CV Analyzer

This project features an AI-powered ATS Parsing Engine designed to automate candidate screening for recruitment teams. Built entirely in Python using the Streamlit framework, the application replaces old-school keyword searching with deep semantic text extraction. When a recruiter uploads an applicant's resume in PDF format, the backend pipeline reads the document and passes the unstructured raw text directly to Google’s Gemini 2.5 Flash Large Language Model (LLM) via its cloud API. Using advanced Natural Language Processing (NLP), the AI understands the context of the resume, automatically classifying messy text blocks into structural parameters like contact info, candidate name, full educational timeline, and professional history. The system then outputs this data into a structured machine-readable JSON object. To make hiring efficient, the portal includes an interactive tracking panel in the sidebar featuring a dynamic multi-select dropdown menu pre-configured with over 130 popular industry tech skills. Recruiters can quickly choose or type mandatory criteria. The system’s logic automatically maps these filters against the candidate’s AI-extracted skill graph to instantly deliver a visual qualification status—marking profiles as Criteria Matched, Partial Match, or Criteria Breached alongside a clear percentage score. Furthermore, the application is engineered with robust exception handling loops that automatically manage temporary API rate-limits and busy server spikes to maintain pipeline stability.