# Team Name - Meraki
# Problem Statement - Productivity and Automation
# Team Leader Email - kiranjobmailid@gmail.com
Constantly changing and evolving solutions, services and products available in the telecom, mobility, and IT management industry create sourcing, evaluation, and selection complexities that SMBs (small and mid-sized businesses) are not always equipped to address due to limited time or resources. How do SMBs more easily and efficiently sort out the thousands of options available to them?
Our solution is an IT product Service Recommender System which will help SMBs find verified solutions to simplify the sourcing, evaluation, and selection of telecom, mobility, and IT services and solutions.
====Requirements====
Python
NodeJS
JavaScript
====File Structure====
- APIS (Having APIs for user LogIn/ SignUp Data)
-env (Python Environment)
-credentials ( Static Database for Credentials for Demo )
credentils file
- main ( Main File having APIs for LogIn, Sign Up)
-FL Models ( Files to train the model using Federated Learning )
-CreateDataset.ipynb ( To create the generic real world dataset)
-FederatedLearning.ipynb ( To train the model using FL )
-mnistAlpha_FL_0_1.ipynb ( To Analyse the Pseudolabeling Semi Supervised Classification Result)
-restApiProduct.ipynb ( To create Endpoints for Service/Product Recommendation System)
-public (To store the trained models)
-models (Trained models)
-server (GraphQL Server for SignUp /LogIn)
-src
-datasources (Datasources Components for APIs)
--graphql (Resolvers and schemas for APIS)
src ( Front End using React and Node JS)
-apis ( APIs for Product Recommendation System )
-main.ipynb
-assests (Common Image files used )
- many image files
-components ( Components for the UI)
-Efficiency
-EfficiencyResult
-Footer
-Recomendation
-SimilarFeature
-SimilarProduct
-SimilarRating
-dataset ( Dataset for the Produts/ Services)
-dataset.csv
-dataset.json
-pages (UI Pages)
- pages for different screens
-services (Service)
-ProductService
====File Description====
==Backend (Python) ===
CreateDataset.ipynb: This notebook creates a synthetic dataset which we have used in our backend and training of the machine learning model under Federated Settings
FederatedLearning.ipynb: This notebook explains the Exploratory Data Analysis and overall training of the global model with client models under Federated Settings. Four models each for Security, Scalability, Ease Of Use, Latency are trained under Federated Settings.
mnistAlpha_0.1_FL.ipynb: This notebook is an example of how pseudo labellings can be used in our problem statement to introduce semi-supervised learning with only 10% labelled data.
==Frontend (React code base) ===
Please follow these commands to create react project and install required nodejs dependencies.
npx create-react-app fl-aotmp => Create the project
npm install bootstrap --save => Install Bootstrap
npm intall react-router-dom => For Router, Switch
< Install Other required Dependencies >
npm start
restApiProduct.ipynb => Host api end points as server using colab
=> Copy Generated Public URL and enter in PRODUCT_API_BASE_URL in services/ProductsService.js file. Eg : const PRODUCT_API_BASE_URL = "http://9d78-34-86-177-191.ngrok.io";
=> Sign up the form
=> Log In
=> Follow as directed in the demo video.# Team-Meraki