A sleek, full-stack platform designed to boost job-seekers’ chances by integrating resume parsing, ATS scoring, job-matching and cover-letter generation — all powered by modern LLMs and vector-search under the hood.

What it does

• Parses resumes (PDF/DOCX/TXT) including OCR for scanned documents.

• Scores resumes using an ATS-style 100-point system with actionable feedback.

• Matches a candidate’s resume to job descriptions, providing compatibility insights.

• Generates personalized cover letters and resumes from scratch or via templates.

• Offers job-search integration, skill-gap analysis and other productivity modules.

• Built as a multi-page UI (Streamlit & Gradio), deployable via Docker, cloud or local.

Why it's useful
In today’s competitive job market, standing out depends not just on experience but on how your materials are presented and aligned with each role. job-snipper helps automate and elevate that process: offering data-driven feedback, AI-enhanced matching and document generation — saving time and improving match quality.

Tech stack at a glance

• Front-end: Streamlit 1.50+, Python 3.13+

• Models & AI: Google Gemini, Groq, OpenAI, Anthropic via LangChain

• Data-processing: pandas, numpy; Visualization: seaborn, matplotlib, plotly

• Document handling: PyPDF2, python-docx, pdfplumber; OCR via Mistral & Tesseract

• Storage & Deployment: ChromaDB vector store; Docker / Docker Compose; Cloud friendly

• Best practices: Local environment variables for keys, robust security and testing in place.

Getting started

1. Clone this repo: git clone https://github.com/KunjShah95/job-snipper.git

2. Create a virtual environment (e.g., python3 -m venv .venv → source .venv/bin/activate)

3. Install dependencies: pip install -r requirements.txt

4. Copy .env.example → .env, add your API keys (Groq recommended, or Gemini, OpenAI)

5. Run the app: streamlit run Home.py and open http://localhost:8501

Who should use it / contribute

• Job-seekers looking for smarter ways to craft or refine their resumes and cover letters.

• Career coaches, HR professionals, or recruiters needing tools to analyse and match applicant documents.

• Developers or open-source contributors who want to extend AI workflows, add new modules (e.g., multi-language support), or improve the matching and scoring algorithms.

Future vision
Planned enhancements include multi-language support, mobile native apps, integration with LinkedIn and major job boards, enhanced career-path recommendations, video resumes and more.