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Eureka!

While surfing through my Instagram feed, a random thought struck in my head. How about creating a tool that gives me random quotes by analyzing the picture and moods. I can’t get this thought out of my mind and I geared up to build something exciting.

In this post, I have shared my experience in each stage of building https://quotes-maker.com/ website, and here is the code.

Searching Dataset

First I wanted a large dataset for quotes/captions. Websites like kaggle.com are best for such datasets. I found a Dataset with 500,000 quotes on https://www.kaggle.com/manann/quotes-500k.

Dataset Cleanup

This dataset has 3 columns:

quote, author, category

“you were chasing stones forever forgetting about diamonds”, Skye Elf, “life…


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Image Source: https://landbot.io/blog/how-to-create-a-chatbot-for-website/

Welcome to the series for Do It Yourself Chatbots.

Over a couple of free weekends I’ve created tutorial articles to create chatbots on your own, you just need to follow the steps and you’ll have the chatbot running locally. Below is the list:

  1. Run your Python Scripts as Slack Commands [Chatops]
  2. QnA Bot with Flask and Sklearn


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Source: https://www.hubtype.com/images/chatbot-for-website.png

QnA chatbot with a feedback mechanism.

Do you have a list of questions and answer pairs? And want to create chatbot out of it? With the feedback on the answers? This article will help you.

Please keep in mind that this bot will only provide the answers from given QnA pairs. Intent classification, Context generation, Entity extraction, etc is for some other day. For python experts here is the code. Bye. Enjoy! For beginners here how it works:

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Follow the instructions from Readme.md


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Have a python script for boring and repetitive tasks but not sure how everyone can use it? then this article is for you!

Slack commands for repetitive and boring tasks is what your team needs.

You can achieve this using AWS chalice framework.

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AWS Chalice allows you to quickly create and deploy applications that use Amazon API Gateway and AWS Lambda.

$ pip install chalice
$ chalice new-project helloworld && cd helloworld
$ cat app.py
from chalice import Chaliceapp = Chalice(app_name="helloworld")@app.route("/")
def index():
return {"hello": "world"}
$ chalice deploy
...
https://endpoint/dev
$ curl https://endpoint/api
{"hello": "world"}
$ chalice…

Yogesh Ingale

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