teleren/devto huggingface dataset:
Introduction to ChatGPT: A Powerful Chatbot Framework
Published: 2023-01-01 15:45:13

---
title: Introduction to ChatGPT: A Powerful Chatbot Framework
published: true
date: 2022-12-31 02:54:17 UTC
tags: 
canonical_url: https://adithyana.hashnode.dev/introduction-to-chatgpt-a-powerful-chatbot-framework
---

Are you looking to build a chatbot that can engage in natural conversation with your users? ChatGPT is an open-source chatbot framework that uses the power of GPT-3, the largest and most advanced language generation model in the world, to enable your chatbot to understand and respond to user input in a natural and engaging way.

In this blog post, we will learn how to use ChatGPT to build a chatbot that can have conversations with your users.

## Prerequisites

To use ChatGPT, you will need to have the following:

- A machine with Python 3.6 or above installed
- An API key for GPT-3 from OpenAI
- The `openai` Python library, which you can install using `pip install openai`

## Setting Up ChatGPT

To get started with ChatGPT, you will need to install the `chatgpt` Python library using the following command:

```bash
pip install chatgpt

```

Next, you will need to create a `ChatGPT` object and pass it your API key and the model ID of the GPT-3 model that you want to use:

```python
import openai
import chatgpt

# Set the API key
openai.api_key = YOUR_API_KEY

# Set the model ID of the GPT-3 model you want to use
model_id = 'text-davinci-002'

# Create a ChatGPT object
chatbot = chatgpt.ChatGPT(model_id)

```

## Using ChatGPT

Now that you have set up ChatGPT, you can use it to have conversations with your users. Here is an example of how you can use ChatGPT to build a simple chatbot that responds to user input:

```python
# Start the chatbot
chatbot.start_conversation()

# Get user input
input_text = input('You: ')

# Get the chatbot's response
response = chatbot.get_response(input_text)
print(f'Chatbot: {response}')

# Continue the conversation
while True:
    # Get user input
    input_text = input('You: ')

    # Check if the user wants to end the conversation
    if input_text.lower() == 'exit':
        break

    # Get the chatbot's response
    response = chatbot.get_response(input_text)
    print(f'Chatbot: {response}')

```

That's it! You now have a chatbot that can have natural conversations with your users using the power of GPT-3.

## Conclusion

In this blog post, we learned how to use ChatGPT to build a chatbot that can engage in natural conversation with your users. ChatGPT makes it easy to leverage the power of GPT-3 to build chatbots that can understand and respond to user input in a natural and engaging way.

I hope you found this blog post useful. If you have any questions or comments, please feel free to leave them in the comments section below.
O que é e para o que serve Kubernetes?
Published: 2022-12-31 02:28:18

Kubernets é uma plataforma fortemente ligada a área de DevOps.
Kubernets também conhecido como K8S é um sistema open source de orquestração de containers para a automatização de deploys e escalonamento e gerenciamento de aplicações.

Diferente do que muitos acreditam, Kubernets não é concorrente ao Docker, os dois podem trabalhar juntos.

Mesmo o próprio Docker tendo seu próprio orquestrador de containers, o Docker Swarm, Kubernets tem se mostrado uma opção muito superior.

Kubernetes foi pensado para escalabilidade, performance e otimização de recursos utilizados para rodar essas aplicações.

O Docker mudou completamente a forma como distribuir e rodar a aplicação. Porém, uma vez tendo um container rodando, há ainda outras questões a resolver. Por Exemplo:

- Se um contêiner falha em execução, como garantir que ele vá subir novamente de maneira automática?

- Se precisarmos de múltiplos contêineres, como automatizar e garantir que eles se mantenham em execução?

- Se quisermos algo elástico, como, por exemplo, ter no mínimo 1 contêiner, mas podendo crescer até 5 em execução, como fazer?

Essa necessidade de ter um "gerenciador de container", alguém que "fica de olho" nos containers em execução, garantindo que o sistema na totalidade continue funcionando são questões facilmente resolvidas com o uso de Kubernetes.

Um outro diferencial dele é que ele pode ser programado para agendamento de tarefas. Exemplo: 

Um concesso sobre atualizar aplicações em horários de menos uso pode ser facilmente executado, pois é possível agendar tais atualizações utilizando Kubernetes, em caso de erro a própria aplicação se encarrega de desfazer a atualização.

Esses são alguns exemplos de todo o poder dos Kubernetes.
pycharm django cannot import module views.py
Published: 2022-12-31 02:24:51

I'm having a weird problem with import views in Django project. I'm not sure whether it is a problem caused by PyCharm of Django. So PyCharm says that it can't import views.py file, but it works when I run the server.

Answer:


PyCharm doesn't know where your source files are. Try this:

Right click on folder vlado_web (the folder that contains manage.py) within PyCharm. Go to Mark Directory As and choose Sources Root.

This will let PyCharm know that the vlado_web directory is the root folder for your source code, and will allow you to perform absolute imports from there, e.g.

from vlado_web.translations import views
You have to check out Github Copilot Labs
Tags: github, webdev, ai, tooling
Published: 2022-12-31 02:07:44

If you are familiar with the awesomeness that is Github Co-Pilot then you need to check out the Github copilot labs visual studio code extensions. This extension comes packed with loads of cool AI features to help you understand, document, improve, and make your code more robust. Here is how you can install and use it. 

### Installing Github Co-Pilot Labs Visual Studio Code Extension

1. Open Visual Studio Code.
2. Click the `Extensions` icon in the left sidebar.
3. Search for `Github Co-Pilot Labs` in the search bar.
4. Select the extension and click the `Install` button.
5. Once the installation is complete, click the `Reload` button.
6. You should now see the Github Co-Pilot Labs extension in the left sidebar.
7. Click on it and highlight any piece of your code and see what it can do
End the year on high note earned a badge
Tags: badges, community
Published: 2022-12-31 01:50:48

I'm ending the year on a high note. I hit 32 weeks of constructive comments today. I love that DEV has the badges to encourage community.



![32 weeks of constructive comments badge](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9zfx9ptu0blt9fabuu7y.jpg)
리눅스를 떠나보내면서
Tags: korean
Published: 2022-12-31 01:47:48

이번에 서피스 프로 8을 사게 되면서 집에서 쓰는 컴퓨터의 OS를 다시 윈도우11으로 바꾸기로 했다. 한 3~4년쯤 전부터 리눅스를 접하고 나서부터 별 문제없이 사용해오기는 했지만, 아무래도 작업 환경을 통일할 필요도 있고 무엇보다 호환성 측면에서 어쩔 수 없다고 느낀 것이 제일 큰 것 같다.

물론 윈도우 11으로 넘어가더라도 최대한 지금 사용하는 환경과 비슷하게 만들어볼 생각이다. Scoop 패키지 매니저, neovim, 타일링 WM등 나와 비슷한 생각을 한 사람이 여럿 있었는지 그래도 여러가지 도구가 준비되어 있던 것을 볼 수 있었다.

그래도 한가지 다행인 것은, WSL이 있다는 것이라고 할 수 있겠다. WSL을 사용한다면 기존에 리눅스에서 하던 작업이나 컴파일러 등을 사용할 수 있을 테니 그 점은 다행이다.

아마 앞으로 다시금 적응하려면 시간이 걸리지 않을까 예상하고 있다. 운영체제의 자유로움과 간결함, 이 두가지를 떠나보내는것이 안타깝기는 하지만 언젠가 리눅스 환경으로 돌아올 때가 있지 않을까 싶다.

그리고 아치의 Live boot를 사용할 수 없으니 앞으로 백업은 잊지 말고 해둬야겠다...
Understanding body-parser, cookie-parser, morgan, nodemon, pm2, serve-favicon, cors, dotenv, fs-extra, moment in Express.js
Tags: middleware, express, node, programming
Published: 2022-12-31 01:20:37

**Express.js is a web application framework for Node.js that makes it easy to build web applications and APIs. There are a number of middleware modules that are commonly used with Express.js to add additional functionality. Here's a brief description of each:**

1. body-parser: This middleware is used to parse the body of an HTTP request, which can contain JSON data or other types of data. It allows you to access this data in your route handlers and use it to create or modify resources.

2. cookie-parser: This middleware is used to parse cookies that are sent with HTTP requests. It allows you to read and write cookies in your route handlers, which can be used for things like session management.

3. morgan: This middleware is used for logging HTTP requests. It can log request details like the method, path, and response status code to a file or the console.

4. nodemon: This is a utility that automatically restarts your Node.js server when it detects changes in your code. It's often used during development to make it easier to work on your server without having to manually restart it every time you make a change.

5. pm2: This is a process manager for Node.js applications. It can be used to run your server in the background and monitor it for crashes or other issues. It can also be used to restart your server if it crashes or if you make code changes.

6. serve-favicon: This middleware serves a favicon (a small icon that appears in the browser's address bar) to the client. It can be used to set a custom favicon for your site or API.

7. cors: This middleware is used to enable CORS (Cross-Origin Resource Sharing) on your server. CORS is a security feature that allows a server to specify which origins are allowed to access its resources.

8. dotenv: This module is used to load environment variables from a .env file into the process.env object in Node.js. This can be useful for storing sensitive information like passwords or API keys that you don't want to hardcode into your application.

9. fs-extra: This module is a thin wrapper around the built-in fs module in Node.js, with additional methods for tasks like copying files and creating directories. It can be used to perform file system operations in your route handlers or other parts of your application.

10. moment: This is a popular library for working with dates and times in JavaScript. It can be used to parse, validate, manipulate, and format dates in your route handlers or other parts of your application.

These are just a few examples of the middleware and utilities that are commonly used with Express.js. There are many other options available, and you can choose the ones that best fit your needs.
sending a file with curl POST needs a magic at sign
Tags: dailystruggle, curl, cli, http
Published: 2022-12-31 00:37:58

---
title: sending a file with curl POST needs a magic at sign
published: true
description: 
tags: dailystruggle, curl, cli, http
# cover_image: https://direct_url_to_image.jpg
# Use a ratio of 100:42 for best results.
# published_at: 2022-12-27 11:46 +0000
---

```
curl -X POST -H "Content-Type: multipart/form-data" -F "file=myAwesomeFile.pdf"
```

does not work. The magic part os prepending at sign in the beginning of file name.

```
curl -X POST -H "Content-Type: multipart/form-data" -F "file=@myAwesomeFile.pdf"
```

Just in case someone struggles with this same as me.
System design. Monitoring and Alerting System.
Tags: systemdesign, monitoring, alerting, interview
Published: 2022-12-31 00:29:53

This article is more like tips to that question rather than system design itself. If you are looking for the system design try [this](https://a.co/d/5oadFci) amazing book.

## Clarifying questions to establish the scope:

What metrics do we want to collect?

What are the supported alert channels?

How long should we keep the data?

Can we reduce the resolution of the metrics data for long-term storage?

Do we need to collect logs?

What is the scale of infrastructure we are monitoring with this system?

Is it a SaaS or just for internal purposes?

## Fundamentals:

A metrics monitoring and alerting system contains 5 features:

1) Collect data from sources

2) Transfer data(what is different between 1 and 2)?

3) Store data

4) Alerting

5) Visualization

## Data access pattern

We will persistently collect data and write it to the database. **Heavy write load**.

Visualization and alerting services would send queries to the databases, an access pattern will vary depending on graphs and alerts. So the **read load will be spiky**.

## Data storage system

SQL - too complicated queries, bad performance under heavy writes

NoSQL - Cassandra and Bigtable can be used, they are optimized for heavy writes. But we can find a better solution

Time-Series databases - InfluxDB and Prometheus are the two most popular databases. They are designed to store large volumes of time series data and quickly perform real-time analysis on that data. Another feature is the efficient aggregation and analysis of a large amount of time-series data by labels. InfluxDB builds indexes on labels to facilitate the fast lookup of time-series by labels.

## Pull or push model to collect metrics

### Pull model

With such an approach we need to know the list of all services that we want to monitor. It is hard to maintain especially at a large scale when we need to add/remove services frequently. Of course, we can use some Service Discovery services like ZooKeeper where services will register their availability and our collector can be notified about any changes.

### Push model

With that model, we need to install an agent on every monitored service. An agent is a long-running software that will collect metrics and send them to our collector.

### Push or pull

Depends totally on the details of the system that we want to support. Large scale system probably needs to support both approaches cause it might uses serverless components and you will not be able to use an agent.

## High level design with Kafka

![High level design](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/duwydzsa9iu824aqy303.png)

Why Kafka needed? 
- avoid data loss in case if DB is unavailable
- decoupling data collectors and data processors
OpenPilot - Poor man's Github Copilot - Part 2
Tags: webdev, vscode, programming, opensource
Published: 2022-12-30 23:44:54

## Recap

So earlier this week I posted about the idea of OpenPilot with a little teaser and promise to release the extension later this week. So what is OpenPilot? Well in simple words it's Github Copilot powered by Open AI. My goal is to create something that every developer can use in a meaningful way.

If you're interest checkout the the [part one](https://dev.to/shakeebaftab/poor-mans-github-copilot-part-1-21ii)

## Progress Report

I showed generating comments from code in my last update and now we are able to generate code from comments. At the moment, how it works is that we select the comment and run the command to generate code. Check it out [here](https://youtu.be/xJ6Sid0-TQ8).

## Future Plans

Well we all know, the best feature about Github Copilot is the real-time recommendations. Although I sometimes find them annoying, it's hard to argue against them so my main goal is get the real-time predictions working before the official launch.

## Collaborations

As mentioned in my last post as well, this is my first VS Code extension so I'm learning as I go but if someone's interested in collaborating. Feel free to [hit me up](https://www.linkedin.com/in/shakeeb-aftab/)

Check out the [Repo](https://github.com/ShakeebAftab/openpilot), I'll be pushing the updated code soon
Replacement for retired VS Code Jest codelens
Published: 2022-12-30 23:32:54

The Codelens provided by VS Code Jest extension on tests in the editor [has been retired](https://github.com/jest-community/vscode-jest/pull/950) (hence [this post on Jest CodeLens](https://dev.to/nausaf/vs-code-jest-extension-tackling-debug-codelens-disappearing-act-1l98) I wrote a while back is also outdated).

The fix is really simple:

In your User Settings file (`setttings.json`), put this line in:

```
"testing.defaultGutterClickAction": "contextMenu"
```

You should now be able to access Debug test etc. from the Play button in the gutter:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/bqad3y247g2f1zw3nme1.png)
5 easy ways to reduce DOM manipulations
Tags: javascript, webdev, programming, tutorial
Published: 2022-12-30 21:46:24

DOM manipulation refers to the process of modifying the structure, content, or style of an HTML or XML document using JavaScript. While DOM manipulation is a powerful tool for building dynamic web applications, it can also be a resource-intensive operation that can negatively impact the performance of an application. In this article, we'll explore some strategies for minimizing the number of DOM manipulation operations in JavaScript.

1. **Use document fragments**: Document fragments are in-memory representations of a portion of a document. By using document fragments to perform multiple DOM manipulation operations at once, you can minimize the number of times the DOM is modified. To use document fragments, you can create a new fragment, append your DOM elements to the fragment, and then append the fragment to the DOM once all the elements have been added.

2. **Use CSS transitions and animations**: Instead of using JavaScript to animate elements on the page, consider using CSS transitions and animations. These techniques can be more efficient and performant than using JavaScript to animate elements.

3. **Avoid repeatedly modifying the same element**: If you need to modify the same element multiple times, consider using a single DOM manipulation operation instead of multiple operations. For example, if you need to change the text of an element multiple times, consider using the textContent property instead of the innerHTML property.

4. **Use event delegation**: Event delegation is a technique that allows you to attach a single event handler to a parent element, rather than attaching individual event handlers to each child element. This can be more efficient, especially in cases where the number of child elements is large or dynamic.

5. **Use requestAnimationFrame**: The requestAnimationFrame function allows you to schedule DOM manipulation operations to occur at the next repaint of the browser window. This can help reduce the number of times the DOM is modified, as the operations will be grouped together and performed at once.

## Conclusion

By following these strategies, you can minimize the number of DOM manipulation operations in your JavaScript code and improve the performance of your application. It's important to note that these techniques may not be applicable in all cases, and the best approach will depend on the specific needs of your application.

---
The goal is to optimize our JavaScript and greatly improve the performance of a web application.
Picturesocial - Cómo es la arquitectura de una aplicación
Published: 2022-12-30 21:39:23

Crear Picturesocial ha sido todo un viaje, cuando comenzamos me propuse crear una red social de cero, con el solo propósito de que aprendamos juntos el proceso de llevar una solución contenerizada a Amazon Web Services. 

Algo que tal vez no dejé claro es que yo aprendí de AWS creando esta serie, comencé hace poco más de 1 año y solo sabía mi nombre y mi edad. Pero hoy me siento orgulloso de todo lo que hemos logrado y aprendido juntos. 

{% embed https://www.youtube.com/watch?v=YEmFv_tjwyE %}

---

Acá les dejo los enlaces con todo el contenido de la primera temporada.

1.     [Cómo crear un container](https://bit.ly/3MrneKU?r=lp)
2.     [Qué es Kubernetes](https://go.aws/3wucSns?r=lp)
3.     [Cómo crear un clúster de Kubernetes](https://go.aws/3GwBnEY?r=lp)
4.     [Cómo desplegar aplicaciones a Kubernetes](https://go.aws/3zZCVpF?r=lp)
5.     [Cómo usar reconocimiento de imágenes](https://go.aws/3PxPyga?r=lp)
6.     [Cómo delegar accesos a servicios de AWS en Kubernetes](https://go.aws/3QDBIJV?r=lp)
7.     [Cómo usar una base de datos documental](https://go.aws/3ShIc1I?r=lp)
8.     [Cómo exponer las API's](https://go.aws/3tk1SI6?r=lp)
9.     [Cómo escalar el clúster de Kubernetes con cómputo Just-in-Time](https://bit.ly/3j4ox9m?r=lp)

---

Estos 10 episodios marcan el fin de nuestra primera temporada que se ha enfocado en el descubrimiento. La siguiente temporada estará pensada en la vida real, esa que no suele ser color de rosa, donde las cosas pueden fallar, podemos tener algunos problemas de disponibilidad, seguridad o gastar más de lo que creíamos, casi todo puede prevenirse y nos vamos a enfocar en crear una mejor experiencia para todos y todas. Por eso me pareció que debíamos cerrar la temporada pensando en arquitectura, a manera de resumen de todo lo que hemos hecho hasta ahora y de pensar en el futuro. He invitado a Gustavo Fandiño quien es arquitecto de soluciones en AWS para que nos guíe en entender cuáles son los retos y el futuro de Picturesocial para el siguiente año.

Lo primero que hicimos fue entender la arquitectura as-is y los servicios que hemos explorado en los diferentes episodios de la serie.

![Picturesocial - Arquitecture actual](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/wfrww69ziqsa016u2dt5.png)

Para luego revisar la primera fase de lo que será nuestra nueva arquitectura, donde iremos agregando Entrega e Integración continua así como el offloading de cómputo de nuestro clúster de Kubernetes hacia componentes Serverless.

![Picturesocial - Arquitectura to-be](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/km4ta7sfno8e4bikheqq.png)

Este post no tiene mucho detalle porque creo que lo más valioso es la conversación del video así que espero sus comentarios y sugerencias. Todo lo que me escriban será tomado en cuenta para la siguiente temporada. 

¿Nos leemos el próximo año?
Hanukkah of Data 2022 - Puzzle 6
Tags: hanukkahofdata, datasette, python, pandas
Published: 2022-12-30 21:07:43

---
title: Hanukkah of Data 2022 - Puzzle 6
published: true
description: Talk through (and spoil!) the sixth puzzle from Hanukkah of Data 2022.
tags: hanukkahofdata, datasette, python, pandas
series: Hanukkah of Data 2022
---
_[Hanukkah of Data](https://hanukkah.bluebird.sh/) is a series of data-themed puzzles, where you solve puzzles to move your way through a holiday-themed story using a fictional dataset. See the [introductory post](https://www.visidata.org/blog/2022/hanukkah-of-data/) for a bit more detail, but the pitch in my head is "[Advent of Code](https://adventofcode.com/) meets [SQL Murder Mystery](https://mystery.knightlab.com/)". This post walks through my approach to the sixth puzzle._

- [Thinking](#thinking)
- [Doing (Pandas)](#doing-pandas)
- [Doing (Datasette)](#doing-datasette)

**Warning:** This post unavoidably contains spoilers. If you'd rather do the puzzles on your own first, please close this browser tab and run away :).

## Thinking

Day 6 brings us a cheapskate!

>“She refused to buy a new rug for herself–she said they were way too expensive. She’s always been very frugal, and she clips every coupon and shops every sale at Noah’s Market. In fact I like to tease her that Noah actually loses money whenever she comes in the store.

So we're looking for someone who's buying things on sale...

## Doing (Pandas)

We have the wholesale cost of items, along with the unit price and quantity of each order. That means we can define the profit for each as:

```python
df['profit'] = df.qty * (df.unit_price - df.wholesale_cost)
```

Which means we can see which customer represented the lowest profit (or greatest loss) in aggregate:

```python
df.groupby(
  ['name','phone']
).agg(
  {'profit':'sum'}
).sort_values(
  by='profit'
).head()
```

Emily, you're a thief!

## Doing (Datasette)

Having a `noahs_order_detail` view with the same structure as my pandas DataFrame makes this conversion pretty smooth:

```sql
select
  name,
  phone,
  sum(qty * (unit_price - wholesale_cost)) as profit
from
  noahs_order_detail
group by
  name,
  phone
order by
  profit
```