Career Conquer at DA-IICT Gandhinagar

Project overview

Career Conquer

An AI-assisted career-guidance platform for resume analysis, career roadmaps, skill assessment, and conversational guidance.

Live Demo · Author

The repository name currently uses “Carrier-Conquer”; the product is documented here as “Career Conquer” because it focuses on career development.

What It Does

Career Conquer helps students and job seekers understand their resumes, explore role-specific learning paths, evaluate skills, and ask career questions from one interface.

Implemented Features

  • AI-assisted resume analysis
  • PDF, DOCX, TXT, RTF, PNG, JPG, and JPEG input support
  • Text extraction with image fallback for scanned documents
  • User-supplied analysis prompts
  • File-type, empty-file, prompt-length, and upload-size validation
  • Career-guidance chatbot page
  • Role roadmap and skill-assessment pages
  • Structured logging and user-friendly error handling
  • Separate experimental Next.js interface under carrier-compass/

Resume Analysis Flow

flowchart LR
    A["Resume Upload"] --> B{"File Type"}
    B --> C["Text Extraction"]
    B --> D["Image Processing"]
    C --> E["Gemini Analysis"]
    D --> E
    E --> F["Actionable Feedback"]

Technology Stack

Flask application

  • Python and Flask
  • Google Gemini
  • PyPDF2 and pdf2image
  • python-docx and striprtf
  • Pillow
  • Jinja templates
  • Gunicorn

Experimental Next.js interface

  • Next.js 16
  • React 19 and TypeScript
  • MongoDB with Mongoose
  • Tailwind CSS
  • Three.js / React Three Fiber

Getting Started

git clone https://github.com/uvpatel/Carrier-Conquer.git
cd Carrier-Conquer
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

On Windows:

.venv\Scripts\activate

Create an environment variable for the AI provider:

GEMINI_API_KEY=
FLASK_SECRET_KEY=

Run the Flask application:

python app.py

Open http://127.0.0.1:5000.

Privacy and Security

Resumes contain sensitive personal information.

  • Never commit API keys or secrets.
  • Revoke any credential that has previously appeared in source history.
  • Load credentials only from environment variables.
  • Avoid retaining uploaded resumes unless users explicitly consent.
  • Use malware scanning and stronger file validation in production.
  • Add authentication, rate limiting, secure cookies, CSRF protection, and a privacy policy before public production use.

Current Limitations

  • AI output can be incomplete or inaccurate and should be reviewed by the user.
  • The Flask and Next.js implementations are not yet unified into one production architecture.
  • The system does not guarantee employment outcomes or ATS performance.
  • Provider availability and model behaviour can change.

Roadmap

  • Move all secrets to environment configuration
  • Unify the Flask service and Next.js product
  • Add user accounts and saved analysis history
  • Add structured ATS scoring with explainable criteria
  • Add tests for file processing and API routes
  • Add Docker, CI, monitoring, and privacy controls

License

See LICENSE.

Author

Built by Urvil Patel.

Build with love by Urvil Patel