CVSift

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  Landing page

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  After uploading my resume

Inspiration

As current CS students, we've heard a lot about the enormous hassle the recruiting process is, especially when recruiters receive thousands of resumes to sift through. To help ease this time-consuming process, we decided to build CVSift, a tool recruiters can use to get quick, concise summaries for their applicants' resumes, helping speed up the entire process.

What it does

CVSift extracts the text from uploaded applicant resumes, and utilizes the GPT 3.5 Turbo LLM model to create comprehensive summaries for the applicants. Finally, it displays these summaries to the end user, the recruiters/hiring managers.

How we built it

We used Python, Python's Flask web framework, HTML, CSS, JavaScript, a few Python libraries, and the OpenAI API.

Challenges we ran into

As beginners, it was a challenge to figure out how to build a web app that calls an API and returns a response in a user-friendly and efficient way.

Accomplishments that we're proud of

We are proud of how much we learnt about the various technologies involved in this project, and about how we overcame difficulties to collectively work together as a team efficiently.

What we learned

We learnt a lot about how to build fast web apps, technologies like JavaScript, HTML, CSS, and more Python, and how to call APIs and get useful responses.

What's next for CVSift

The next step is to get this product in the hands of recruiters and get user feedback, and keep iterating new versions with new features based on the responses. This tool has potential to simplify and streamline the entire recruitment/resume screening process, and so it would be great to keep getting feedback and keep building this product out.

Built With

• css
• flask
• html
• javascript
• python