Job Recommender System With
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Описание
You said: how to activate uv env ChatGPT said: uv is a fast Python package manager and virtual environment tool (alternative to pip + venv). To activate a uv environment, follow these steps: Step 1: Create a virtual environment using uv Open terminal in
README
Upload your resume and get AI-powered job recommendations, skill gap analysis, and a personalized career roadmap --- powered by Groq LLaMA 3.3-70B and real-time job listings from LinkedIn & Naukri via Apify.
🔗 Live Demo:
https://chitaki10-job-recommender-system-with-mcp-app-eynbly.streamlit.app
Table of contents
- Features
- Architecture
- Tech Stack
- Getting Started
- API Keys Setup
- MCP Tool Server
- Project Structure
- Results
- License
Features
- Resume Parsing --- Extracts raw text from uploaded PDF résumés
using PyMuPDF\
- Resume Summary --- LLM summarizes your skills, experience, and
profile in seconds\
- Skill Gap Analysis --- Identifies missing skills, certifications,
and areas to improve\
- Career Roadmap --- Personalized 6--12 month plan: skills to
learn, projects to build, certs to pursue\
- Real-Time Job Recommendations --- Fetches live jobs from LinkedIn
and Naukri via Apify API\
- MCP Tool Server --- Core functions exposed as MCP tools
Architecture
user uploads Résumé (PDF)
│
▼
Extract PDF Text (PyMuPDF)
│
▼
Groq LLaMA 3.3-70B (LLM)
│
├── Summary
├── Skill Gap
├── Career Roadmap
└── Job Keywords
│
├──────────────┬───────────────┐
▼ ▼
LinkedIn Jobs Naukri Jobs
(Apify Actor) (Apify Actor)
│
└──────────────┬───────────────┘
▼
Job Recommendations UI
Tech Stack
Component Technology
UI Streamlit LLM Groq API --- llama-3.3-70b-versatile PDF Parsing PyMuPDF Job Scraping Apify MCP Server mcp Python SDK Package Manager uv Deployment Streamlit Cloud
Getting Started
clone Repository
git clone https://github.com/chitaki10/Job_Recommender_System_with_MCP.git
cd Job_Recommender_System_with_MCP
Install Dependencies
uv venv
uv add -r requirements.txt
Create .env file
GROQ_API_KEY=your_groq_api_key_here
APIFY_API_TOKEN=your_apify_api_token_here
Run App
uv run streamlit run app.py
MCP Tool Server
Run MCP server:
uv run python mcp_server.py
Test with inspector:
npx @modelcontextprotocol/inspector uv run python mcp_server.py
Available tools:
- extract_text_from_pdf
- ask_groq
- fetch_linkedin_jobs
- fetch_naukri_jobs
Project Structure
Job_Recommender_System_with_MCP/
│
├── app.py
├── mcp_server.py
├── requirements.txt
├── pyproject.toml
├── .env
├── .gitignore
│
└── src/
├── __init__.py
└── helper.py
Results
- Resume summary generated in seconds\
- Skill gap analysis provided\
- Career roadmap generated\
- 120 real-time jobs fetched
Contact
Установка Job Recommender System With
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/chitaki10/Job_Recommender_System_with_MCPFAQ
Job Recommender System With MCP бесплатный?
Да, Job Recommender System With MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Job Recommender System With?
Нет, Job Recommender System With работает без API-ключей и переменных окружения.
Job Recommender System With — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Job Recommender System With в Claude Desktop, Claude Code или Cursor?
Открой Job Recommender System With на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
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