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LLC Koveh Development Studio

My team creates applications, APIs, websites, and telegram bots.

Why is it interesting

Every project is published at, with the process shown, and the pitfalls described. It is helpful for people, who want to know more about programming: the difficulties, the process itself, and most importantly: how much time and money should you spend on a project.

Join the Studio

If you are keen in programming, design, marketing, or finance you are welcome to join a team!


Daniil Kovekh


New projects

During this half a year, I created tens of apps: for programmers, language learners, lawyer apps, and tens of different things. I would like to share some of them with you for free.

Leakage App: A lifesaver for developers. Ever pushed code to GitHub only to realize you’ve included sensitive data? My Python tool scans through your repositories, sniffing out passwords, API keys, and other secrets. It neatly organizes them into an Excel sheet, where you can review and scrub the sensitive data with a single click. It’s the guardian your codebase needs.

Download Leakage App from GitHub

GitHub Access: Dive into my digital toolbox. From web scrapers to¬†AI bots, and¬†automation scripts to¬†speech conversion tools, I‚Äôm opening up my year‚Äôs worth of¬†creations. They‚Äôre yours to¬†tinker with¬†‚Äď all I ask is a¬†nod in¬†your Readme or¬†a¬†mention in¬†your code comments.

Koveh GitHub Repository


20 Websites with a Free Data for Machine Learning

these websites provide free data, as well as an API with a real time data.

FiveThirtyEight Data This site offers data and code related to the articles and graphics at FiveThirtyEight, covering sports, politics, economics, and more. It’s a great resource for data journalists and enthusiasts.

CERN Open Data Portal: This portal provides access to a portion of the data from CERN experiments, with tools, resources, and guidelines to help researchers and students.

NASA’s Earth Observing System Data and Information System: EOSDIS provides a range of Earth observation data from satellites, aircraft, and ground stations, supporting research about the planet’s climate and environment.

NASDAQ Data: Offers various datasets related to the financial market, including historical stock prices, company information, and financial analytics. The U.S. government’s open data site provides data, tools, and resources to conduct research, develop web and mobile applications, and design data visualizations.

Kaggle Datasets: Features a wide array of datasets for data science and machine learning, with user contributions and regular competitions.

Google Dataset Search: A tool to search for datasets across thousands of online repositories, useful for scholarly and academic research.

UCI Machine Learning Repository: Contains datasets specifically for machine learning, managed by the University of California, Irvine.

GitHub¬†‚Äď Awesome Public Datasets: A¬†comprehensive list of¬†public datasets on¬†GitHub, covering various subjects like government, finance, science, and¬†more.

Yahoo Finance: A comprehensive site offering free financial news, data, and resources. It’s particularly useful for historical and real-time stock data, financial reports, and market analysis.

NewsAPI: This API provides easy access to a range of news articles and headlines from various sources, ideal for aggregating news data for analysis or keeping up with current events.

Quandl: Offers a variety of financial, economic, and alternative data from multiple sources, useful for investment professionals and data analysts. A global registry of research data repositories across various academic disciplines, offering a comprehensive directory for researchers looking for data.

UK Government Data: Provides a vast range of UK government data, including information on education, health, economics, and more, for public use.

European Data Portal: Offers access to public data resources across Europe, including a wide variety of datasets in different languages and formats. A platform for modern data teamwork, providing a space where people can find, share, and collaborate on data.

International Monetary Fund Data: Provides access to macroeconomic statistics and financial data from countries all over the world, maintained by the IMF. Dedicated to making high value health data more accessible to entrepreneurs, researchers, and policymakers in the hopes of better health outcomes for all.

Open Energy Information: A knowledge-sharing online community dedicated to connecting people with the latest in open energy data.

Gapminder: Offers global data on various aspects like health, income, and environment, aiming to promote a fact-based world view.

2023   AI

What to read #7: Money

Money: An Unofficial Biography of Money by Felix Martin

Restored by

When I was 15, I received a¬†gift from my best friend, Max‚ÄĒa¬†book that would shape my understanding of¬†money. From that day on, my friends knew that books were the¬†way to¬†my heart.

Yap Island’s Unique Currency: Giant Stones and a Trust-Based Economy

In the remote Yap Island, found a hundred years ago, an extraordinary monetary system thrived. The inhabitants used enormous coin-like stones with holes in the center, known as Rai stones, as a form of currency. These stones were immovable, some as large as cars, and were quarried from distant islands and transported with great effort.

What made this system work was an intricate web of trust and communal understanding. Ownership of the stones could change without them physically moving. If someone wished to buy something, like 10 kilograms of fish, they would simply declare the transfer of ownership to the fisherman. Everyone in the community would acknowledge the new ownership and the transaction was complete.

The stones didn’t even need to be seen to hold value. In one legendary tale, a Rai stone was lost at sea during transport, but the community continued to recognize its value, and it remained part of the island’s currency system.

This unique approach to money, rooted in social trust and shared belief, challenges conventional economic thinking and provides a fascinating glimpse into how value can be ascribed and exchanged in diverse cultures.

The Birth of Forwards and Futures: Lyon, 1535

In the bustling market town of Lyon, France, in 1535, an extraordinary transformation was taking place. Amidst the cacophony of sellers peddling meats, fruits, and tools, one man stood out. He had no physical products, just a fountain pen, paper, and an idea that would revolutionize trading.

This man began selling the future by signing contracts to buy wheat at predefined prices and reselling them at higher rates. This practice, known as Forwards or Futures, was a radical departure from traditional commerce. It attracted attention, skepticism, and eventually imitation. Other traders started following suit, and soon, Lyon’s market was flooded with these future contracts.

The city’s reputation grew, attracting merchants and financiers from far and wide. Banks and financial institutions took notice, and Lyon became a hub for innovation in finance. By the end of the century, it was not only France’s financial center but also the heart of Europe’s burgeoning capital market. The man with the fountain pen had set in motion a wave that would eventually shape modern stock markets.

Eric’s Adventure: Risk, Profit, and the Birth of Joint-Stock Companies

Across the sea, in the small but ambitious trading nation of the Netherlands, a Dutch trader named Eric had a vision. He dreamed of building ships to explore and trade with distant lands, but his ambition was larger than his credit. Banks and friends lent what they could, but it was never enough.

Undeterred, Eric took a novel approach. Instead of seeking credit, he asked investors to buy a share of his profits. This was a new concept, and it attracted adventurous and like-minded individuals who saw the potential in overseas trade.

Eric’s first voyage was a resounding success, bringing in profits that exceeded all expectations. Word spread quickly, and more people clamored to invest in Eric’s next expedition. His fleet grew, and so did his reputation.

However, managing the growing number of investors became a Herculean task. There were disputes over ownership, profits, and investment terms. The government, too, struggled to track who owned what and how much tax was due.

The¬†solution was as¬†innovative as¬†Eric‚Äôs trading model‚ÄĒa¬†centralized exchange where shares in¬†his ventures, and¬†those of¬†his competitors, could be bought and¬†sold. This was the¬†genesis of¬†the¬†joint-stock company and¬†the¬†stock market, concepts that would define global commerce for¬†centuries to¬†come.

With each new voyage, risks and rewards were shared among an ever-growing pool of investors. The idea spread across Europe, laying the groundwork for modern corporations and investment structures. Eric’s vision had not only opened new trade routes but also charted a course for the future of business and finance.

John Law: The Gambler Who Shaped France’s Monetary System

John Law, a charismatic Scotsman, was a gambler, banker, and economist whose ideas would leave an indelible mark on France’s monetary system. Sentenced to death in Britain for killing a man in a duel, Law escaped to Europe, where his financial acumen caught the attention of France’s regent.

France was in financial ruin after years of war, and Law proposed a radical solution: replace gold and silver with paper money backed by land. He believed this would stimulate the economy and reduce the national debt.

In 1716, Law founded the Banque Générale, issuing paper money that could be exchanged for coins. His ideas were initially successful, and Law’s influence grew. He took over the Mississippi Company, controlling French trade with the Americas, and his paper money fueled a speculative bubble.

However, Law’s success was short-lived. Doubts about the real value of the paper money led to a loss of confidence, and the bubble burst. Law was forced to flee France in disgrace, his innovations leading to financial chaos.

Yet, despite the catastrophic end, Law’s ideas were ahead of their time. He foresaw the potential of a centralized banking system, fiat currency, and the complex interplay of economics and psychology. His story is a cautionary tale about innovation, ambition, and the fragile nature of economic systems, but also a testament to the power of ideas to shape history.

These expanded sections provide a more comprehensive view of the unique currency system of Yap Island and the complex story of John Law. By delving into the details, the narrative paints a vivid picture of these historical phenomena, offering readers a deeper understanding of the diverse and often surprising world of money.

Beyond the Stories: A Rich Exploration of Money

‚ÄúMoney: An¬†Unofficial Biography of¬†Money‚ÄĚ by¬†Felix Martin goes beyond the¬†fascinating stories of¬†Yap Island, Lyon, Eric‚Äôs adventures, and¬†John Law. It dives into the¬†complexities of¬†debt, the¬†art of¬†printing money (seigniorage), the¬†ideologies of¬†capitalism and¬†communism, the¬†evolution from the¬†gold standard to¬†fiat currency, and¬†the¬†prominence of¬†the¬†U.S. dollar.

This article was edited by¬†ChatGPT, which assisted me in¬†crafting the¬†storytelling, paraphrasing sentences, and¬†verifying facts from both the¬†book ‚ÄúMoney: An¬†Unofficial Biography of¬†Money‚ÄĚ by¬†Felix Martin and¬†my own memory. The¬†collaboration has helped shape a¬†more engaging and¬†accurate representation of¬†the¬†book‚Äôs rich exploration of¬†money and¬†its multifaceted history.

Speech into text in Telegram

I have developed a @speech_into_text application that transcribes audio into text. In addition to simple transcription, the app offers text editing features to correct mistakes, and it can format the text to suit various purposes such as academic writing, emails, business plans, or glossaries.

I frequently utilize this app to compose articles, create prompts for chatGPT, send messages to friends, and articulate ideas and debates.

You can try this bot for just $1 per hour of audio. This fee covers the costs of server maintenance and API usage. If you find the app beneficial, please share your experiences and recommend it to friends or colleagues. If you have any knowledge or ideas to enhance the application, your feedback would be greatly appreciated.

2023   AI   Telegram

Financial Glossary

Join the Financial Terms Telegram channel and get a new financial term every day.

I created an app for this project that generates the terms and definitions in my style, turns them into an image, and posts it in Telegram at the specified time.

2023   AI   Finance   IT   Telegram

Financial Data Dashboard in Telegram

I created the financial dashboard @financial_data_bot. It is free to use.

Get stock data

/info msft y ma30

/info is a command that activates the get stock data function.

msft is a ticker name of Microsoft. insert any other ticker. Note, for now, not every ticker is available.

y is a date range. You may use w, m, 3m, 6m, y, 3y, or 5y. Also, you may enter specific dates, for example, 01-2021 03-2023 prints the data from January 2021 to March 2023.

ma30 is a monthly average line. it takes the average price of a stock in the last 30 days. You take any days you want: ma40 ma60 ma120 etc.

You can also print more than one line, add one more ma30 /info msft y ma60 ma80 ma240.

Get market data

/market USA 1y

Get the market data with the most popular metrics, such as federal debt, inflation, trade balance, unemployment etc.

Ask about financial stuff

/ask provides a definition of a financial instrument, information about a company, and events.
/ask what is CDS
/ask how to count the option price

price alerts

/alert msft 9:00 12:00 18:00

alert prints the price in the defined time every day, except of weekends

/alert currency 9:00¬†‚Äď prints the¬†eurrub and¬†usdrub prices
/alert brent 9:00¬†‚Äď prints brent oil price

/stop msft¬†‚Äď to¬†stop the¬†alert of¬†a¬†specific stock.

Get summary news (Soon)

to¬†get the¬†summary, I use Openai chatgpt. It is not¬†free¬†‚Äď each stock news analysis costs 50 cents daily. This is because the¬†bot analyses a¬†million words in¬†hundreds of¬†newspapers. If you need this function, get the¬†Openai API and¬†enter it in¬†/openai Your_API_Key, like /openai sk-jfkafjkwhlfhph82p9hi3u2hrbguashg98g3.

The bot will store the summary in the database, and share the news analysis with others

/summary msft d

d is a day. It can also be a week (w) or month (m). You may also choose a specific date, e. g., /summary msft 01-02-2023 03-02-2023.

/summary USA d gets the overall USA market news summary.

Additional data with APIs (Soon)

I want the bot to be free to use, but I have limits on the usage of specific data. For example, only 2 requests about the USD and EUR prices can be done in one minute. the stock news analysis is also very expensive.

Get these 3 APIs to use the bot for free:

  • Alpha vantage¬†‚Äď give the¬†currency prices, free to¬†use for¬†less than 5 queries per¬†minute
  • Fred¬†‚Äď American database of¬†the¬†market data. Requires a¬†2-minute registration
  • Openai¬†‚Äď Ghatgpt machine. Register and¬†get 5$ for¬†free. Get API.

More Features

I want to get real-time stock, bond, and forex prices. They cost a bit. for example, to buy the info on the Moscow Stock Exchange, I have to pay 100$ monthly for each type of security. I can’t afford it, but if there will be enough users, I will create the 1$ subscription, which will give all the features.

Also, I pay 4$ for the server, which is enough to handle 15 requests in one minute. I have to pay 2$ for every 20 additional requests per minute monthly. The database costs 5$ for 15GB. If you like the bot, you may help me to finance it via Koveh Pay

Some bugs with the graphs will be edited soon.

have any ideas, found a bug, wanna promote the project, or join a team, write a comment below or to

2023   AI
2023   AI   Programming

DIY: Summarise Your Speech

I like discussing ideas out loud. I record my thoughts using Voice Memos on my iPhone. I talk for hours but never listen to my records, because it gets boring.

We will make an app, that transforms audio into text. And then, we will make a structured summary. And even a Business Model Canvas of an idea.


  1. Install Openai Library
  2. Audio to Text
  3. Audio to Summary
  4. Audio to Business Model Canvas
  5. Draw a Business Model Canvas Automatically


You need Python and Openai account.

if you don’t have Python or don’t know how to work with libraries on Python, take a look at my previous tutorial about object detection in images, where we installed it on Mac and Windows.

If you have some errors or don’t understand something, ask chatGPT.

Install OpenAI Library

Open ‚ÄúTerminal‚ÄĚ on¬†Mac or¬†in¬†Visual Studio Code. type the¬†following:

pip install openai

The library is used for 3 purposes: ask ChatGPT, translate speech to text, and generate an image. We use ChatGPT and Speech-to-Text functions.

Get the API key

Openai is not free to use. We should pay 0.006$ per each minute of audio. Therefore, we need to register on the Openai website and get the API key, that we insert later in the code. Openai also gives 5$ to the new users.

get the API key after registration.

Record your voice

Use the¬†‚ÄúVoice Memos‚ÄĚ app on¬†Mac or¬†iPhone or¬†any other recording app. Most of¬†the¬†audio file formats are supported by¬†Whisper AI from OpenAI.

Speak for up to 10 minutes in English. Next, move the recording file to the same directory where you will store the Python file. You may also copy the path to the audio file instead of moving it.

Audio to Text

import openai

# insert your API key
openai.api_key = "sk-tIBD5UTbJ9RdqrwjP2dNT3BlfkFJzex4WGqvNsi3uAdMdC1V"

# Define the path to the file. if the file is in the same folder as the Python code, just write the name of a file
path = "audio.m4a"
audio_file= open(path, "rb")

# Select the AI to use (whisper-1 is the newest in April 2023)
transcript = openai.Audio.transcribe("whisper-1", audio_file)


We have translated our speech into text.

Audio to Summary

We use ChatGPT-3 to make a summary. You may use gpt-3.5 that costs 10 times less or gpt-4 for a more precise summary of more than 4000 words.

The¬†maximum amount of¬†words is 4096. The¬†token is a¬†way how the¬†AI stores the¬†data. for¬†English, each token represents nearly one word (700 words are nearly 1000 tokens). For¬†Russian, German, and¬†French¬†‚Äď each token is nearly one letter (700 words are nearly 5000 tokens). Translate the¬†text to¬†English and¬†only then use chatGPT. You will save 5x dollars.

In Prompt we add the translated text and the task that we want an AI to do.

# save the translated text as text
text = transcript.text

prompt = (
        f"Make the summary of the text: \n\n{text}\n\n"

response = openai.Completion.create(
    max_tokens=3900 # 4096 minus the constant prompt message


combine this code with the previous one and run the script. That’s it.

Audio to Business Model Canvas

If I talk about founding a business, why not combine the text in a business model canvas.

prompt = (
    f"Create a business model canvas based on the idea explained below:\n\n{text}\n\n"
    "Use the following structure of 9 blocks:\n"
    "Customer Segments:\n"
    "Value Propositions:\n"
    "Customer Relationships:\n"
    "Revenue Streams:\n"
    "Key Resources:\n"
    "Key Activities:\n"
    "Key Partners:\n"
    "Cost Structure:"

You get the structured answer, and you can insert the data in your business model canvas template.

Draw business model canvas

Let’s make a real business model canvas out of the idea described in the audio. We need to do a simple website with CSS, HTML, JSON, and Python. I prepared it for you:
Download the folder

Install Flask library

pip install flask

Run,, or (if you don’t have openai account) within the given folder. Don’t forget to write Openai API key and the audio or text:

Open in the browser: Or any other page that is written in your terminal

you may also run or, where the full code is written, just edit the API key and that’s it.

That’s it. You don’t need any longer to think about how to structure the business model canvas.


Create the House of Quality model, that tells the difference between you and competitors.

Create the¬†business plan template, which is created automatically based on¬†your recording¬†‚Äď add the¬†SWOT, PESTEL analysis, intro about the¬†product, executive summary, team structure, and¬†maybe competitor and¬†market analysis, using the¬†knowledge from my ‚ÄúDIY‚ÄĚ articles.

Share the¬†tutorial with your friends and¬†colleagues. If you want to¬†see how to¬†record one hour of¬†a¬†speech or¬†team meeting, and¬†how to¬†record in¬†any language and¬†cheaply, then leave a¬†comment. Meanwhile, read other DIY articles, you can find them, by¬†simply typing ‚ÄúDIY‚ÄĚ in¬†the¬†search on¬†the¬†top right. Also, check out the¬†articles about finances and¬†urban planning.

If you want to use a similar app casually, subscribe to my Telegram bot. @ @speech_into_text_bot can listen for up to 10 minutes of speech and transform it into text and summarise everything you said. It costs just 20 cents per 10 minutes of speech. The money is spent on Openai libraries and server maintenance.

2023   AI   DIY   Programming

DIY: Image Recognition

You have a farm, and you need to count the number of cows in the field to make sure none are missing. A machine can do this for you:

A program has found 77 cows in one second.

Real-life examples: count customers who entered a store today, sort fruits, or count cars in parking.

We will create a simple image analyzer to count the number of cows in a field and automatically highlight them in a photo. No programming experience required. If you don’t understand something, just ask ChatGPT.

The code is universal, so you can detect people or other objects just by editing a single line of code. We’ll use YOLOv8, a cutting-edge, highly accurate image recognition library that’s free to use.


  • Computer (Mac or¬†Windows or¬†Linux)
  • Internet
  • Python (we will install it)
  • AI model YOLOv8 (we will download it)
  • Image of¬†cows (we will download it)

Setup the Workspace

The¬†coding process involves two parts: writing code and¬†executing it. Typically, we write code in¬†a¬†text editor, and¬†we execute it using a¬†tool like ‚ÄúTerminal‚ÄĚ on¬†a¬†Mac. However, there are applications specifically designed for¬†coding, like Visual Studio Code, which lets you write and¬†execute code on¬†the¬†same page.

Visual Studio will also help you install Python. You can also install extensions that make coding faster (such as Python autocomplete and Copilot) or an extension that stores your code online (like a Git repository).

Install Python

Python is a¬†programming language. We‚Äôll use it to¬†write code and¬†install the¬†AI models. To¬†run the¬†Python code, we‚Äôll use Visual Studio Code. To¬†install Python, simply open ‚ÄúExtensions‚ÄĚ on¬†the¬†left bar, type ‚ÄúPython‚ÄĚ in¬†the¬†search field, and¬†install the¬†Python extension.

Install Python on Mac

During installation, you may encounter errors. Ask ChatGPT for help resolving them (e. g., copy and paste the error message).

To¬†install Python on¬†a¬†Mac, you need to¬†download Homebrew, which is like an¬†app store for¬†programmers. Open the¬†‚ÄúTerminal‚ÄĚ application (press Command+Space and¬†type ‚ÄúTerminal‚ÄĚ), then copy and¬†paste the¬†code below into the¬†‚ÄúTerminal‚ÄĚ:

/bin/bash -c "$(curl -fsSL"

Press enter and do everything that the program says.

After installing Brew, it’s recommended to run

brew update

This ensures you have the latest version of Homebrew and that you’ll download the most recent version of Python.

Next, download Python by typing

brew install python

Follow the instructions and ask ChatGPT if you encounter any errors.

Install on Windows

Visit the o official Python installation page and select the latest Python installer for Windows.

Installation is straightforward, but you might encounter some problems. If you do, explain the errors to ChatGPT (e. g., copy and paste the error message).

Write Code

Create a¬†new document by¬†pressing Command+N. Next, save the¬†file using Command+S as¬†‚Äú‚ÄĚ, where ‚Äúanalyze_image‚ÄĚ is the¬†name, and¬†‚Äú.py‚ÄĚ indicates a¬†Python file.

Now, write a¬†simple code that prints ‚ÄúHi!‚ÄĚ:


Save a code by pressing command+s

Run Code

To¬†run the¬†code, click the¬†play button. You will see the¬†output in¬†the¬†‚ÄúTerminal‚ÄĚ window at¬†the¬†bottom.
(Note: your terminal may look different from mine, but it doesn’t make any difference)

you will get an answer here

You may also press the F5 button. The first time you use it, you will be asked which debugger (same as running the code) to use. Select the first one.

Alternatively, you can run a¬†program using the¬†terminal in¬†Visual Studio Code or¬†the¬†‚ÄúTerminal‚ÄĚ on¬†Mac. This is slightly more challenging. First, you should open the¬†folder where the¬†Python file is located. This is displayed at¬†the¬†top of¬†Visual Studio Code.

To¬†open it, type the¬†path and¬†the¬†‚Äúcd‚ÄĚ function (change directory) in¬†the¬†command line. Don‚Äôt include the¬†name of¬†the¬†file; stop at¬†the¬†last folder.

cd /Users/daniil_kovekh/Desktop/bot/Image/find_cow

Now, you can list all the files in the folder.


To¬†run the¬†Python code, type ‚Äúpython‚ÄĚ and¬†the¬†name of¬†the¬†file with ‚Äú.py‚ÄĚ at¬†the¬†end.


That’s it. If you encounter an error, ask ChatGPT for help. There may be a mistake in the code.

Download Libraries


A library is code written by other developers. Here’s how to use one:

For¬†example, if you want to¬†create a¬†graph showing the¬†number of¬†customers who visited your store last week, you could use an¬†existing graphing function like ‚ÄúMatplotlib.‚ÄĚ This library can draw any graph you need.

First, download the¬†library from the¬†internet. Use the¬†command line in¬†Visual Studio Code for¬†this (you can also use the¬†‚ÄúTerminal‚ÄĚ application on¬†Mac). We‚Äôll use the¬†Python installer, called ‚Äúpip.‚ÄĚ

pip install matplotlib

Press ‚ÄúEnter‚ÄĚ on¬†your keyboard.

After it’s downloaded, simply write this code.

the¬†‚Äú#‚ÄĚ symbol is used for¬†comments. The¬†text after it on¬†the¬†line will be
visible to you, but not to the machine.

# Import the library and the module pyplot. The module contains functions.
import matplotlib.pyplot

# Define the data. We store data as lists - the elements that have the same format.
# days is a list that contains the text - it is a "string" format
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

# customers is a list containing numbers - "integer" format
customers = [120, 140, 110, 100, 170, 200, 210]

# Add the data to the graph (X, Y).
# We use a bar graph - the function is also named "bar"., customers)

# Draw and display the graph.

Save the code with the Command+S button, and click the play button to run the code (or use other methods that we learned previously). The graph appears in another window, or maybe on another desktop.

Free or Paid

Most libraries are free, but some require payment. For example, Yahoo Finance offers free access to stock price data. However, OpenAI, the provider of ChatGPT, charges $0.06 for every 1000 words printed using ChatGPT-4.

The¬†YOLOv8¬†‚Äď the¬†image analyzer is free to¬†use.

Internet Access

Some libraries require internet access. Yahoo Finance and OpenAI both work with the internet. Yahoo Finance uses the internet to access its database of stock prices. OpenAI uses the internet for another reason: it receives the text you wrote, runs the AI program on its server, and sends you the answer.

YOLOv8 works both online and offline. YOLOv8 needs the internet to access the database with trained models that identify what’s in a picture. If you train the model yourself, by uploading hundreds of photos and naming what is drawn on it, then it will work offline.

Import YOLOv8 Library

The YOLO library is developed by Ultralytics. To install it, simply run the following command:

pip install ultralytics

You might encounter errors regarding the need to download other libraries or outdated packages. Just copy-paste those error messages here in ChatGPT.

Get Images of Cows

We’ll find cows in meadows by using these images:
Download these images and¬†place them in¬†the¬†folder containing the¬†‚Äú‚ÄĚ script.

Write Cow Detection Code.

Let‚Äôs write the¬†basic code to¬†detect cows in¬†the¬†images. First, set the¬†path to¬†the¬†source image. Then, define the¬†model¬†‚Äď we‚Äôll use a¬†simple universal pre-trained model called ‚Äú‚ÄĚ. Next, determine whether to¬†save a¬†new image with highlighted cows. Also, set the¬†confidence threshold for¬†the¬†computer to¬†identify a¬†cow (0 to¬†1). The¬†lower the¬†confidence level, the¬†more chances are that the¬†cow will be found, but¬†the¬†risk that another object will be selected as¬†a¬†cow will also be higher (e.¬†g., a¬†car may become a¬†cow). Set the¬†confidence threshold to¬†0.4.

Additional settings can be found on the official webpage

Note: We don’t specifically define that we’re searching for cows; the code simply identifies the most obvious objects in the picture. We’ll fine-tune this later.

from ultralytics import YOLO

source = "/Users/daniil_kovekh/Desktop/bot/Image/find_cow/cows.jpg"

# Load a YOLOv8 model from a pre-trained weights file
model = YOLO('')

# Find the cow with confidence 0.4 and save the image
model.predict(source, save=True, conf=0.4)
The¬†results are saved in¬†the¬†runs/detect/predict folder. If the¬†‚Äúpredict‚ÄĚ folder already exists, it will be named ‚Äúpredict1‚ÄĚ, etc.

Here are the results of an image with 2 cows on it:

Results with a large number of cows:

As we can see, when the cows are smaller and overlap each other, the code makes mistakes due to uncertainty.

Improve Results¬†‚Äď Decrease Confidence.

To¬†improve the¬†results, we can adjust the¬†confidence threshold and¬†Intersection Over Union (IOU)¬†‚Äď which accounts for¬†overlapping cows, such as¬†when a¬†cow hides behind a¬†tree. Change these settings within a¬†range of¬†0 to¬†1.

from ultralytics import YOLO

source = "/Users/daniil_kovekh/Desktop/bot/Image/find_cow/cows.jpg"

# Load a YOLOv8 model from a pre-trained weights file
model = YOLO('')

# Find the cow with confidence 0.4 and save the image
model.predict(source, save=True, conf=0.4, iou= 0.4)

The results may still not be ideal, because when the confidence threshold is low, the AI might misidentify other objects, like cars, as cows.

Improve Results¬†‚Äď Increase Image Size

By default, the image width is 640 pixels. The size of cows3.jpg is approximately 5000x3000 pixels, while cows.jpg has a different size. Determine the appropriate image size for each picture using the Python Imaging Library (PIL).

Install PIL using the terminal and pip command:

pip install Pillow

Now, write Python code in Visual Studio:

from PIL import Image

source = "/Users/daniil_kovekh/Desktop/bot/Image/find_cow/cows3.jpg"

# Load the image and get its width
image =
width, _ = image.size # _ means the height, that we dont need. 

# Load a YOLOv8 model from a pre-trained weights file
model = YOLO('')

# use imgsz equal to width
model.predict(source, imgsz=width, save=True, conf=0.4, iou= 0.4)

This approach yields better results, but feel free to experiment with the settings to further improve them.

Count the Cows

To¬†count the¬†cows, we‚Äôll tally the¬†number of¬†‚Äúcow‚ÄĚ indices. Cow indices are stored in¬†a¬†dictionary, similar to¬†the¬†example below:

animals = {"cow": 0.8, "cow":0.5, "dog":0.4, "cow":0.66, "duck":0.4}

This dictionary records each detected object in the image and its confidence as the specified animal. We’ll count all the cows present in the dictionary. In the example above, there are three cows.

from ultralytics import YOLO
from PIL import Image

source = "/Users/daniil_kovekh/Desktop/bot/Image/find_cow/cows3.jpg"

# Load the image and get its width
image =
width, _ = image.size

# Load a YOLOv8 model from a pre-trained weights file
model = YOLO('')

# Define the cow class index
cow_class_index = None # default, if no cows are on the picture

for key, value in model.names.items():
    if value == "cow":
        cow_class_index = key # key is the first element in a Python dictionary

# Run prediction on the first image
results = model.predict(source, save=True, imgsz=width, conf=0.3, iou = 0.5)
boxes = results[0].boxes
cow_detections = [box for box in boxes if int(box.cls) == cow_class_index]

print(f"Number of cows in {source}: {len(cow_detections)}")
The computer counts 77 cows. (out of 78)

Analyze Anything Else

You‚Äôre now equipped to¬†write object detection code for¬†various purposes. If you want to¬†detect people, simply change the¬†value from ‚Äúcow‚ÄĚ to¬†‚Äúperson‚ÄĚ or¬†‚Äúcup‚ÄĚ.

4 people
18 people
3 cups of coffee

Explore the¬†documentation and¬†analyze whatever you‚Äôd like¬†‚Äď you can analyze videos or¬†live streams, detect a¬†person‚Äôs movements, or¬†even draw an¬†individual‚Äôs skeleton to¬†assess the¬†movements of¬†athletes.

If you require an AI application, website, bot for Telegram, Google extension, automation tool, or web scraper, feel free to contact me at My team and I are eager to assist you.

2023   AI   Programming
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