StudyAssistant

24-Transcription-Study-Assistant

Description

This project aims to create a tool that will assist in taking transcripts of courses and breaking them down into pieces that can more easily be used for studying. This tool is intended to be utilized by all audiences but may be particularly beneficial for neurodivergent students that face sensory overload during long lectures.

This project is divided into three primary components.

1. Hardware
2. Web Software
3. Machine Learning

The hardware team is focusing on developing a video taking device for use with Microsoft Teams, and other meeting software. This is meant to be an alternative to the Meeting Owl 3. The primary benefit that our device has over the meeting owl is cost. Our device aims to be manufacturable as a proof of concept for under $200. This is much cheaper than the $1050 price tag the Meeting Owl 3 costs. Our device is meant to be an assistant for tracking presenters in a classroom like environment for meetings.

The software team is focusing on developing a website that will support all the necessary functions of our application, including our machine learning models. The website will be designed so that video transcripts and other text based files can be uploaded to the server. Then the server will allow you to modify and clean up the text, generate summaries of the content, identify relevant topics, and possibly build study aids. An example study aid would be question and answer flashcards.

The machine learning team is focusing on the models that allow us to summarize text and topics. This is primarily done through transformers and Latent Dirichlet Allocation models.

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Visuals

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Automatic Installation

Running this project locally can be done through a few simple steps. The first step is to install python 3.10.0. Once that is done, you should be able to install the project by running setup.py. Once this is completed you can run the entire project through run.py

Manual Installation

Running this project locally can be somewhat difficult as there are external requirements. Assuming that the proper python version (as noted in the requirements section) is installed it should be possible to install relevant python library files by running the command pip install -r requirements.txt out of the software home directory.

This application uses the SpaCy library and its language kits. Unfortunately, these can not be installed through pip. Instead, they must be installed through the command line. There are three model sizes that must be installed to be able to run the software. These models can be found from SpaCy. Download using the following commands:

• python -m spacy download en_core_web_sm
• python -m spacy download en_core_web_md
• python -m spacy download en_core_web_lg

The SpaCy en_core_web_trf model is not required.

The step is installing MinIO. MinIO is an object store that can be downloaded here. Both the server, and the client, must be downloaded. If on Windows, this will give two files: minio.exe and mc.exe. Place these two executeables in a folder that you can run things from. In order to run the web backend you must launch a console out of the folder and run the command: minio server minio_data. This starts the object store which allows for storing log files as well as files that are uploaded to the program.

The next step is to set up users and buckets inside MinIO. Access the MinIO web interface. Log in with the default username minioadmin and default password minioadmin. Next you should see a sidebar of options. Click on Administrator -> Identity -> Users -> Create User. You should then create a user called tsa-backend. Give it a password you can remember as well as read write access then save. Next navigate to Administrator -> Buckets. We need to create a bucket called tsa-backend-endpoint-logs and another bucket called tsa-backend-user-files. We also need to create a bucket called tsa-backend-account-info. No parameters need to be changed on these buckets, so they can be saved right after setting their names.

After the MinIO setup is complete you need to add a .env file under the API folder. This file should have the below format. You should fill in the password for the user you created under the access key. If your object store is on another computer then set the endpoint url to its location. If you decided to name your user something other than tsa-backend above than change that as well.

AWS_ACCESS_KEY_ID = "tsa-backend"
AWS_SECRET_ACCESS_KEY = "minioadmin"
MINIO_URL = "http://localhost:9000"
ENDPOINT_LOG_PATH = "endpoint_log_file.json"
ENDPOINT_LOGS_BUCKET = 'tsa-backend-endpoint-logs'
USER_FILES_BUCKET = 'tsa-backend-user-files'
ACCOUNT_INFO_BUCKET = 'tsa-backend-account-info'
LOGIN_TRACKING_BUCKET = 'tsa-backend-login-tracking'
BACKEND_URL_FOR_TESTS = "http://localhost:8844"

If you are planning on testing the backend without the use of a frontend then you will need to either use curl commands or install a tool to send http requests. Much of the testing during development was done using Postman.

Running the Web Software

• Pull down the repo in Visual Studio Code.
• cd or navigate into the transcript-study-assistant folder using the terminal within VS Code.
• Type in "ng serve". The website should run at an address like http://localhost:4200/.
• If the website does not run, try running "npm install -g @angular/cli".
• Please contact Teresa with further questions. I'm happy to improve these instructions further.

Requirements

• Python version 3.10

Usage

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Roadmap

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Contributing

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Authors and acknowledgment

Hardware Team:

• Angela Christie
• Alexander Karpov

Software Team:

• Teresa Toohill
• Grant Fass

Machine Learning Team:

• Nicholas Kaja
• Grant Fass

Advisor:

• Dr. Sohum Sohoni

Institution:

• Milwaukee School of Engineering 2022-2023

License

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Project status

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