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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