Store chat bot

An assistant chatbot for Seashore seashell that helps answer customer queries.

The chatbot will greet the user, then answer their question about store/product information or other, more complex concerns.

The chatbot will do so by doing a basic check of the user input, and redirecting the query to the appropriate mini-bot, where a more in-depth response will be handled.

Table of contents

• Setup
  • Windows
  • Unix
  • Run Bot
• Overall chat structure
• Mini Bots (products & store information)
• Tests
  • All cases
  • Selected cases
• API
  • Store Info API
  • Product Info API
  • Database API
• New Features
  • User interface
  • Distance Matrix API
  • Product carousel
  • Paypal sandbox

Setup

REQUIREMENTS: Facebook Messenger App, Dialogflow agent, Google API Key, Paypal sandbox

1. Create Dialogflow agent and connect it to project on Google console. Then import the dialogflow-agent.zip to your project. Refer to the first part of this blog for more details.
2. Create a Facebook Messenger App and connect it to the Dialogflow agent. Refer to this blog
3. On Goolge console, go to your project and enable Distance Matrix API and create a Google API Key.
4. Create a Paypal sandbox account and retrieve client_id and secret. Refer to this blog
5. Set environment variables.

Environment variables

Windows

Powershell

python3 -m venv venv
venv\Scripts\activate
$env:API_KEY=<Your key>
$env:client_id=<Your Paypal client id>
$env:secret=<Your Paypal secret>

Command Prompt

python3 -m venv venv
venv\Scripts\activate
setAPI_KEY=<Your key>
setclient_id=<Your Paypal client id>
setsecret=<Your Paypal client secret>

Unix (Bash)

./install.sh
export API_KEY=<Your key>
export client_id=<Your Paypal client id>
export secret=<Your Paypal secret>

Run

python3 index.py

Overall chat structure

Whenever user sends a message on Facebook messenger, a request containing the message will be sent to Dialogflow agent. Intent of the message is detected by the agent. If the intent is Default Welcome intent or Default Fallback Intent, Dialogflow will handle it itself and respond to Messenger app. Otherwise, Dialogflow sends another request to our web server to handle fullfilment.

Product & Store Mini-bots

This mini-bot will handle any question related to product price, stock, and store information. The bot first determines what topic is of interest (product or store information) and breaks the user's message down into keywords. The bot then compares these words and checks them in order to create the most appropriate response, and returns the response to the main function.

See in-depth documentation here.

Tests

Disclaimer: Due to new updates, this old tests are currently not working

We are using pytest for all of our tests. Our test cases can be found in the test folder.

Test all cases:

python -m pytest

Test selected cases:

• database
• store_info
• prod_info
• intent_detection

python -m pytest -v -m <selected case>

API

You can call the API from any other Python script to check store's information or products information. This does not require the bot to be running or a diagflow key.

Make sure to import the API correctly, as the API is not imported by default, and is dependent on your project's file hierarchy.

Store Info API

From this bot you can query the store's information.

Example import for store info method:

from app.shop.store_info import *

handle_store_info(string) -> string

message = "Where is the store?"
output = handle_store_info(message)
# returns string: "It is 123 Main St"

parse(string) -> object

message = "Where is the store?"
print(parse(message))
# returns object: {'request': 'address'}

Product Info API

From this bot you can query the store's products information.

Example import for product info bot:

from app.products.product_info import handle_product_info

handle_product_info(string) -> string

message = "How much does a banana cost?"
output = handle_product_info(message)
# returns string: "Bananas cost $0.67 per kg."

Database API

The database API also allows queries straight from the database.

Example import for database:

from app.database import Database

Database lifecycle:

db = Database.instance()
db.connect()
db.init_database()
# queries or methods
db.close()

get_product("id", str) -> List

output = db.get_product("id", "4011")
# returns list: [OrderedDict([('id', '4011'), ('name', 'banana'), ('names', 'bananas'), ('price', 0.67), ('price_scale', 'per kg'), ('in_stock', True), ('calories', 89), ('protein', '1.1 g'), ('carbs', '22.8 g'), ('sugar', '12.2 g'), ('fat', '0.3 g')])]

New Features

User interface

The chatbot is integrated with Facebook Messenger App so it has a new user interface. User can go to Seashore seashell page on Facebook and interact with the chatbot through Messenger. With this new UI, it's easier for user to interact with the bot and see media content.

Estimated delivery time

When user asks about estimated delivery time from store to their place, the bot will ask for user address. Then it makes a request to Google Distance Matrix API to get the estimated delivery time.

Product carousel

On request, the bot will return a carousel of products. Each product will have a button for quick checkout.

Paypal checkout

When user hits Checkout button, another button for Paypal checkout will be shown.

When user hits Paypal button, they will be directed to Paypal website to complete the payment.

After completing authorization step, the user will be directed to confirmation website. This completes the payment process.