## Inspiration

The inspiration behind CareerFairy stemmed from a desire to transform traditional career fairs into a more accessible and efficient virtual experience. We aimed to leverage technology to streamline the recruitment process, making it easier for both job seekers and employers to connect in a 2D spatial calling web application.

## What it does

CareerFairy is a revolutionary platform that facilitates virtual career fairs, allowing job seekers to explore booths and engage with recruiters seamlessly. Utilizing React, Flask, PeerJS, WebRTC, SocketIO, and OpenAI's GPT-3.5, the application not only provides a user-friendly interface but also incorporates AI-generated candidate overviews for recruiters, optimizing the hiring process.

## How we built it

We built CareerFairy using React for the front end, Flask for the backend, and integrated PeerJS, WebRTC, and SocketIO for real-time communication. The inclusion of OpenAI's GPT-3.5 ensures that recruiters receive instant, AI-generated insights into candidate qualifications and skills.

## Challenges we ran into

Integrating multiple technologies posed challenges in terms of synchronization and seamless communication. Overcoming the learning curve associated with WebRTC and ensuring a smooth user experience were additional hurdles that our team successfully addressed during the development process.

## Accomplishments that we're proud of

We are proud to have created a functional and innovative platform that enhances the virtual recruitment experience. The successful integration of AI-driven candidate overviews and the collaborative efforts of our team in overcoming technical challenges stand out as notable accomplishments.

## What we learned

Throughout the development of CareerFairy, our team gained valuable insights into the intricacies of real-time communication, virtual environments, and the application of AI in recruitment processes. We refined our skills in React, Flask, PeerJS, WebRTC, and SocketIO, further enhancing our capabilities.

## What's next for Fairy

Looking ahead, CareerFairy aims to expand its features and capabilities. We plan to refine the AI models for more nuanced candidate assessments, incorporate additional interactive elements for job seekers and recruiters, and continually enhance the overall user experience. Our vision is to make CareerFairy the go-to platform for efficient, AI-driven virtual recruitment.
## Inspiration

An abundance of qualified applicants lose their chance to secure their dream job simply because they are unable to effectively present their knowledge and skills when it comes to the interview. The transformation of interviews into the virtual format due to the Covid-19 pandemic has created many challenges for the applicants, especially students as they have reduced access to in-person resources where they could develop their interview skills.

## What it does

Interviewy is an **Artificial Intelligence** based interface that allows users to practice their interview skills by providing them an analysis of their video recorded interview based on their selected interview question. Users can reflect on their confidence levels and covered topics by selecting a specific time-stamp in their report.

## How we built it

This Interface was built using the MERN stack
In the backend we used the AssemblyAI APIs for monitoring the confidence levels and covered topics. The frontend used react components.

## Challenges we ran into

* Learning to work with AssemblyAI
* Storing files and sending them over an API
* Managing large amounts of data given from an API
* Organizing the API code structure in a proper way

## Accomplishments that we're proud of

• Creating a streamlined Artificial Intelligence process
• Team perseverance

## What we learned

• Learning to work with AssemblyAI, Express.js
• The hardest solution is not always the best solution

## What's next for Interviewy

• Currently the confidence levels are measured through analyzing the words used during the interview. The next milestone of this project would be to analyze the alterations in tone of the interviewees in order to provide a more accurate feedback.
• Creating an API for analyzing the video and the gestures of the the interviewees
## Inspiration

As students, we know navigating a career path is often confusing and overwhelming, especially when geographical location, access to resources, and personal background create additional barriers. We were inspired by the challenges that many people face when trying to advance in their careers, particularly those in underrepresented regions or communities. We wanted to create a tool that could help anyone, regardless of where they come from, gain clarity on their career goals and how to achieve them. The idea was to harness the power of AI to provide personalized, actionable career advice that takes into account an individual’s unique context—be it their location, skills, education, or interests. This idea was mainly inspired by reading various SDGs such as "8. Decent Work and Economic Growth" and "Quality Education".

## What it does

Our platform helps users by generating personalized career paths based on key inputs such as current location, skills, education, and professional interests. The AI model processes this information and provides users with a 5 step plan for their desired career, outlining the skills they should develop, and various resources to help them. Additionally, the platform adjusts recommendations based on the user's location to ensure that they are both relevant and realistic.

## How we built it

Frontend: We used React to build a responsive and user-friendly interface. The frontend allows users to easily input their data and interact with the platform.
Backend: The backend was built using Node.js and frameworks such as Express.js to handle the API requests, user data processing, and communication with the AI model.
Database: We implemented PostgreSQL to store user data, including their inputted information and the career paths generated by the AI model.
AI Model: We used the llama-8b-8192 model for our career advice generation purposes. The model was personalized to analyze user inputs and generate personalized career recommendations based on the factors we gave it.
Development Tools: Kaggle was used to fine-tune the AI model, while Postman was used to test API and form requests.

## Challenges we ran into

* The main challenge we ran into was fine-tuning our AI model with a custom data set as none of us had any previous ML experience and had to learn from scratch this weekend by following various tutorials.
* Another challenge we experienced was handling various errors in our backend with our login and registration. We tackled this by using JWT's to simplify our login/logout process and express-session to handle the session of the user.

## Accomplishments that we're proud of

We’re incredibly proud of the fact that we successfully built a working website capable of generating career paths that adapt to users’ unique circumstances. We believe the ability to factor in both geographical and personal elements to offer tailored advice sets our project apart. Additionally, we are proud of the smooth integration between the AI model, frontend, and backend, which ensures a cohesive and responsive user experience.

## What we learned

Throughout the development process, we gained significant experience in training and fine-tuning AI models. We also learned the importance of refining data inputs and how they affect AI output, particularly when trying to achieve a high degree of personalization. Collaboratively, our team improved our problem-solving skills, learning to overcome technical challenges related to AI integration, backend efficiency, and frontend responsiveness.

## What's next for CareerPathAI

We would say the next main steps for CareerPathAI would be how to web scrape from various sources to get better data for our model, and also how to properly fine-tune/train an AI model for better personalization to help more people.