Search Image Faces in Repository
Kibana Dashboard for Product Feedback Analysis
Single framework for translation, image detection, image search , face collection and search, managing content in enterprise repository, and analysis of documents in the repository just at your fingertips. Image analysis to find text in the images so that we can translate text of the image,image moderation to find nude or obscene pictures. .Same framework serves product feedback analyse and creates beautiful dashboards using elasticsearch and Kibana
Turbo Mobile Bot App is available on slack workspace :
What it does
It does almost everything what AWS AI framework provides.
Find images in S3 bucket repository based on keyword,
Image recognition to get all information from image.i.e. Labels, text, faces, celebrities and moderation
List face to create face collection
Search faces in the S3 bucket repository
Search user by Name , User ID and Face
Analysis of Product Feedback using Elastic Search and Kibana
Provide dashboard containing feedback sentiments, keywords, ratings in customer feedback of products
How I built it
I learnt everything from AWS AI libraries and utilized Amzon Lex bot framwework to create 'Turbo Mobile Bot' for an enterprise called ' Turbo Mobile'. I integrated this bot with 'Slack' and Amazon ElasticSearch. Slack is the front end where all data of images, documents and product review are stored in S3 bucket. Whenever any item is uploaded in S3 bucket, event is triggered and based on type of uploaded file ( image, doc or feedback) details are logged on Elastic Search engine to create multiple indices. Elastic Search is interfaced with Kibana. Amazing and beautiful dashboards are created on Kibana for live analysis of images, documents and customer feedback. Two different Lambda functions are used first to receive S3 trigger whenever item is put in the bucket and second one is to fulfill intents from Lex bot
Challenges I ran into
I got challenges in analyzing images, detecting input languages. Controlling the data flow in intents of Lex. But with practice, I got the right slot setting and data flow for Lex and then integrated Lex with Slack. To manage multiple intents was tedious because bot serves so many task in single lambda function
Accomplishments that I'm proud of
I have learnt Amazon AI and ML libraries, it was fun integrating Lex with Slack and Kibana. I created my own search engine first time. I created an end to end solution.
What I learned
All AI libraries, Lex, Slack, Kibana, ElasticSearch , EC2 , Kinesis oh so manythings
What's next for Turbo Mobile Bot - enterprise bot based framework
Lot of stuff is pending, immediate next is to create image capturing prototype using Raspberry Pi and upload on S3 bucket to detect the person which shall be used for employee identification, Then live one to one audio translation on mobile , live image translator on mobile, Hotword ( Wakeword) based apps and son on..........