🚀 AI Career Copilot

Full-stack platform for AI-driven resume intelligence, career coaching, and job-market insights

AI Career Copilot combines a FastAPI backend with a Next.js frontend to deliver resume rewriting, skill-gap analysis, role-fit scoring, career coaching, job alerts, and progress tracking powered by Google Gemini. The project now supports a multi-step analyzer wizard, persistent dashboards, and commercial licensing.

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📦 Monorepo Structure

.
├── backend/
│   ├── src/
│   │   ├── api/api.py            # FastAPI application with 20+ endpoints
│   │   ├── models/gemini.py      # Gemini model wrappers
│   │   └── utils/prompts.py      # Prompt builders for all features
│   ├── requirements.txt
│   └── tests/
│       ├── test_api.py
│       ├── test_prompts.py
│       └── ...
├── frontend/
│   └── smart-ats-ui/
│       ├── app/
│       │   ├── dashboard/
│       │   │   ├── page.tsx          # Dashboard home
│       │   │   ├── analyzer/page.tsx # 5-step wizard (new)
│       │   │   ├── analyze/page.tsx  # Legacy single-page analyzer
│       │   │   ├── coach/page.tsx    # Career coach chat
│       │   │   └── jobs/page.tsx     # Job alerts & tracker
│       │   └── page.tsx              # Landing page
│       ├── components/
│       │   ├── analyzer/
│       │   ├── dashboard/
│       │   ├── jobs/
│       │   └── visualizations/
│       ├── store/useCareerCopilotStore.ts  # Zustand persistence
│       ├── package.json
│       └── pnpm-lock.yaml
├── LICENSE-commercial
├── README.md (this file)
└── .gitignore

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✨ Feature Overview

Backend API (FastAPI)

• 20+ endpoints covering resume intelligence, coaching, job insights, analytics, and progress tracking
• Google Gemini integration with structured prompt outputs
• PDF parsing, ATS simulation, orchestration planning, knowledge graph prompts, and more
• Test suite with pytest + httpx

Frontend App (Next.js + shadcn/ui)

• Analyzer Wizard: 5-step guided flow with resume upload, job description, module selection, progress, and results
• Results Dashboard: Tabbed interface with Recharts visualizations (skill heatmap, keyword cloud, radar)
• Career Coach: Persistent chat with suggested prompts and resume context
• Job Search Hub: AI job alerts, application tracker, saved searches
• Dashboard Home: Quick actions, live stats, recent activity, progress tracker
• Zustand store for cross-page persistence (analysis history, job data, coach conversation)

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🔐 Licensing

• The project ships with a commercial license (LICENSE-commercial).
• Contact the maintainers to obtain rights for redistribution or resale.
• Open-source dependencies retain their respective licenses.

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⚙️ Environment Setup

Backend (FastAPI)

cd backend
python3 -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Configure environment
cp .env.example .env            # create if needed
echo "GOOGLE_API_KEY=your_key" >> .env

# Run API
uvicorn src.api.api:app --reload

The API serves Swagger docs at http://localhost:8000/docs and ReDoc at /redoc.

Frontend (Next.js)

cd frontend/smart-ats-ui
pnpm install

# Set API base URL
echo "NEXT_PUBLIC_API_BASE_URL=http://localhost:8000" >> .env.local

# Start dev server
pnpm dev

Visit http://localhost:3000 for the landing page and /dashboard for the app.

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📊 Analyzer Wizard Flow

1. Upload – PDF drag/drop and/or paste resume text
2. Job Description – paste full JD (min length validation)
3. Pick Analyses – select modules or use quick picks
4. Processing – live progress bar and status updates
5. Results – tabbed insights, overall score badge, before/after comparison, visualizations

Results persist to the dashboard via the Zustand store (useCareerCopilotStore).

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📈 Dashboard Metrics & Persistence

frontend/smart-ats-ui/store/useCareerCopilotStore.ts stores:

• Aggregate stats (total analyses, average match, skills improved)
• Recent analyses (for dashboard activity feed)
• Job applications + saved searches (for job tracker)
• Career coach conversation history

State persists in localStorage so browser refreshes keep context.

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🧪 Testing

Backend

cd backend
source venv/bin/activate
python -m pytest

Frontend

Add React Testing Library / Playwright suites as needed. Current focus is on end-to-end flows once backend endpoints are stable.

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♻️ Git Workflow & Ignore Rules

.gitignore has been updated to exclude:

• Python venvs (venv/, .venv/, etc.)
• Next.js build artifacts (.next/, node_modules/)
• Environment files (backend/.env, frontend/.env.local)
• Logs, coverage reports, temporary assets

Before committing:

git status

Ensure only relevant source changes are staged.

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🚀 Deployment Notes

• Backend can be containerized with Uvicorn/Gunicorn for cloud deployment (AWS, GCP, Azure).
• Frontend supports Next.js static export or server rendering. Configure NEXT_PUBLIC_API_BASE_URL accordingly.
• Add CI/CD pipelines to run backend tests and lint frontend before deploys.

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📄 Legal

• Commercial usage governed by LICENSE-commercial.
• Evaluate third-party API terms (Google Gemini) before production use.
• Do not commit API keys or customer data.

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🤝 Contributing

1. Fork the repository
2. Create your feature branch: git checkout -b feature/awesome
3. Commit changes with context-rich messages
4. Run backend tests and lint frontend
5. Open a pull request detailing changes and testing steps

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📞 Support

For licensing, enterprise support, or feature requests, contact the maintainers with project context and timelines.

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Enjoy building with AI Career Copilot! 🌟