Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/cosmicoppai/geek-a-thon

Olympus: Enhancing Olympic Experience
https://github.com/cosmicoppai/geek-a-thon

auth0 cockroachdb fastapi gpt gradient-boosting-classifier hugging-face olympic python react roberta sklearn vimeo

Last synced: 7 days ago
JSON representation

Olympus: Enhancing Olympic Experience

Awesome Lists containing this project

README 

        
# Geek-Olympic Hackathon Project



![Olympic Rings](./screenshots/logo.png)



## Introduction



Welcome to our Geek-Olympic Hackathon project! This submission aims to enhance the Olympics experience for users and organizers through a web-based application that offers various exciting features. We have utilized the provided Olympic dataset along with other data from the internet to create a feature-rich platform for Olympic enthusiasts.



## Project Overview



Our web-based application, **Geek-Olympic WebApp**, provides users with an interactive interface to stream various Olympic events and engage with them in a fun manner. It incorporates the following key features:



1. **Live Streaming & Interaction**: We have integrated Vimeo, a live-streaming platform, to broadcast ongoing events. Users can interact with the live stream through comments, like events, and view multiple ongoing events simultaneously.



2. **Predictive Analysis**: Our system includes a prediction model based on Sklearn's Gradient Boosting Classifier. This model predicts the likelihood of an athlete winning a medal based on various attributes, such as age, height, weight, gender, sport, country, and the season of the Olympics. While the model's accuracy is approximately 45%, it provides valuable insights into athletes' performances.



3. **Sentiment Analyzer**: To gauge public sentiment during the event, we have implemented a sentiment analysis model based on Hugging Face's pretrained roBERTa model (cardiffnlp/twitter-roberta-base-sentiment). The sentiment analyzer processes tweets related to the Olympics, providing an overall accuracy of around 67% and an average f1 score of 0.635.



4. **Interactive Charts**: We have utilized various plotting types, including pie charts, bar plots, radar plots, line plots, and doughnut plots, to provide users with a better understanding of the Olympics data and enhance their overall experience.



5. **Authentication & Data Protection**: To ensure a smooth and secure user experience, we have integrated Auth0 for authentication, authorization, and data protection of our users.



6. **Chat Bot Assistance**: Our platform features a chatbot powered by the GPT-3 model to assist users with their queries and provide helpful information.



## Tech Stack



The project is built using the following technologies:



- Backend: Python with Fast API as the web framework

- Database: CockroachDB (Serverless Cloud Instance)

- Frontend: React for the user interface

- Live Streaming Platform: Vimeo for broadcasting events

- Authentication: Auth0 for secure user access

- Prediction Model: Sklearn's Gradient Boosting Classifier

- Sentiment Analysis Model: Hugging Face's pretrained roBERTa model (cardiffnlp/twitter-roberta-base-sentiment)



## Environment Files Setup



### The steps to add the environment files and the root.crt file are as follows:



- api/auth/.env: Create a file named .env inside the api/auth/ directory. Add the necessary configuration variables for the API and Auth0 in this file.



- streaming/.env: Create a file named .env inside the streaming/ directory. Add the required configurations for the Vimeo live streaming platform in this file.



- .env: Create a file named .env in the project's root directory. Add the DB_USERNAME & PASSWORD.



- models/server.env: Create a file named server.env inside the models/ directory. Add the specific environment variables required for the predictive and sentiment analysis models.



- CockroachDB Signing Key (root.crt): Place the root.crt file inside the certs/ directory. This ensures secure communication with the CockroachDB.



- Please ensure that you add all the necessary configurations to these environment files to avoid any issues while running the Geek-Olympic WebApp.



## Achievements



Throughout the development process, we achieved notable results:



- The predictive model using Sklearn's Gradient Boosting Classifier achieved an accuracy of approximately 45% with both an f1-score and precision score of 0.53.



- The sentiment analysis model using roBERTa achieved an overall accuracy of around 67%, an average f1 score of 0.635, an average recall score of 0.652, and an average precision score of 0.671.



# Video



https://github.com/Cosmicoppai/Geek-a-Thon/assets/63765823/46c827ba-fb7c-48e7-ad41-0028a891f90f



## ScreenShots



![Home Page](./screenshots/home-page.png)

![Stats & Analysis Page](./screenshots/stats-analysis.png)

![Olympic Rings](./screenshots/chart-1.png)

![Olympic Rings](./screenshots/chart-5.png)

![Olympic Rings](./screenshots/events.png)

![Olympic Rings](./screenshots/stream.png)

![Olympic Rings](./screenshots/chatbot.png)



For more screenshots, [click here](./screenshots/).

## Run

- Backend

```

pip install -r ./requirements.txt

py main.py

```

- Frontend

```

npm i

npm start

```



## Acknowledgments



We would like to express our gratitude to the organizers of the Geek-Olympic Hackathon for providing us with this opportunity to work on such an exciting project.



Thank you for exploring our Geek-Olympic WebApp! We hope you enjoy the enhanced Olympics experience it provides. Happy hacking and happy Olympics! 🏅🎉