The technology industry is growing faster than ever, but memorizing syntax can be daunting for beginners. This drew the inspiration for speak: a compiler that understands English instructions and translates them into compilable Python code. This makes coding more accessible, easy, and enjoyable.
What it does
speak reads the text that the user inputs. It utilizes set of Python synonyms and keywords, spaCy dependencies trees and Natural Language Processing, as well as NLTK for determining the parts of speech for each instruction.
How we built it
Challenges we ran into
Because this compiler was a completely new and novel idea, there were no precedents to use as inspiration. We ran into many difficult concepts, problems, and implementation decisions. These decisions ranged from pivoting away from our initial NLP backend flow on NodeRED to adapting NLTK to determine overall meaning rather than the sentiment of user input.
Accomplishments that we're proud of
We are incredibly proud to have created a completely novel product in the space of one weekend at TreeHacks. We solved problems, collaborated, struggled, and found eventual success. Notably, we are quite proud of the fact that we figured out how to implement this tool without having to use expensive online Natural Language Classifiers.
What we learned
spaCy + NLTK + Natural Language Processing concepts + how to create syntax trees + how to process the meaning of English text + how to configure a centOS Apache web server to run Python + Flask + how to use a domain.com domain
What's next for speak
We want to continue with speak and are excited to expand upon it in the future. We also hope to iterate upon it until it is ready for a software patent or copyright. Lastly, we plan to use a speech-to-text cloud service to allow verbal user input, which will further improve accessibility.