🐱 CareerPilot AI

CareerPilot AI is an AI-powered resume analysis and career discovery platform that helps candidates understand where their resume fits, evaluate ATS readiness, identify skill gaps, and compare their profile against a specific job description.

Resume analysis without the corporate headache.

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Live Application

Frontend:
https://careerpilot-ai-vijitha.streamlit.app

Backend:
Deployed using Render.

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Features

Career Discovery

Upload a resume without providing a job description and CareerPilot AI analyzes the candidate's profile to:

• Identify suitable career paths
• Generate career-fit scores
• Explain why each career matches
• Highlight missing skills
• Recommend the strongest career fit
• Generate an AI-based candidate profile and verdict

ATS Resume Analysis

CareerPilot evaluates the overall health of the resume, including:

• ATS score
• Resume length
• Contact information detection
• LinkedIn and GitHub detection
• Resume section detection
• Action-oriented writing
• Quantified achievements
• Missing or weak sections

Job Description Matching

Users can optionally paste a job description to compare it against their resume.

The system analyzes:

• Resume-to-job compatibility
• Relevant skills
• Missing skills
• Candidate strengths
• Skill gaps
• Areas requiring improvement

Resume Improvement Suggestions

CareerPilot provides actionable recommendations to improve the resume based on detected weaknesses and AI analysis.

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Tech Stack

Frontend

• Streamlit
• Python

Backend

• FastAPI
• Uvicorn
• Python

AI

• Groq API
• Large Language Models (LLMs)

Resume Processing

• PyMuPDF
• PDF text extraction and parsing

Communication

• REST API
• Requests

Deployment

• Streamlit Community Cloud — Frontend
• Render — FastAPI Backend

Version Control

• Git
• GitHub

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System Architecture

User
 │
 ▼
Streamlit Frontend
 │
 │ Resume PDF + Optional Job Description
 ▼
FastAPI Backend
 │
 ├── Resume Parser
 │      │
 │      └── Extract Resume Text
 │
 ├── ATS Analysis
 │
 ├── Evidence Analysis
 │
 ├── Career / Job Analysis
 │
 └── Groq AI Service
        │
        ▼
     LLM Analysis
        │
        ▼
Structured Results
 │
 ▼
Streamlit Dashboard

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Project Structure

CareerPilot-AI/
│
├── backend/
│   ├── services/
│   │   ├── ats_service.py
│   │   ├── evidence_service.py
│   │   ├── groq_service.py
│   │   ├── job_service.py
│   │   └── parser_service.py
│   │
│   ├── main.py
│   ├── database/
│   ├── models/
│   ├── prompts/
│   └── routes/
│
├── frontend/
│   ├── assets/
│   │   └── cat_img.jpeg
│   ├── app.py
│   ├── components/
│   └── pages/
│
├── data/
│
├── tests/
│
├── .gitignore
├── requirements.txt
└── README.md

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⚙️ How It Works

1. Upload Resume

The user uploads a resume in PDF format through the Streamlit interface.

2. Extract Resume Content

The PDF is sent to the FastAPI backend where PyMuPDF extracts and processes the resume text.

3. Analyze Candidate Profile

CareerPilot analyzes the candidate's:

• Skills
• Projects
• Education
• Experience
• Resume structure
• Career evidence

4. AI Analysis

Extracted resume information is processed using the Groq API to generate career recommendations and contextual feedback.

5. ATS Evaluation

The system evaluates resume quality and identifies missing sections, weak areas, and opportunities for improvement.

6. Display Results

The structured analysis is returned to the Streamlit frontend and displayed as an interactive CareerPilot Analysis dashboard.

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Running Locally

1. Clone the repository

git clone https://github.com/Vijitha14/CareerPilot-AI.git
cd CareerPilot-AI

2. Create a virtual environment

python -m venv venv

Activate it on Windows:

venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Create a .env file in the project root:

GROQ_API_KEY=your_groq_api_key

Never commit your .env file or API keys to GitHub.

5. Start the FastAPI backend

uvicorn backend.main:app --reload

Backend runs locally at:

http://127.0.0.1:8000

6. Start the Streamlit frontend

Open another terminal:

streamlit run frontend/app.py

The application will normally open at:

http://localhost:8501

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Environment Variables

CareerPilot requires:

GROQ_API_KEY=your_groq_api_key

For production deployment, environment variables should be configured through the deployment platform rather than committed to the repository.

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☁️ Deployment

CareerPilot uses separate frontend and backend deployments.

Streamlit Community Cloud
        │
        │ HTTPS API Request
        ▼
Render
FastAPI Backend
        │
        ▼
Groq API

The Streamlit frontend communicates with the deployed FastAPI REST API hosted on Render.

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🔮 Future Improvements

• User authentication
• Resume history
• Multiple resume comparison
• Improved ATS keyword matching
• Resume rewriting suggestions
• Downloadable analysis reports
• Career roadmap generation
• Job recommendation integration
• Resume version tracking

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⚠️ Disclaimer

CareerPilot AI provides AI-assisted resume and career analysis. Scores and recommendations should be treated as guidance rather than guarantees of hiring outcomes or ATS performance.

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Author

Glory Vijitha

Computer Science & Engineering

GitHub: Vijitha14

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⭐ Support

If you find CareerPilot AI useful, consider giving the repository a ⭐ on GitHub.