Inspiration

In today's competitive job market, both applicants and recruiters face challenges—applicants struggle to create impactful resumes, while recruiters sift through hundreds to find the right fit. This inspired us to build SmartResume Pro, a one-stop platform to build, parse, and analyze resumes intelligently using NLP and automation.

What it does

SmartResume Pro allows users to create clean, ATS-friendly resumes using pre-built templates and then analyzes uploaded resumes to extract key skills, education, experience, and more. It also offers visual insights and keyword recommendations, helping job seekers and recruiters alike.

How we built it

We used Streamlit for the front-end interface, integrated FPDF for generating resumes, and used PyPDF2, nltk, and regex for text extraction and analysis. The backend logic was developed in Python, and we used pandas and scikit-learn for data handling and ML-powered analysis.

Challenges we ran into

Parsing text from various PDF formats

Extracting relevant information like skills and experience accurately

Designing resume templates that look good and are ATS-friendly

Ensuring the UI was responsive and user-friendly

Accomplishments that we're proud of

Built an end-to-end working resume builder and analyzer

Automated keyword extraction and job-fit analysis

Designed intuitive resume templates from scratch

Learned and implemented real-world NLP techniques

What we learned

We learned how to integrate multiple Python libraries into a seamless app, manage user flow, and apply NLP and data science concepts in a practical setting. We also gained experience in debugging parsing errors and improving accuracy through iteration.

What's next for SmartResume Pro

Add login/authentication to save resumes for future editing

Expand templates and design customization options

Integrate AI to match resumes with job descriptions

Deploy the platform and make it publicly accessible

Built With

  • flask-libraries:-pypdf2
  • fpdf
  • languages:-python-frameworks:-streamlit
  • nltk
  • pandas
  • regex-version-control:-git
  • scikit-learn
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