Differences

This shows you the differences between two versions of the page.

Link to this comparison view

public:gsoc:2015:nurendra [2018/01/06 19:29] (current)
willem ↷ Page moved and renamed from public:gsoc:nurendra_gsoc2015 to public:gsoc:2015:nurendra
Line 1: Line 1:
 +====== GSOC 2015 Documentation ======
  
 +===== Projects =====
 +
 +  - Realtime Translation using Google Translate
 +  - Realtime Translation using Apertium
 +  - Web application to compare statistics of Stock Price, TV Mentions and Twitter
 +
 +===== Technical Documentation =====
 +
 +All the technical details are commented in the codes and the documentation is available in the Readme'​s of their directories. The variables, classes and other components of the code are named properly in Camel Case for easier understanding of the code. \\
 +\\
 +**Repositories:​**
 +  * https://​github.com/​Akirato/​goslateTranslator - Google Translate Translator
 +  * https://​github.com/​Akirato/​apertiumTranslator - Apertium Translator
 +  * https://​github.com/​Akirato/​sentimentAnalysisTool - Tool for Sentiment Analysis
 +  * https://​github.com/​Akirato/​statsChart - The entire web application for statistics comparison
 +  * https://​github.com/​Akirato/​statsChart-backend - The backend of the statistics web application
 +
 +===== How to use? =====
 +
 +==== Google Translate and Apertium Realtime Translation ====
 +
 +The instruction to use the codes directly are given in the Readme of the\\
 +repositories.\\
 +A sample of translator from English to Spanish is available at\\ 
 +http://​gsocdev.ccextractor.org/​~nurendra/​translated/​test2/​tail.php
 +
 +==== Web Application for comparing Statistics ====
 +
 +The application is presently hosted at https://​95.211.109.210/​statsChart/​default/​index \\ 
 +It has been built on Web2py framework.\\
 +  - Make a MySQL database called "​statschart"​ and run the three scripts given in https://​github.com/​Akirato/​statsChart-backend
 +  - Download web2py from http://​web2py.com/​init/​default/​download
 +  - Clone https://​github.com/​Akirato/​statsChart into web2py/​applications/​
 +  - Go to web2py/​application/​statsChart/​models/​db.py and connect your databases.
 +  - Run the web2py server.
 +  - The application should be hosted at <​host-server>/​statsChart/​default/​index
 +
 +**Deployment on a new server:**\\
 +  - Make a MySQL database on the server called "​statschart"​ and run the three scripts given in https://​github.com/​Akirato/​statsChart-backend
 +  - Install and deploy web2py on a new server.
 +  - Several Deployment Recipes for common servers are given at http://​web2py.com/​books/​default/​chapter/​29/​13/​deployment-recipes
 +  - After this is done, a web2py/ directory will be made in the server.
 +  - Clone https://​github.com/​Akirato/​statsChart into web2py/​applications/​
 +  - Go to web2py/​application/​statsChart/​models/​db.py and connect your databases.
 +  - Run the web2py server.
 +  - The application should be hosted at <​host-server>/​statsChart/​default/​index
 +
 +===== How to evaluate? =====
 +
 +==== Google Translate and Apertium Realtime Translation ====
 +
 +Repositories of both the translators have methodAnalysis/​analyse.py file.
 +Execute this file if the code is working properly.
 +Also English->​Spanish realtime translation is available at\\
 +http://​gsocdev.ccextractor.org/​~nurendra/​translated/​test2/​tail.php ​
 +
 +==== Web Application for comparing Statistics ====
 +
 +The web application is hosted on https://​95.211.109.210/​statsChart/​default/​index
 +\\
 +To look at the entire code, go to https://​95.211.109.210/​admin/​default/​index
 +Give the password: "​akirato123"​ and select "​statsChart"​ to check the entire code.
 +
 +
 +===== Contribution to blog =====
 +
 +The subtitles generated by CCExtractor can now be translated and be made available to
 +a larger audience due to the realtime translation tool.\\
 +The tool uses Google Translate and Apertium to provide online and offline translation
 +respectively.\\
 +The Statistics website collects data from Twitter using Twitter API, from TV advertisements\\
 +using CCExtractor and shows it effects on the Stock Price which are updated using Google \\
 +Finance. This will be very useful for opinion collection and looking at the effects of\\
 +advertisements on Social Media.
  • public/gsoc/2015/nurendra.txt
  • Last modified: 2018/01/06 19:29
  • by willem