Project: Creative Apps - PathPilot AI Career Reality Simulator

[xenon1919]

Track

Creative Apps (GitHub Copilot)

Project Name

PathPilot – AI Career Reality Simulator

GitHub Username

@xenon1919

Repository URL

https://github.com/xenon1919/PathPilot-AI-Career-Reality-Simulator

Project Description

PathPilot is an AI-powered career simulation tool that transforms career confusion into structured decision insight.

Many students and professionals struggle with choosing between multiple interests, fear of making the wrong decision, and uncertainty about long-term growth. PathPilot addresses this by generating three realistic career futures based on user input, including skills, interests, and risk tolerance.

For each career path, the system provides:

A 5-step actionable roadmap

Key skill requirements

Salary range (INR-based)

A “day in the life” simulation

Compatibility score

Regret risk indicator

Growth confidence level

The app also includes a visual compatibility comparison chart to help users evaluate trade-offs clearly.

GitHub Copilot was used extensively to refine prompt engineering, structure JSON output, implement Streamlit UI layouts, design scoring logic, and improve error handling.

PathPilot demonstrates how AI-assisted development can create interactive, emotionally intelligent, and data-driven decision-support tools.

Demo Video or Screenshots

Screenshots: https://github.com/xenon1919/PathPilot-AI-Career-Reality-Simulator/blob/main/demo.png

Primary Programming Language

Python

Key Technologies Used

• Python
• Streamlit
• OpenRouter API
• Matplotlib
• GitHub Copilot
• python-dotenv

Submission Type

Individual

Team Members

No response

Submission Requirements

• My project meets the track-specific challenge requirements
• My repository includes a comprehensive README.md with setup instructions
• My code does not contain hardcoded API keys or secrets
• I have included demo materials (video or screenshots)
• My project is my own work with proper attribution for any third-party code
• I agree to the Code of Conduct
• I have read and agree to the Disclaimer
• My submission does NOT contain any confidential, proprietary, or sensitive information
• I confirm I have the rights to submit this content and grant the necessary licenses

Quick Setup Summary

1. Clone the repository
2. Create a virtual environment
3. Install dependencies:
   pip install -r requirements.txt
4. Add your API key to a .env file:
   OPENAI_API_KEY=your_key_here
5. Run the app:
   streamlit run app.py

Technical Highlights

• Designed structured JSON-based AI response architecture for reliable parsing
• Implemented compatibility scoring and regret risk evaluation logic
• Built dynamic three-column career comparison layout using Streamlit
• Added visual compatibility comparison using Matplotlib
• Integrated GitHub Copilot for prompt optimization, UI refinement, and debugging
• Applied structured error handling for API and JSON parsing stability

Challenges & Learnings

One of the biggest challenges was ensuring reliable structured output from the language model. Early responses returned unstructured text, which caused parsing errors.

To solve this, I redesigned the prompt to enforce strict JSON formatting and implemented defensive parsing with error handling. This significantly improved stability.

Another key learning was how GitHub Copilot accelerates structured development workflows, especially in prompt refinement and UI scaffolding. It acted as an intelligent pair programmer throughout the project.

Contact Information

https://www.linkedin.com/in/rishisaiteja

Country/Region

India