Inspiration

Job seekers often struggle with optimizing their resumes for ATS (Applicant Tracking Systems) and human recruiters. We wanted to create an AI-powered tool to streamline this process by providing instant feedback and actionable suggestions.

What it does

Software AI Resume Critic is a Streamlit-based web application that analyzes resumes, detects errors, and suggests improvements. It evaluates grammar, structure, job relevance, and formatting while checking for missing keywords that align with the job description. The tool uses the Gemini 2.0 Flash (Experimental) API for intelligent analysis.

How we built it

Frontend: Streamlit for an interactive user interface
Backend: Python for text extraction and processing
AI Integration: Google’s Gemini 2.0 Flash API for resume analysis
File Handling: PDF upload and text extraction for processing

Challenges we ran into

Fine-tuning AI prompts for accurate and constructive feedback
Extracting and formatting text from different resume templates
Ensuring a smooth and intuitive user experience in Streamlit

Accomplishments that we're proud of

Successfully integrating Gemini 2.0 Flash for AI-driven resume evaluation
Creating an easy-to-use interface for job seekers
Providing actionable, real-time feedback to improve resumes

What we learned

How to leverage LLMs (Large Language Models) for document analysis
Enhancing prompt engineering to refine AI-generated suggestions
Working with Streamlit to quickly prototype AI applications

What's next for Software AI Resume Critic

ATS Score Prediction: Estimate how well a resume performs in applicant tracking systems
Resume Formatting Suggestions: Provide guidance on layout and structure
Job-Specific Optimization: Tailor resumes to specific industries and roles
Expanded AI Models: Experiment with other LLMs for comparison