perlathebian/job-application-assistant
AI-powered job application assistant that analyzes job descriptions, parses resumes, calculates semantic match scores, and generates personalized cover letters using NLP and LLMs. Full-stack ML application with FastAPI backend, Streamlit frontend, 85% test coverage, and Docker deployment.
What's novel
AI-powered job application assistant that analyzes job descriptions, parses resumes, calculates semantic match scores, and generates personalized cover letters using NLP and LLMs. Full-stack ML application with FastAPI backend, Streamlit frontend, 85% test coverage, and Docker deployment.
Code Analysis
17 files read · 7 roundsA Streamlit-based application that uses a FastAPI backend to parse resumes, semantically match them against job descriptions using embeddings, and generate AI-powered cover letters.
Strengths
Clean separation of concerns between frontend (Streamlit) and backend (FastAPI). Good use of Pydantic schemas for type safety. The architecture is straightforward and easy to understand.
Weaknesses
Missing core service implementations (semantic_matcher.py, letter_generator.py, resume_parser.py) in the provided files, suggesting incomplete code or missing dependencies. Limited error handling details visible in endpoints. No tests observed.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
72
Contributors
1
Files
71
Active weeks
4
Repository
Language
Python
Stars
1
Forks
0
License
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