A Streamlit-based web app that ingests PDF or text job descriptions, surfaces key role insights, flags potential red-flags, suggests salary benchmarks, and provides an AI-driven interview-prep chatbot.
- Job Description Parser
Upload a PDF or plain text Job Description and automatically extract role, required skills and responsibilities. - AI-Driven Insights
Uses Google Gemini API to detect red-flags, recommend salary ranges and highlight missing skills. - Interview Prep Chatbot
Interactive Q&A assistant offering targeted mock-interview questions, company research tips and career guidance.
- Language & Framework: Python, Streamlit
- AI / NLP: Google Gemini API
- PDF Parsing: PyPDF2 (or pdfplumber)
- Frontend: Streamlit Components, HTML/CSS
- Hosting: (optional) Streamlit Community Cloud or your choice of cloud provider
- Clone the repository
git clone https://github.com/Aaditya514/Rojgaar_Connect.git
cd Rojgaar_Connect
- Create & activate a virtual environment
python3 -m venv venv
source venv/bin/activate # macOS/Linux
venv\Scripts\activate # Windows
- Install dependencies
pip install -r requirements.txt
- Configure your Google Gemini API key
- Option A: Add directly in
app.py - Option B: Set as an environment variable
export GEMINI_API_KEY="your_api_key_here" # macOS/Linux set GEMINI_API_KEY="your_api_key_here" # Windows
streamlit run app.py
- Open your browser at
http://localhost:8501/ - Upload a job description (PDF or text)
- Explore AI-powered insights and chat with the interview-prep assistant