lists, and even tables.

Example:

"I need a blog post about ChatGPT. Write a headline, a teaser, a subtitle, and a paragraph. Format everything in Markdown."

Trick #6 -Generate Prompt Instructions

Instruct ChatGPT to take on a specific role and ask itself the questions it needs to answer in the next prompts.

Example:

"You are a robot for creating prompts. You need to gather information about the user’s goals, examples of preferred output, and any other relevant contextual information. The prompt should contain all the necessary information provided to you. Ask the user more questions until you are sure you can create an optimal prompt."

Trick #7 -Extract Structured From Unstructured Data

Extract structured data from unstructured data by specifying a desired output format (e.g., CSV) with one example output. This can help you in data preprocessing.

Example:

Extract house pricing data from the following text. Text: """
A 100 square meter house I recently visited in Florida costs $1 million dollars. I was surprised as my own 90 square meter house in Florida costs only $100 thousands USD. Compare this to the house of my friend ($500000 USD for 110 square meter). """ Desired output format: """
House 1 | $1,000,000 | 100 sqm """

Conclusion

ChatGPT is a versatile, mind-blowing tool that can assist you in generating various types of text content. However, you need to give clear and specific prompts to get the best results.

These 7 prompting strategies can help you get the most out of ChatGPT and produce high-quality content.

Recommended: Free ChatGPT Prompting Cheat Sheet (PDF)

Hold onto your hats, folks! OpenAI has just introduced GPT-4, a colossal leap forward in deep learning.

This large multimodal model is more than just a text-based smarty-pants; it can process both images and text, producing text outputs that are nothing short of impressive.

Showdown: At the end of this article, I’ll let both GPT-3.5 and GPT-4 write a short story. You’ll be mind-blown by the difference!

But how does it stack up against its predecessor, GPT-3.5?

In this beginner-friendly blog post, we’ll break down the key differences, explain why GPT-4 is such a big deal, and give you some mind-blowing statistics to showcase its extraordinary capabilities.

Recommended: Read the full paper here.

GPT-4: The Superstar Student

Picture this: GPT-4 just took a simulated bar exam and scored in the top 10% of test-takers! To put that in perspective, GPT-3.5’s score was around the bottom 10%.

That’s a massive improvement, and it’s all thanks to OpenAI’s tireless efforts to fine-tune GPT-4 over six months using lessons from adversarial testing and ChatGPT. The result? Improved factuality, steerability, and adherence to guardrails.

Supercomputer Stack

The brainpower behind GPT-4 isn’t all software – there’s some serious hardware at play too.

Over the past two years, OpenAI rebuilt its entire deep learning stack, partnering with Azure to create a supercomputer specifically designed for their workload.

GPT-3.5 was like a test run — an impressive one nonetheless — allowing the team to work out the kinks and refine their approach.

And boy, did they succeed!

GPT-4’s training was stable and predictable, making it the first large model with performance that could be accurately forecasted ahead of time.

Text Input Capability: Now Available for Everyone!

GPT-4’s text input capabilities have been released via ChatGPT and the API (with a waitlist), so everyone can enjoy its remarkable advancements.

OpenAI is also working on its image input capability, collaborating with a single partner to prepare it for wider availability.

Plus, they’re open-sourcing OpenAI Evals, a framework for evaluating AI model performance, so you can help guide future improvements.

GPT-4 vs. GPT-3.5: The Showdown

When it comes to casual conversation, the differences between GPT-3.5 and GPT-4 might seem subtle. But the magic happens when the task complexity increases.

GPT-4 is more reliable, creative, and capable of handling nuanced instructions than GPT-3.5.

OpenAI tested both models on various benchmarks, including simulated exams designed for humans. GPT-4’s performance was nothing short of astonishing.

Above 50% is super-human average performance!

Beyond English: Multilingual Mastery

GPT-4 doesn’t just excel in English; it shines in other languages too.

OpenAI tested GPT-4 using the MMLU benchmark, a suite of 14,000 multiple-choice problems across 57 subjects, translated into various languages.

In 24 of 26 languages tested, GPT-4 outperformed GPT-3.5 and other large language models—even in low-resource languages like Latvian, Welsh, and Swahili!

A Handy Helper in the Office

GPT-4 isn’t just flexing its muscles in the lab; it’s making a real-world impact too.

OpenAI has been using GPT-4 internally for support, sales, content moderation, programming, and even evaluating AI outputs. It’s a versatile and powerful tool that’s ready to revolutionize the way we work.

GPT-4 is significantly better than previous versions in a wide variety of general-purpose skills:

Visual Inputs: A Sneak Peek into the Future

But wait, there’s more!

GPT-4 can also handle visual inputs, allowing users to prompt the model with both text and images. This means it can generate text outputs based on a variety of image types, like documents with text and photographs, diagrams, or screenshots.

GPT-4’s capabilities in this domain are similar to its text-only prowess and can be enhanced with test-time techniques developed for text-only language models, such as few-shot and chain-of-thought prompting.

Keep in mind that image inputs are still in the research preview stage and not yet publicly available.

Conclusion

In a nutshell, GPT-4 is the AI game-changer we’ve all been waiting for.

With human-level performance on various professional and academic benchmarks, impressive multilingual capabilities, and the ability to process both text and image inputs, it’s easy to see why GPT-4 is making waves in the AI community.

As OpenAI continues to refine and expand GPT-4’s capabilities, we can expect even more groundbreaking advancements in the near future. So, stay tuned, AI has never been more exciting!

Recommended: ChatGPT at the Heart – Building a Movie Recommendation Python Web App in 2023

Interesting Facts and Stats GPT-4

1. GPT-4 is a multimodal model, capable of processing both text and image inputs.
2. It has shown human-level performance on various professional and academic benchmarks.
3. On a simulated bar exam, GPT-4 scored in the top 10% of test-takers, while GPT-3.5 scored in the bottom 10%.
4. OpenAI spent six months fine-tuning GPT-4 using lessons from adversarial testing and ChatGPT.
5. GPT-4 was tested on the MMLU benchmark, a suite of 14,000 multiple-choice problems across 57 subjects.
6. In 24 of the 26 languages tested on the MMLU benchmark, GPT-4 outperformed GPT-3.5 and other large language models.
7. OpenAI used GPT-4 internally for various tasks, including support, sales, content moderation, and programming.
8. GPT-4 can generate text outputs from inputs consisting of interspersed text and images.
9. The development of GPT-4 involved rebuilding OpenAI’s entire deep learning stack and co-designing a supercomputer with Azure.
10. GPT-4 considerably outperforms existing large language models and most state-of-the-art models on traditional benchmarks designed for machine learning models.

Appendix: GPT-3.5 vs GPT-4 Short Story

To showcase the new capabilities of GPT-4, I let both write a short story with the following prompt:

Write a funny soccer story for kids that teaches the kid that persistence is key for success in the style of Rowling.

Here’s the output of GPT-3.5:

Once upon a time, in a small village in England, there was a young boy named Harry who loved playing soccer more than anything in the world. He dreamed of one day playing for the famous Manchester United team, but he wasn't very good at it. Harry would spend hours every day practicing his soccer skills, but no matter how much he tried, he just couldn't seem to improve. His kicks were weak, his passes were sloppy, and he always seemed to trip over his own feet. One day, Harry heard that the Manchester United team was holding tryouts in his town. He knew that he wasn't good enough to make the team, but he decided to give it a try anyway. He put on his favorite soccer jersey, laced up his cleats, and headed to the field. When he arrived, he saw hundreds of other kids just like him, all vying for a chance to play for Manchester United. Harry watched as the other kids showed off their skills, dribbling the ball with ease, making perfect passes, and scoring goal after goal. Harry knew that he couldn't compete with those kids, but he refused to give up. He stepped up to the ball, took a deep breath, and kicked it with all his might. The ball flew into the air, wobbling and spinning wildly, and Harry watched in horror as it sailed straight into the goalpost and bounced back toward him. But Harry didn't give up. He chased after the ball, ignoring the snickers and jeers of the other kids, and kicked it again. And again. And again. Each time, the ball sailed in a different direction, bouncing off the other kids, the goalposts, and even the referee's head. But Harry didn't care. He was having the time of his life, and he refused to let anyone else bring him down. He kicked the ball with all his might, laughing and shouting and cheering himself on. And then, something amazing happened. The Manchester United coach, who had been watching the tryouts from the sidelines, walked up to Harry and tapped him on the shoulder. "Son," the coach said, "I've never seen anyone as persistent as you. You may not be the best soccer player on this field, but you have heart. And that's something that can't be taught." And with that, the coach offered Harry a spot on the Manchester United team. Harry couldn't believe his ears. He had made it! He had achieved his dream! From that day on, Harry worked harder than ever before, practicing every day and pushing himself to be the best he could be. And in the end, his persistence paid off. He became one of the greatest soccer players in the world, known not just for his skill, but for his determination and his unwavering spirit. So remember, kids: persistence is key. If you want to achieve your dreams, you can't give up. You have to keep pushing, keep trying, and keep kicking that soccer ball with all your might. Who knows? Maybe one day, you'll be the next Harry of the soccer world!

And here’s the impressive output of GPT-4, notice the unique use of words and creative ideas and turns in the storyline!

Once upon a time in the magical land of Fussballia, there lived a young and spirited soccer player named Jorrick Kickalot. Jorrick was no ordinary soccer player. He had an extraordinary talent of kicking soccer balls with the precision of a master archer. However, his small size made it difficult for him to make the local team, the Fussballia Falcons. Jorrick's best friend, a tiny but wise owl named Hootius Whistlebeak, would always accompany him to the games. One day, Hootius spotted a secret sign in the sky. It was an invitation to the legendary Flying Cleat Cup, a mysterious soccer tournament that only appeared once every 100 years. "Jorrick, this is your chance to prove yourself!" said Hootius excitedly, flapping his wings in anticipation. Together, they embarked on a long and treacherous journey to the secret location of the Flying Cleat Cup. They encountered peculiar creatures, such as the Zigzag Zephyrs, who could change the direction of the wind with a flick of their tails, and the elusive Balliphants, giant soccer balls with elephant trunks. Finally, they reached the hidden stadium, where they found teams of all shapes, sizes, and magical abilities. Jorrick felt nervous and unsure, but he remembered Hootius' words: "Persistence is the key to success!" The tournament began with a grand fanfare. In the first match, Jorrick and his makeshift team of magical creatures faced off against the towering Stomposaurs, a team of dinosaur-like creatures with powerful legs. Despite their intimidating size, Jorrick and his teammates worked together, and he managed to score the winning goal with a perfectly placed kick. In the next round, they faced the clever Whizbang Wizards, a team of magical beings who could make the ball disappear and reappear in the most unexpected places. Jorrick, however, was not to be outwitted. With Hootius' help, he kept a sharp eye on the ball and successfully led his team to another victory. The final match was against the reigning champions, the Enchanted Elves, known for their unmatched speed and agility. Jorrick was exhausted and felt the weight of the pressure on his shoulders. His legs were heavy, and his heart pounded in his chest. As the match began, the Enchanted Elves darted around the field, scoring goal after goal. It seemed like all hope was lost. But then, Hootius flew down to Jorrick and whispered, "Remember, persistence is the key to success!" With renewed determination, Jorrick summoned every ounce of strength he had left. He dodged, weaved, and kicked with all his might. Miraculously, he scored an equalizer in the final minutes of the game, sending it into extra time. In the final moments, Jorrick took a deep breath and prepared for a corner kick. He spotted Hootius in the stands, who gave him a reassuring nod. Jorrick took a step back, and with a powerful swing of his leg, sent the ball soaring through the air. The ball curved around the defenders, flew past the goalie, and nestled perfectly into the corner of the net. The stadium erupted in cheers as Jorrick's team celebrated their unexpected victory. They had won the Flying Cleat Cup, and Jorrick had proven that size did not matter when it came to playing soccer. Jorrick and Hootius returned home to Fussballia, where they were greeted as heroes. And from that day on, Jorrick Kickalot became a legend, not only for his incredible skills but also for his unwavering persistence. Young soccer players from all over the land would come to watch Jorrick play and learn from him. They admired not just his talent, but also his determination and grit. Jorrick always took the time to share his wisdom with the aspiring athletes, reminding them of the important lesson he had learned: "Persistence is the key to success!" As for Hootius Whistlebeak, he became the official mascot of the Fussballia Falcons, always encouraging the players to never give up, no matter the odds. Years later, the story of Jorrick Kickalot and his magical adventure at the Flying Cleat Cup became a beloved tale in Fussballia. Parents would tell their children the story of the young soccer player who, against all odds, achieved greatness through his sheer persistence and determination. And so, the legacy of Jorrick Kickalot lived on, inspiring generations of young soccer players to believe in themselves and never give up, no matter how tough the challenges they faced. They knew that if they could persist like Jorrick, they too could achieve their dreams and become the best soccer players they could be.

Django is a backend web framework that makes it easy to build web pages quickly using Python. There is no better way to learn Django than by building projects. In this tutorial, I will show you how I built an Artificial Intelligence-powered web assistant using Django.

Set Up

To get started, we will create a directory where every file will live in. In the directory, we will create and activate a virtual environment. Then we will install the required Python libraries for the project. I am using an Ubuntu terminal, so a basic knowledge of the command line will be an added advantage going forward.

mkdir project && cd project
python3 -m venv .venv
source .venv/bin/activate

In the project directory, we create and activate a virtual environment using the source command. You can also replace the source command with a dot .. Let’s now install the modules we will be using.

pip install django tzdata openai

Creating Django Project

Once the installation is complete, run the following command in your Ubuntu terminal to create a Django project.

django-admin startproject webassistant .

This creates a folder with the name webassistant.

• The . tells Django to create the project in the current directory.
• The manage.py file is used to execute several Django commands.
• The settings.py in the webassistant folder is the project’s settings. In it, we will register the Django apps we are about to create.
• The urls.py is where we will let Django know what it should display to the user.

We now check to ensure that the installation went successfully. In your terminal run the following command:

python3 manage.py runserver

Once you have seen the above image, congrats! You have successfully installed Django. You can use control C to close the server.

Creating Django Apps

Back to your terminal, run the following command to create a Django app.

python3 manage.py startapp assistant

Use the ls command to see what’s inside the assistant folder.

ls assistant
__init__.py admin.py apps.py migrations models.py tests.py views.py

The __init__.py file found in both the webassistant and assistant folders enables the folders to be imported as a Python package. The views.py is where we code what we want the browser to be displayed to the user. These files are what concern our project. To know more about other files, check the documentation.

Next, we go to the settings.py file, in INSTALLED_APPS section to register the name of the app we just created. Use the nano command.

nano webassistant/settings.py

...
INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', # custom app 'assistant',
]

We also open the project’s urls.py file to register the app-level URLs.

from django.contrib import admin
from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('', include('assistant.urls')),
]

The path() function is used to map the URL to the appropriate view. The include() function adds the URL of the app to the project-level urls.py. The empty quote means the home URL, that is, what we see when we run the local server.

If you have read Django tutorials including this one, you are gradually becoming familiar with the process. That’s how it is done in every Django application.

Getting the API Key

We need an API key to enable the OpenAI model to perform web assistant tasks for us. To get the API key, we first have to create an account on the official website of OpenAI. Once you have completed the signup process, go to the OpenAI API reference where you will be directed to a page to generate your API key.

Recommended: OpenAI API – or How I Made My Python Code Intelligent

Make sure you keep the API key safe. Create a file in your app-level folder and call it key.py.

API_KEY = 'YOUR SECRET API KEY'

Just replace the text in quotes with your own generated API key.

Integrating the OpenAI Model

To integrate the API with our Django application, create a file called engine.py in the app’s folder and input the following python script.

# engine.py from .key import API_KEY
import openai openai.api_key = API_KEY def model(request): prompt = request.POST.get('prompt') response = openai.Completion.create( engine='text-davinci-003', temperature=0.5 prompt=prompt, max_tokens=1000, ) text = response.choices[0].text chats = {'prompt': prompt, 'response': text } return chats

We import the API key and the openai module. We use the openai.api_key to load the API key. Then, in the function, we requested to get the prompt, which is the question asked by the user. We then return the response generated by the model in form of a dictionary.

The temperature affects the randomness of the output, and it’s between 0 and 1. The AI model employed to generate predictions is the text_davinci_003. The max_tokens specifies the maximum number of tokens or pieces of words that can be generated by the model.

To learn more about the parameters, perhaps this article can be of help. We will now import the function in our views.py file.

from django.shortcuts import render, redirect
from .engine import model def home(request): try: if request.method == 'POST': context = model(request) return render(request, 'home.html', context) else: return render(request, 'home.html') except: return redirect('error') def error_handler(request): return render(request, 'error.html')

Two functions indicate two separate HTML files. In the first function, we use a try statement to check the block of code for errors. If no errors were found, the code under the try statement will execute. But if there were errors, the code under the except statement will be executed.

Recommended: Python Try/Except Error Handling

The if statement checks if the request method is POST, if so, it will generate a response from the OpenAI model. But if otherwise, the else statement will be run in which no response will be generated.

The render() function renders or displays a response in the HTML files which we are yet to create. Notice that in the else statement, the render() function just renders the same homepage without the context because the request method was not POST. The redirect() function is used to redirect a user to another webpage.

Let’s now write a URL in the urls.py file to display our contents.

assistant/urls.py from django.urls import path
from .import views urlpatterns = [ path('', views.home, name='home'), path('error', views.error_handler, name='error_handler'),
]

The name argument is kind of an alias for the URL. So instead of writing long URLs, we can just reference them with the name given. Mostly used in HTML files.

Templates

We now want to render our templates. Create a folder named templates in the current directory. This is where we will keep our HTML files. Having created the folder, go to settings.py and let Django know that a templates folder is created.

In the settings.py file, scroll down to the ‘TEMPLATES’ section and add the following to DIRS.

…
TEMPLATES = [ { … 'DIRS': [os.path.join(BASE_DIR, 'templates')], … }
]

Be sure to import the os module. Then, create a file in the templates folder with the name base.html

<!DOCTYPE html>
<html lang="en">
<head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Web Assistant | {% block title %} {% endblock %}</title> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/css/bootstrap.min.css" rel="stylesheet">
</head>
<body> {% block content %} {% endblock %}
</body>
</html>

That’s our HTML boilerplate with bootstrap added to it for styling our web pages. Next is the home.html, the homepage that will inherit everything in the base.html template.

{% extends 'base.html' %}
{% block title %} Home {% endblock %}
{% block content %}
<div class="row justify-content-center my-4"> <div class="col-md-7 mt-4"> <div class="card"> <h1 class="card-header text-center">A.I WEB ASSISTANT</h1> <div class="card-body"> <pre>Hello, how can I help you?</pre> <form action="." method="POST"> <!-- this secures the form from malicious attacks during submission --> {% csrf_token %} <input class="form-control mb-2" required type="text" autofocus="autofocus" name="prompt" value="{{ prompt }}" id=""> <button class="btn btn-success fw-bold" type="submit"> GENERATE </button> </form> <hr> <pre> {{ response }} </pre> </div> </div> </div> </div>
</div>
{% endblock %}

Finally, the error.html will be displayed when an error occurs. It also inherits everything in the base.html.

{% extends 'base.html' %}
{% block title %} 404 {% endblock %}
{% block content %}
<div class="row justify-content-center my-4"> <div class="col-md-7 mt-4"> <h1>Page Not Found</h1> <p>Make sure you are connected to the internet or your query is correct</p> <a href="{% url 'home' %}" class="btn btn-secondary">Home</a> </div>
</div>
{% endblock %}

Certain things in these HTML files demand an explanation. Those strange syntaxes that begin with curly braces are Django templating language. When used with a block statement, it must end with an endblock statement. In base.html, we inserted the empty block statement in the title tag.

This makes it possible to override the home and error HTML files with a different word. But you can see the ‘Web Assistant’ remains the same in all files inheriting base.html.

The csrf_token is for security reasons. It’s compulsory. If you don’t add it, Django will throw an error. The prompt variable comes from the view.py file which in turn is imported from the engine.py file. The same applies to the response. Remember, we sent them here using the render() function.

The {% url 'home' %} syntax is Django’s way of displaying internal URLs. Go back to the app-level urls.py, you will see where we defined the name and this makes it possible to use it in HTML files.

Conclusion

Congrats on creating an AI-powered web assistant using Django. If you enjoy the tutorial, feel free to share it with others. Have a nice day.

Recommended: How I Created an URL Shortener App Using Django

As a Python developer, I love using Django for web development. Its built-in features and clear code structure make building scalable and robust web applications fast and efficient. In fact, I used Django to build my own web app for Python testing and training.

Here’s how you can install Django:

pip install django

Alternatively, you may use any of the following commands to install django, depending on your concrete environment. One is likely to work!

 If you have only one version of Python installed:
pip install django  If you have Python 3 (and, possibly, other versions) installed:
pip3 install django  If you don't have PIP or it doesn't work
python -m pip install django
python3 -m pip install django  If you have Linux and you need to fix permissions (any one):
sudo pip3 install django
pip3 install django --user  If you have Linux with apt
sudo apt install django  If you have Windows and you have set up the py alias
py -m pip install django  If you have Anaconda
conda install -c anaconda django  If you have Jupyter Notebook
!pip install django
!pip3 install django

Let’s dive into the installation guides for the different operating systems and environments!

How to Install Django on Windows?

To install the updated Django framework on your Windows machine, run the following code in your command line or Powershell:

• python3 -m pip install --upgrade pip
• python3 -m pip install --upgrade django

Here’s the code for copy&pasting:

python3 -m pip install --upgrade pip
python3 -m pip install --upgrade django

I really think not enough coders have a solid understanding of PowerShell. If this is you, feel free to check out the following tutorials on the Finxter blog.

Related Articles:

• Powershell Developer — Income and Opportunity
• Powershell Tutorial

How to Install Django on Mac?

Open Terminal (Applications/Terminal) and run:

• xcode-select -install (You will be prompted to install the Xcode Command Line Tools)
• sudo easy_install pip
• sudo pip install django
• pip install django

As an alternative, you can also run the following two commands to update pip and install the Django library:

python3 -m pip install --upgrade pip
python3 -m pip install --upgrade django

These you have already seen before, haven’t you?

Related Article:

• Pip Commands — A Simple Guide

How to Install Django on Linux?

To upgrade pip and install the Django library, you can use the following two commands, one after the other.

• python3 -m pip install --upgrade pip
• python3 -m pip install --upgrade django

Here’s the code for copy&pasting:

python3 -m pip install --upgrade pip
python3 -m pip install --upgrade django

How to Install Django on Ubuntu?

Upgrade pip and install the Django library using the following two commands, one after the other:

• python3 -m pip install --upgrade pip
• python3 -m pip install --upgrade django

Here’s the code for copy&pasting:

python3 -m pip install --upgrade pip
python3 -m pip install --upgrade django

How to Install Django in PyCharm?

The simplest way to install Django in PyCharm is to open the terminal tab and run the pip install django command.

This is shown in the following code:

pip install django

Here’s a screenshot of the two steps:

1. Open Terminal tab in Pycharm
2. Run pip install django in the terminal to install Django in a virtual environment.

As an alternative, you can also search for Django in the package manager.

However, this is usually an inferior way to install packages because it involves more steps.

How to Install Django in Anaconda?

You can install the Django package with Conda using the command conda install -c anaconda django in your shell or terminal.

Like so:

 conda install -c anaconda django 

This assumes you’ve already installed conda on your computer. If you haven’t check out the installation steps on the official page.

How to Install Django in VSCode?

You can install Django in VSCode by using the same command pip install django in your Visual Studio Code shell or terminal.

pip install django

If this doesn’t work — it may raise a No module named 'django' error — chances are that you’ve installed it for the wrong Python version on your system.

To check which version your VS Code environment uses, run these two commands in your Python program to check the version that executes it:

import sys
print(sys.executable)

The output will be the path to the Python installation that runs the code in VS Code.

Now, you can use this path to install Django, particularly for that Python version:

/path/to/vscode/python -m pip install django

Wait until the installation is complete and run your code using django again. It should work now!

Recommended: Django Developer — Income and Opportunity

More Finxter Tutorials

Learning is a continuous process and you’d be wise to never stop learning and improving throughout your life.

What to learn? Your subconsciousness often knows better than your conscious mind what skills you need to reach the next level of success.

I recommend you read at least one tutorial per day (only 5 minutes per tutorial is enough) to make sure you never stop learning!

If you want to make sure you don’t forget your habit, feel free to join our free email academy for weekly fresh tutorials and learning reminders in your INBOX.

Also, skim the following list of tutorials and open 3 interesting ones in a new browser tab to start your new — or continue with your existing — learning habit today!

Python Basics:

• Python One Line For Loop
• Import Modules From Another Folder
• Determine Type of Python Object
• Convert String List to Int List
• Convert Int List to String List
• Convert String List to Float List
• Convert List to NumPy Array
• Append Data to JSON File
• Filter List Python
• Nested List

Python Dependency Management:

• Install PIP
• How to Check Your Python Version
• Check Pandas Version in Script
• Check Python Version Jupyter
• Check Version of Package PIP

Python Debugging:

• Catch and Print Exceptions
• List Index Out Of Range
• Fix Value Error Truth
• Cannot Import Name X Error

Fun Stuff:

• 5 Cheat Sheets Every Python Coder Needs to Own
• 10 Best Python Puzzles to Discover Your True Skill Level
• How to $1000 on the Side as a Python Freelancer

Thanks for learning with Finxter!

Programmer Humor

Question: How did the programmer die in the shower? ☠

❗ Answer: They read the shampoo bottle instructions:
Lather. Rinse. Repeat.

• Join Discord: https://discord.com/invite/Cn7jWhTckP
• Join GitHub: https://github.com/shandralor/PeerBrain

I have a problem. I’m addicted to OpenAI. Every day I find new exciting ways to use it. It’s like somebody gave me a magic stick and I use it for stupid things like cleaning the kitchen. But I cannot help it! So, how to create images with OpenAI in Python? Easy, follow these four steps!

Step 1: Install the OpenAI Python Library

The first step to using OpenAI’s DALL·E in Python is to install the OpenAI Python library. You can do this using pip, a package manager for Python.

Open your terminal and enter the following command:

pip install openai

I have written a whole tutorial on this topic in case this doesn’t work instantly.

Recommended: How to Install OpenAI in Python?

Step 2: Create an OpenAI API Key

OpenAI is not free for coders — but it’s almost free. I only pay a fraction of a cent for a request, so no need to be cheap here.

Visit the page https://platform.openai.com/account/api-keys and create a new OpenAI key you can use in your code. Copy&paste the API key because you’ll need it in your coding project!

Step 3: Authenticate with OpenAI API Key

Next, you’ll need to authenticate with OpenAI’s API key. You can do this by importing the openai_secret_manager module and calling the get_secret() function. This function will retrieve your OpenAI API key from a secure location, and you can use it to authenticate your API requests.

import openai_secret_manager
import openai secrets = openai_secret_manager.get_secret("openai") # Authenticate with OpenAI API Key
openai.api_key = secrets["api_key"]

If this sounds too complex, you can also use the following easier code in your code script to try it out:

import openai # Authenticate with OpenAI API Key
openai.api_key = 'sk-...'

The disadvantage is that the secret API key is plainly visible to anybody with access to your code file. Never load this code file into a repository such as GitHub!

Step 4: Generate Your DALL·E Image

Now that you’re authenticated with OpenAI, you can generate your first DALL·E image. To do this, call the openai.Image.create() function, passing in the model name, prompt, and size of the image you want to create.

import openai # Authenticate with OpenAI API Key
openai.api_key = 'sk-...' # Generate images using DALL-E
response = openai.Image.create( model="image-alpha-001", prompt="a coder learning with Finxter", size="512x512"
) print(response.data[0]['url'])

In the code above, we specified the DALL·E model we wanted to use (image-alpha-001), provided a prompt for the image we wanted to create (a coder learning with Finxter), and specified the size of the image we wanted to create (512x512).

Once you’ve generated your image, you can retrieve the image URL from the API response and display it in your Python code or in a web browser.

print(response.data[0]['url'])

Conclusion

Using OpenAI’s DALL·E to generate images is a powerful tool that can be used in various applications. So exciting!

With just a few lines of Python code, you can create unique images that match specific text descriptions. By following the four easy steps outlined in this article, you can get started generating your own DALL·E images today.

Recommended: OpenAI’s Speech-to-Text API: A Comprehensive Guide

As promised in the previous article, we’ll get more closely familiar with the concept of scoping next. We’ll explain what scoping is, why it exists, and how it helps us in programming.

It’s part of our long-standing tradition to make this (and other) articles a faithful companion, or a supplement to the official Solidity documentation.

Scopes Overview

Scope refers to the context in which we can access a defined variable or a function. There are three main types of scope specific to Solidity:

• global,
• contract, and
• function scope.

In the global scope, variables, and functions are defined at the global level, i.e., outside of any contract or function, and we can access them from any place in the source code.

In the contract scope, variables and functions are defined within a contract, but outside of any function, so we can access them from anywhere within the specific contract. However, these variables and functions are inaccessible from outside the contract scope.

In the function scope, variables and functions are defined within a function and we can access them exclusively from inside that function.

Note:

The concept of scopes in Solidity is similar and based on the concept of scopes in the C99 programming language. In both languages, a “scope” refers to the context in which a variable or function is defined and can be accessed.

In C99 (a C language standard from 1999), variables and functions can be defined at either the global level (i.e., outside of any function) or within a function. There is no “contract” scope in C99.

Global Scope

Let’s take a look at a simple example of the global scope:

pragma solidity ^0.6.12; uint public globalCounter; function incrementGlobalCounter() public { globalCounter++;
}

In this example, the globalCounter variable is defined at the global level and is, therefore, in the global scope. We can access it from anywhere in the code, including from within the incrementGlobalCounter(...) function.

Reminder: Global variables and functions can be accessed and modified by any contract or function that has access to them. We can find this behavior useful for sharing data across contracts or functions, but it can also present security risks if the global variables or functions are not properly protected.

Contract Scope

As explained above, variables and functions defined within a contract (but outside of any function) are in contract scope, and we can access them from anywhere within the contract.

Contract-level variables and functions are useful for storing and manipulating data that is specific to a particular contract and is not meant to be shared with other contracts or functions.

Let’s take a look at a simple example of the contract scope:

pragma solidity ^0.6.12; contract Counter { uint public contractCounter; function incrementContractCounter() public { contractCounter++; }
}

In this example, the contractCounter variable is defined within the Counter contract and is, therefore, in contract scope. It is available for access from anywhere within the Counter contract, including from within the incrementContractCounter() function.

Warning: We should be aware that contract-level variables and functions are only accessible from within the contract in which they are defined. They cannot be accessed from other contracts or from external accounts.

Function Scope

Variables and functions that are defined within a function are in the function scope and can only be accessed from within that function.

Function-level variables and functions are useful for storing and manipulating data that is specific to a particular function and is not meant to be shared with other functions or with the contract as a whole.

Let’s take a look at the following example of the function scope:

pragma solidity ^0.6.12; contract Counter { function incrementCounter(uint incrementAmount) public { uint functionCounter = 0; functionCounter += incrementAmount; }
}

In this example, the functionCounter variable is defined within the incrementCounter(...) function and is, therefore, in the function scope. It can only be accessed from within the incrementCounter function and is not accessible from other functions or from outside the contract.

C99 Scoping Rules

Now, let’s take a look at an interesting example showing minimal scoping by using curly braces:

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.5.0 <0.9.0;
contract C { function minimalScoping() pure public { { uint same; same = 1; } { uint same; same = 3; } }
}

Each of the curly braces pair forms a distinct scope, containing a declaration and initialization of the variable same.

This example will compile without warnings or errors because each of the variable’s lifecycles is contained in its own disjoint scope, and there is no overlap between the two scopes.

Shadowing

In some special cases, such as this one demonstrating C99 scoping rules below, we’d come across a phenomenon called shadowing.

Shadowing means that two or more variables share their name and have intersected scopes, with the first one as the outer scope and the second one as the inner scope.

Let’s take a closer look to get a better idea of what’s all about:

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.5.0 <0.9.0;
// This will report a warning
contract C { function f() pure public returns (uint) { uint x = 1; { x = 2; // this will assign to the outer variable uint x; } return x; // x has value 2 }
}

There are two variables called x; the first one is in the outer scope, and the second one is in the inner scope.

The inner scope is contained in or surrounded by the outer scope.

Therefore, the first and the second assignment assign the value 1, and then value 2 to the outer variable x, and only then will the declaration of the second variable x take place.

In this specific case, we’d get a warning from the compiler, because the first (outer) variable x is being shadowed by the second variable x.

Warning: in versions prior to 0.5.0, Solidity used the same scoping rules as JavaScript: a variable declared at any location within the function would be visible through the entire function’s scope. That’s why the example below could’ve been compiled in Solidity versions before 0.5.0:

// SPDX-License-Identifier: GPL-3.0
pragma solidity >=0.5.0 <0.9.0;
// This will not compile
contract C { function f() pure public returns (uint) { x = 2; uint x; return x; }
}

The code above couldn’t compile in today’s versions of Solidity because an assignment to variable x is attempted before the variable itself is declared. In other words, the inner variable x‘s scope starts with the line of its declaration.

Conclusion

In this article, we learned about variable and function scopes.

• First, we made a scope overview, introducing ourselves to three different scopes in Solidity.
• Second, we investigated the global scope by studying an appropriate example.
• Third, we looked at the contract scope through an appropriate example.
• Third, learned about the function scope on an appropriate example.
• Fourth, we glanced at C99 scoping rules based on C99 – a C language standard.
• Fifth, we also learned about shadowing and got an idea of why we should be careful about it.

What’s Next?

This tutorial is part of our extended Solidity documentation with videos and more accessible examples and explanations. You can navigate the series here (all links open in a new tab):

Welcome to part 3 of this series, and thank you for sticking around!

• Part 1: The P2P Bootstrapping Problem – Starting My Journey To Help Build a P2P Social Network
• Part 2: BrainWaves P2P Social Network – How I Created a Basic Server

I’ve come to realize this might become a rather long series. The main reason for that is that it documents two things. This is the birth of an application and my personal journey in developing that application. I know parts 1 and 2 have been very wordy. This will change now. I promise that you will see a lot of code in this episode :-).

Database Code

So after that slight philosophical tidbit, it is time to dive into the actual database code. As I mentioned in the previous article, I chose to use Deta Space as the database provider. There are two reasons for this. The first is the ease of use and the second are its similarities to my favorite NoSQL database MongoDB.

Recommended: Please check my article on creating a shopping list in Streamlit on how to set it up. It takes only a few minutes.

For reference, the server directory with all the code to get the FastAPI server working looks as follows:

server
├── db.py
├── main.py
├── models.py
├── requirements.txt
├── .env

All the database code will live in the db.py file. For the Pydantic models, I’ll use models.py.

Database Functions

The database functions are roughly divided into three parts.

• We first need functionality for everything related to users.
• Next, we need to code that handles everything related to adding and managing friends.
• The third part applies to all the code for managing thoughts. Thoughts are Peerbrain’s equal of messages/tweets.

The file will also contain some helper functions to aid with managing the public keys of users. I’ll go into a lot more detail on this in the article about encryption.

To set up our db.py file we first need to import everything needed. As before, I’ll show the entire list and then explain what everything does once we write code that uses it.

"""This file will contain all the database logic for our server module. It will leverage the Deta Base NoSQL database api.""" from datetime import datetime
import math
from typing import Union
import os
import logging
from pprint import pprint #pylint: disable=unused-import
from uuid import uuid4
from deta import Deta
from dotenv import load_dotenv
from passlib.context import CryptContext

The #pylint comment you can see above is used to make sure pylint skips this import. I use pprint for displaying dictionaries in a readable way when testing. As I don’t use it anywhere in the actual code, pylint would start to fuss otherwise.

Tip: For those interested, pylint is a great tool to check your code for consistency, errors and, code style. It is static, so it can’t detect errors occurring at runtime. I like it even so.

After having imported everything, I first initialize the database. The load_dotenv() below, first will load all my environment variables from the .env file. 

load_dotenv() #---DB INIT---#
DETA_KEY = os.getenv("DETA_KEY")
deta = Deta(DETA_KEY)
#---#
USERS = deta.Base("users")
THOUGHTS = deta.Base("thoughts")
KEYS = deta.Base("keys_db")

Once the variables are accessible, I can use the Deta API key to initialize Deta. Creating Bases in Deta is as easy as defining them with deta.Base. I can now call the variable names to perform CRUD operations when needed.

Generate Password Hash

The next part is very important. It will generate our password hash so the password is never readable. Even if someone has control of the database itself, they will not be able to use it. Cryptcontext itself is part of the passlib library. This library can hash passwords in multiple ways..

#---PW ENCRYPT INIT---#
pwd_context = CryptContext(schemes =["bcrypt"], deprecated="auto")
#---#
def gen_pw_hash(pw:str)->str: """Function that will use the CryptContext module to generate and return a hashed version of our password""" return pwd_context.hash(pw)

User Functions

The first function of the user functions is the easiest. It uses Deta’s fetch method to retrieve all objects from a certain Base, deta.users, in our case.

#---USER FUNCTIONS---#
def get_users() -> dict: """Function to return all users from our database""" try: return {user["username"]: user for user in USERS.fetch().items} except Exception as e: # Log the error or handle it appropriately print(f"Error fetching users: {e}") return {}

The fact that the function returns the found users as a dictionary makes them easy to use with FastAPI. As we contact a database in this function and all the others in this block, a try–except block is necessary. 

The next two functions are doing the same thing but with different parameters. They accept either a username or an email.

I am aware that these two could be combined into a single function with an if-statement. I still do prefer the two separate functions, as I find them easier to use. Another argument I will make is also that the email search function is primarily an end user function. I plan to use searching by username in the background as a helper function for other functionality.

def get_user_by_username(username:str)->Union[dict, None]: """Function that returns a User object if it is in the database. If not it returns a JSON object with the message no user exists for that username""" try: if (USERS.fetch({"username" : username}).items) == []: return {"Username" : "No user with username found"} else: return USERS.fetch({"username" : username}).items[0] except Exception as error_message: logging.exception(error_message) return None def get_user_by_email(email:str)->Union[dict, None]: """Function that returns a User object if it is in the database. If not it returns a JSON object with the message no user exists for that email address""" try: if (USERS.fetch({"email" : email}).items) == []: return {"Email" : "No user with email found"} else: return USERS.fetch({"email" : email}).items[0] except Exception as error_message: logging.exception(error_message) return None

The functions above both take a parameter that they use to filter the fetch request to the Deta Base users.

If that filtering results in an empty list a proper message is returned. If the returned list is not empty, we use the .items method on the fetch object and return the first item of that list. In both cases, this will be the user object that contains the query string (email or username).

The entire sequence is run inside a try-except block as we are trying to contact a database.

Reset User Password

When working with user creation and databases, a function to reset a user’s password is required. The next function will take care of that.

def change_password(username, pw_to_hash): """Function that takes a username and a password in plaintext. It will then hash that password> After that it creates a dictionary and tries to match the username to users in the database. If successful it overwrites the previous password hash. If not it returns a JSON message stating no user could be found for the username provided.""" hashed_pw = gen_pw_hash(pw_to_hash) update= {"hashed_pw": hashed_pw } try: user = get_user_by_username(username) user_key = user["key"] if not username in get_users(): return {"Username" : "Not Found"} else: return USERS.update(update, user_key), f"User {username} password changed!" except Exception as error_message: logging.exception(error_message) return None 

This function will take a username and a new password. It will first hash that password and then create a dictionary. Updates to a Deta Base are always performed by calling the update method with a dictionary. As in the previous functions, we always check if the username in question exists before calling the update. Also, don’t forget the try-except block!

Create User

The last function is our most important one :-). You can’t perform any operations on user objects if you have no way to create them! Take a look below to check out how we’ll handle that.

def create_user(username:str, email:str, pw_to_hash:str)->None: """Function to create a new user. It takes three strings and inputs these into the new_user dictionary. The function then attempts to put this dictionary in the database""" new_user = {"username" : username, "key" : str(uuid4()), "hashed_pw" : gen_pw_hash(pw_to_hash), "email" : email, "friends" : [], "disabled" : False} try: return USERS.put(new_user) except Exception as error_message: logging.exception(error_message) return None

The user creation function will take a username, email, and password for now. It will probably become more complex in the future, but it serves our purposes for now. Like the Deta update method, creating a new item in the database requires a dictionary. Some of the necessary attributes for the dictionary are generated inside the function.

The key needs to be unique, so we use Python’s uuid4 module. The friend’s attribute will contain the usernames of other users but starts as an empty list. The disabled attribute, finally, is set to false. 

After finishing the initialization, creating the object is a matter of calling the Deta put method. I hear some of you thinking that we don’t do any checks if the username or email already exists in the database. You are right, but I will perform these checks on the endpoint receiving the post request for user creation.

Some Coding Thoughts and Learnings

One thing that never ceases to amaze me is the amount of documentation I like to add. I do this first in the form of docstrings as it helps me keep track of what function does what. I find it boring most of the time, but in the end, it helps a lot!

The other part of documenting that I like is type hints. I admit they sometimes confuse me still, but I can see the merit they have when an application keeps growing. 

We will handle the rest of the database function in the next article. See you there!

Participate in Building the Decentralized Social Brain Network

As before, I state that I am completely self-taught. This means I’ll make mistakes. If you spot them, please post them on Discord so I can remedy them . 

As always, feel free to ask me questions or pass suggestions! And check out the GitHub repository for participation!

GitHub: https://github.com/shandralor/PeerBrain