## Inspiration

The inspiration for InstaPresent came from our frustration with constantly having to create presentations for class and being inspired by the 'in-game advertising' episode on Silicon Valley.

## What it does

InstaPresent is a tool that uses your computer's microphone to generate a presentation in real-time. It can retrieve images and graphs and summarize your words into bullet points.

## How we built it

We used Google's Text To Speech API to process audio from the laptop's microphone. The Text To Speech is captured when the user speaks and when they stop speaking, the aggregated text is sent to the server via WebSockets to be processed.

## Challenges We ran into

Summarizing text into bullet points was a particularly difficult challenge as there are not many resources available for this task. We ended up developing our own pipeline for bullet-point generation based on part-of-speech and dependency analysis. We also had plans to create an Android app for InstaPresent, but were unable to do so due to limited team members and time constraints. Despite these challenges, we enjoyed the opportunity to work on this project.

## Accomplishments that we're proud of

We are proud of creating a web application that utilizes a variety of machine learning and non-machine learning techniques. We also enjoyed the challenge of working on an unsolved machine learning problem (sentence simplification) and being able to perform real-time text analysis to determine new elements.

## What's next for InstaPresent

In the future, we hope to improve InstaPresent by predicting what the user intends to say next and improving the text summarization with word reordering.
## Inspiration

We're 4 college freshmen that were expecting new experiences with interactive and engaging professors in college; however, COVID-19 threw a wrench in that (and a lot of other plans). As all of us are currently learning online through various video lecture platforms, we found out that these lectures sometimes move too fast or are just flat-out boring. Summaread is our solution to transform video lectures into an easy-to-digest format.

## What it does

"Summaread" automatically captures lecture content using an advanced AI NLP pipeline to automatically generate a condensed note outline. All one needs to do is provide a YouTube link to the lecture or a transcript and the corresponding outline will be rapidly generated for reading. Summaread currently generates outlines that are shortened to about 10% of the original transcript length. The outline can also be downloaded as a PDF for annotation purposes. In addition, our tool uses the Google cloud API to generate a list of Key Topics and links to Wikipedia to encourage further exploration of lecture content.

## How we built it

Our project is comprised of many interconnected components, which we detail below:

**Lecture Detection**

Our product is able to automatically detect when lecture slides change to improve the performance of the NLP model in summarizing results. This tool uses the Google Cloud Platform API to detect changes in lecture content and records timestamps accordingly.

**Text Summarization**

We use the Hugging Face summarization pipeline to automatically summarize groups of text that are between a certain number of words. This is repeated across every group of text previous generated from the Lecture Detection step.

**Post-Processing and Formatting**

Once the summarized content is generated, the text is processed into a set of coherent bullet points and split by sentences using Natural Language Processing techniques. The text is also formatted for easy reading by including “sub-bullet” points that give a further explanation into the main bullet point.

**Key Concept Suggestions**

To generate key concepts, we used the Google Cloud Platform API to scan over the condensed notes our model generates and provide wikipedia links accordingly. Some examples of Key Concepts for a COVID-19 related lecture would be medical institutions, famous researchers, and related diseases.

**Front-End**

The front end of our website was set-up with Flask and Bootstrap. This allowed us to quickly and easily integrate our Python scripts and NLP model.

## Challenges we ran into

1. Text summarization is extremely difficult -- while there are many powerful algorithms for turning articles into paragraph summaries, there is essentially nothing on shortening conversational sentences like those found in a lecture into bullet points.
2. Our NLP model is quite large, which made it difficult to host on cloud platforms

## Accomplishments that we're proud of

1) Making a multi-faceted application, with a variety of machine learning and non-machine learning techniques.

2) Working on an unsolved machine learning problem (lecture simplification)

3) Real-time text analysis to determine new elements

## What we learned

1) First time for multiple members using Flask and doing web development

2) First time using Google Cloud Platform API

3) Running deep learning models makes my laptop run very hot

## What's next for Summaread

1) Improve our summarization model through improving data pre-processing techniques and decreasing run time

2) Adding more functionality to generated outlines for better user experience

3) Allowing for users to set parameters regarding how much the lecture is condensed by
## Inspiration

Oftentimes when we find ourselves not understanding the content that has been taught in class and rarely remembering what exactly is being conveyed. And some of us have the habit of mismatching notes and forgetting where we put them. So to help all the ailing students, there was this idea to make an app that would give the students curated automatic content from the notes which they upload online.

## What it does

A student uploads his notes to the application. The application creates a summary of the notes, additional information on the subject of the notes, flashcards for easy remembering and quizzes to test his knowledge. There is also the option to view other student's notes (who have uploaded it in the same platform) and do all of the above with them as well. We made an interactive website that can help students digitize and share notes!

## How we built it

Google cloud vision was used to convert images into text files. We used Google cloud NLP API for the formation of questions from the plain text by identifying the entities and syntax of the notes. We also identified the most salient features of the conversation and assumed it to be the topic of interest. By doing this, we are able to scrape more detailed information on the topic using google custom search engine API. We also scrape information from Wikipedia. Then we make flashcards based on the questions and answers and also make quizzes to test the knowledge of the student. We used Django as the backend to create a web app. We also made a chatbot in google dialog-flow to inherently enable the use of google assistant skills.

## Challenges we ran into

Extending the platform to a collaborative domain was tough. Connecting the chatbot framework to the backend and sending back dynamic responses using webhook was more complicated than we expected.
Also, we had to go through multiple iterations to get our question formation framework right. We used the assumption that the main topic would be the noun at the beginning of the sentence. Also, we had to replace pronouns in order to keep track of the conversation.

## Accomplishments that we're proud of

We have only 3 members in the team and one of them has a background in electronics engineering and no experience in computer science and as we only had the idea of what we were planning to make but no idea of how we will make. We are very proud to have achieved a fully functional application at the end of this 36-hour hackathon. We learned a lot of concepts regarding UI/UX design, backend logic formation, connecting backend and frontend in Django and general software engineering techniques.

## What we learned

We learned a lot about the problems of integrations and deploying an application. We also had a lot of fun making this application because we had the motive to contribute to a large number of people in day to day life. Also, we learned about NLP, UI/UX and the importance of having a well-set plan.

## What's next for Noted

In the best-case scenario, we would want to convert this into an open-source startup and help millions of students with their studies. So that they can score good marks in their upcoming examinations.