https://github.com/cloudacademy/aws-lexv2-chatbot
AWS Lex v2 Chatbot
https://github.com/cloudacademy/aws-lexv2-chatbot
aws chatbot cloudacademy devops lex tmdb
Last synced: 5 months ago
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AWS Lex v2 Chatbot
- Host: GitHub
- URL: https://github.com/cloudacademy/aws-lexv2-chatbot
- Owner: cloudacademy
- Created: 2021-10-18T23:31:38.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-05-13T06:19:59.000Z (over 3 years ago)
- Last Synced: 2025-04-09T12:21:50.484Z (6 months ago)
- Topics: aws, chatbot, cloudacademy, devops, lex, tmdb
- Language: Shell
- Homepage:
- Size: 12.8 MB
- Stars: 6
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Amazon Lex Movie Recommendations Chatbot
## Introduction
A simple [Amazon Lex](https://aws.amazon.com/lex/) Chatbot that is able to provide movie recommendations for a given movie genre. This implementation is performed using [Amazon Lex V2](https://docs.aws.amazon.com/lexv2/latest/dg/what-is.html).
## Amazon Lex V2 Slides
[AWSLexV2Chatbots.pdf](./doc/AWSLexV2Chatbots.pdf)
## Integration
Movie recommendation data is provided by The Movie Database ([TMDB](https://www.themoviedb.org/)).
## Lambda Function
The chatbot integrates with [TMDB API](https://developers.themoviedb.org/3) endpoint using a custom AWS [Lambda Function](./lambda/lambda_function.py) developed in Python3:
```python
import os
from tmdbv3api import TMDb, Discover
tmdb = TMDb()
tmdb.api_key = os.getenv('APIKEY')
GENRES = {
"action": 28,
"adventure": 12,
"animation": 16,
"comedy": 35,
"crime": 80,
"documentary": 99,
"drama": 18,
"family": 10751,
"Fantasy": 14,
"history": 36,
"horror": 27,
"music": 10402,
"mystery": 9648,
"romance": 10749,
"science fiction": 878,
"thriller": 53,
"war": 10752,
"western": 37
}
def get_slots(intent_request):
return intent_request['sessionState']['intent']['slots']
def get_slot(intent_request, slotName):
slots = get_slots(intent_request)
if slots is not None and slotName in slots and slots[slotName] is not None:
return slots[slotName]['value']['interpretedValue']
else:
return None
def get_session_attributes(intent_request):
sessionState = intent_request['sessionState']
if 'sessionAttributes' in sessionState:
return sessionState['sessionAttributes']
return {}
def elicit_intent(intent_request, session_attributes, message):
return {
'sessionState': {
'dialogAction': {
'type': 'ElicitIntent'
},
'sessionAttributes': session_attributes
},
'messages': [ message ] if message != None else None,
'requestAttributes': intent_request['requestAttributes'] if 'requestAttributes' in intent_request else None
}
def close(intent_request, session_attributes, fulfillment_state, message):
intent_request['sessionState']['intent']['state'] = fulfillment_state
return {
'sessionState': {
'sessionAttributes': session_attributes,
'dialogAction': {
'type': 'Close'
},
'intent': intent_request['sessionState']['intent']
},
'messages': [message],
'sessionId': intent_request['sessionId'],
'requestAttributes': intent_request['requestAttributes'] if 'requestAttributes' in intent_request else None
}
def MovieRecommendation(intent_request):
session_attributes = get_session_attributes(intent_request)
slots = get_slots(intent_request)
genre = get_slot(intent_request, 'genre')
discover = Discover()
movies = discover.discover_movies({
'primary_release_date.gte': '2019-01-01',
'primary_release_date.lte': '2021-12-01',
'with_genres': GENRES.get(genre.lower())
})
recs = ""
for movie in movies:
recs = recs + f" {movie.title}, "
text = f"Thank you. Your recommended movies are: {recs}"
message = {
'contentType': 'PlainText',
'content': text
}
fulfillment_state = "Fulfilled"
return close(intent_request, session_attributes, fulfillment_state, message)
def dispatch(intent_request):
intent_name = intent_request['sessionState']['intent']['name']
response = None
if intent_name == 'MovieRecommendation':
return MovieRecommendation(intent_request)
#elif intent_name == 'OtherIntent':
# return OtherIntent(intent_request)
raise Exception('Intent with name ' + intent_name + ' not supported')
def lambda_handler(event, context):
response = dispatch(event)
return response
```
## Installation
1. Register for a TMDB API key
2. Within the **install.sh** file, update the ```APIKEY``` variable:
**install.sh**:
```
APIKEY=TMDB_API_KEY_HERE
```
3. Execute the **install.sh** script
4. Log in to the AWS Lex console and then import the newly created **lex-movierecommendations.zip** package
Set the following options:
- **Bot name**, enter ```MovieRecommendations```
- **IAM permissions**, select **create a role with basic Amazon Lex permissions**
- **Childrens Online Privacy Protection Act**, select **No**
5. After the import has succeeded - in the lefthand menu navigate to **Deployment/Aliases** for the newly created **MovieRecommendations** bot and click on the **TestBotAlias**
6. Within the **TestBotAlias** click on the **English** language
7. Select **movierecommendations** for **Source**, and **$LATEST** for **Lambda function version or alias**. Click Save.
8. In the lefthand menu navigate to **Bot versions/Draft version**, and then under **Languages** click on the **English** language link.
9. In the bottom menu bar click on the **build** button to compile the MovieRecommendations Chatbot.
10. Confirm that the MovieRecommendations Chatbot build has completed successfully.
11. Confirm that the MovieRecommendations Chatbot build has completed successfully, and then click on the **Test** button to activate the Chatbot.
12. Within the Chatbot test pane, enter any of the following utterances to start the Chatbot conversation:
```
recommend a movie
recommend a {genre} movie
recommend a {genre} film
I want to watch a movie
I want to watch a {genre} movie
```
Note: The following movie genres are supported:
```
Action
Adventure
Animation
Comedy
Crime
Documentary
Drama
Family
Fantasy
History
Horror
Music
Mystery
Romance
Science Fiction
Thriller
War
Western
```
Example Chatbot conversation:
