https://github.com/abhay-vaidya/doggo

A chatbot and web app system to streamline the ordering system for small food vendors.
https://github.com/abhay-vaidya/doggo

chatbot food food-vendor hot-dogs transaction

Last synced: 11 days ago
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A chatbot and web app system to streamline the ordering system for small food vendors.

• Host: GitHub
• URL: https://github.com/abhay-vaidya/doggo
• Owner: abhay-vaidya
• Created: 2017-01-09T20:04:10.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-01-28T05:14:01.000Z (almost 8 years ago)
• Last Synced: 2024-10-24T11:11:04.771Z (27 days ago)
• Topics: chatbot, food, food-vendor, hot-dogs, transaction
• Language: JavaScript
• Homepage:
• Size: 36.4 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

![DogGo Logo](https://i.imgur.com/26pMCAx.png)

# DogGo: Hot dogs for the next generation

![Screenshot](https://i.imgur.com/f9ik7l5.png)

##Inspiration

We were inspired very locally, by the resident hot dog stand owner, Nasir Al-Huttam. He's been taking orders for the UTSC community for almost a decade and has been in need of an easier way of taking orders, as taking orders through text has been somewhat cumbersome for him.

##Problem

Small businesses, especially food stands, have a difficult time managing non-immediate orders. Current solutions include receiving texts and calls from customers which is unintuitive because this process makes it difficult to determine which orders to take first.

##Our solution

Streamline the order process and give the business a clean, interactive interface to track user orders and respond efficiently and cleanly.

##What it does

Using Microsoft Azure and Recast.ai, we've created a chatbot that takes orders from the customer from the Facebook Messenger app, and gives them an interactive and intuitive experience akin to buying from another human, allowing them to even pay for their meal in advance, and further streamlining the process.

##How we built it

For frontend, we used HTML and CSS in combination with JQuery and Javascript to receive JSON files from the serverside, and interpret that information into an interface for the business owner to look.

Recast.ai as well as the Microsoft Bot Framework was used to create and deploy the chatbot and program natural language and reactive conversation with the user. Node.js was also used.

The backend is powered by Python and Flask, using Microsoft Azure to deploy the database. PostgreSQL, SQLAlchemy were also used.