## Inspiration

The time-consuming process of watching videos and taking notes can discourage students from retaining knowledge and staying engaged. By creating a notes, bullet point, or essay summary from any YouTube link, YouWrite is an efficient way for students to grasp knowledge from videos.

## What it does

After logging in, the application will redirect you to another page that will allow you to make your conversions. All you need is to submit a YouTube link, include a timestamp, and input approximately how many words you want your summary to be.

## How we built it

The YouTube URL to summary functionality was built using the OpenAI API and youtube-to-transcript API,. Here, the user can input a YouTube URL and it will be converted to transcript and then summarized using AI.

The login system was created using Flask and MongoDB, where a user's login information inputed through our web application and then securely routed to our database through Flask.

The front end was built using React and provides a responsive user interface for the user to route data to the back end.

## Challenges we ran into

Integrating the HTML forms with MongoDB was a big challenge we faced during this project. Also, working on a time crunch with limited experience with the tech stack we wanted to use was very difficult since many of our team members had to learn them from scratch.

## Accomplishments that we're proud of

While we ran into a lot of technical issues because we were new to many of the things that we worked with, we were able to persevere, problem solve, and get the majority of our project finished and integrated together!

## What we learned

We learned how to integrate Python backend with React frontend through Flask, how to use Mongodb, and how to use OpenAI API. More importantly, we learned how fun (but difficult) hackathons can be and how to manage our time effectively working in a project setting!

## What's next for YouWriter

Creating another database to store the notes a user has generated so they can continue to access these documents in the future. Another goal for YouWriter is to generate images onto the notes for more comprehensive learning and cohesive notes.
## 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.
## Inspiration

Us college students all can relate to having a teacher that was not engaging enough during lectures or mumbling to the point where we cannot hear them at all. Instead of finding solutions to help the students outside of the classroom, we realized that the teachers need better feedback to see how they can improve themselves to create better lecture sessions and better ratemyprofessor ratings.

## What it does

Morpheus is a machine learning system that analyzes a professor’s lesson audio in order to differentiate between various emotions portrayed through his speech. We then use an original algorithm to grade the lecture. Similarly we record and score the professor’s body language throughout the lesson using motion detection/analyzing software. We then store everything on a database and show the data on a dashboard which the professor can access and utilize to improve their body and voice engagement with students. This is all in hopes of allowing the professor to be more engaging and effective during their lectures through their speech and body language.

## How we built it

### Visual Studio Code/Front End Development: Sovannratana Khek

Used a premade React foundation with Material UI to create a basic dashboard. I deleted and added certain pages which we needed for our specific purpose. Since the foundation came with components pre build, I looked into how they worked and edited them to work for our purpose instead of working from scratch to save time on styling to a theme. I needed to add a couple new original functionalities and connect to our database endpoints which required learning a fetching library in React. In the end we have a dashboard with a development history displayed through a line graph representing a score per lecture (refer to section 2) and a selection for a single lecture summary display. This is based on our backend database setup. There is also space available for scalability and added functionality.

### PHP-MySQL-Docker/Backend Development & DevOps: Giuseppe Steduto

I developed the backend for the application and connected the different pieces of the software together. I designed a relational database using MySQL and created API endpoints for the frontend using PHP. These endpoints filter and process the data generated by our machine learning algorithm before presenting it to the frontend side of the dashboard. I chose PHP because it gives the developer the option to quickly get an application running, avoiding the hassle of converters and compilers, and gives easy access to the SQL database. Since we’re dealing with personal data about the professor, every endpoint is only accessible prior authentication (handled with session tokens) and stored following security best-practices (e.g. salting and hashing passwords). I deployed a PhpMyAdmin instance to easily manage the database in a user-friendly way.
In order to make the software easily portable across different platforms, I containerized the whole tech stack using docker and docker-compose to handle the interaction among several containers at once.

### MATLAB/Machine Learning Model for Speech and Emotion Recognition: Braulio Aguilar Islas

I developed a machine learning model to recognize speech emotion patterns using MATLAB’s audio toolbox, simulink and deep learning toolbox. I used the Berlin Database of Emotional Speech To train my model. I augmented the dataset in order to increase accuracy of my results, normalized the data in order to seamlessly visualize it using a pie chart, providing an easy and seamless integration with our database that connects to our website.

### Solidworks/Product Design Engineering: Riki Osako

Utilizing Solidworks, I created the 3D model design of Morpheus including fixtures, sensors, and materials. Our team had to consider how this device would be tracking the teacher’s movements and hearing the volume while not disturbing the flow of class. Currently the main sensors being utilized in this product are a microphone (to detect volume for recording and data), nfc sensor (for card tapping), front camera, and tilt sensor (for vertical tilting and tracking professor). The device also has a magnetic connector on the bottom to allow itself to change from stationary position to mobility position. It’s able to modularly connect to a holonomic drivetrain to move freely around the classroom if the professor moves around a lot. Overall, this allowed us to create a visual model of how our product would look and how the professor could possibly interact with it. To keep the device and drivetrain up and running, it does require USB-C charging.

### Figma/UI Design of the Product: Riki Osako

Utilizing Figma, I created the UI design of Morpheus to show how the professor would interact with it. In the demo shown, we made it a simple interface for the professor so that all they would need to do is to scan in using their school ID, then either check his lecture data or start the lecture. Overall, the professor is able to see if the device is tracking his movements and volume throughout the lecture and see the results of their lecture at the end.

## Challenges we ran into

Riki Osako: Two issues I faced was learning how to model the product in a way that would feel simple for the user to understand through Solidworks and Figma (using it for the first time). I had to do a lot of research through Amazon videos and see how they created their amazon echo model and looking back in my UI/UX notes in the Google Coursera Certification course that I’m taking.

Sovannratana Khek: The main issues I ran into stemmed from my inexperience with the React framework. Oftentimes, I’m confused as to how to implement a certain feature I want to add. I overcame these by researching existing documentation on errors and utilizing existing libraries. There were some problems that couldn’t be solved with this method as it was logic specific to our software. Fortunately, these problems just needed time and a lot of debugging with some help from peers, existing resources, and since React is javascript based, I was able to use past experiences with JS and django to help despite using an unfamiliar framework.

Giuseppe Steduto: The main issue I faced was making everything run in a smooth way and interact in the correct manner. Often I ended up in a dependency hell, and had to rethink the architecture of the whole project to not over engineer it without losing speed or consistency.

Braulio Aguilar Islas: The main issue I faced was working with audio data in order to train my model and finding a way to quantify the fluctuations that resulted in different emotions when speaking. Also, the dataset was in german

## Accomplishments that we're proud of

Achieved about 60% accuracy in detecting speech emotion patterns, wrote data to our database, and created an attractive dashboard to present the results of the data analysis while learning new technologies (such as React and Docker), even though our time was short.

## What we learned

As a team coming from different backgrounds, we learned how we could utilize our strengths in different aspects of the project to smoothly operate. For example, Riki is a mechanical engineering major with little coding experience, but we were able to allow his strengths in that area to create us a visual model of our product and create a UI design interface using Figma. Sovannratana is a freshman that had his first hackathon experience and was able to utilize his experience to create a website for the first time. Braulio and Gisueppe were the most experienced in the team but we were all able to help each other not just in the coding aspect, with different ideas as well.

## What's next for Untitled

We have a couple of ideas on how we would like to proceed with this project after HackHarvard and after hibernating for a couple of days.

From a coding standpoint, we would like to improve the UI experience for the user on the website by adding more features and better style designs for the professor to interact with. In addition, add motion tracking data feedback to the professor to get a general idea of how they should be changing their gestures.

We would also like to integrate a student portal and gather data on their performance and help the teacher better understand where the students need most help with.
From a business standpoint, we would like to possibly see if we could team up with our university, Illinois Institute of Technology, and test the functionality of it in actual classrooms.