"Surgically extract skill gaps and build your path to mastery." CodeForge AI uses Google Gemini 2.0 Flash to bridge the gap between your resume and your dream job with a DAG-powered adaptive roadmap.
Most career platforms offer generic advice.
- Resumes are often misaligned with modern Job Descriptions (JDs).
- Learning paths lack structure, leading to "tutorial hell."
- Skill gaps are invisible until you fail an interview.
CodeForge AI provides a precision instrument for career growth:
- Semantic Gap Analysis: LLM-powered comparison that understands concepts, not just keywords.
- Topological Roadmaps: A Directed Acyclic Graph (DAG) ensure you learn prerequisites first.
- Real-time Readiness Score: A dynamic metric that updates as you master new skills.
| 🧠 Neural Roadmap | 📊 Gap Analytics | 💻 AI Sandbox |
|---|---|---|
| DAG-based topological sorting for prerequisite-first learning. | 6-axis spider charts and depth/breadth scoring. | Real-time coding editor with an AI pair programmer. |
| 🃏 Active Recall | 🏆 Dynamic Portfolio | 🔊 Voice Briefs |
|---|---|---|
| Auto-generated AI flashcards for every skill in your resume. | Self-upgrading portfolio with AI-written project studies. | Audio briefings generated directly from your learning path. |
| Service | Environment | Endpoint |
|---|---|---|
| Frontend UI | Vercel | art-park-code-forge-hackathon-nine.vercel.app |
| Backend API | Render | artpark-codeforge-hackathon.onrender.com |
| API Docs | Swagger | /docs |
We store skills as nodes in a Directed Acyclic Graph (DAG). When a gap is identified:
- The engine performs a Topological Sort (Kahn's Algorithm) on the required skills.
- It recursively discovers and injects missing prerequisites into your roadmap.
- This ensures a logically sound learning sequence (e.g., Variables → Functions → APIs).
graph TD
A[Raw Resume] --> B(Gemini Skill Extractor)
C[Job Description] --> D(Gemini JD Extractor)
B & D --> E{Gap Analyzer}
E -->|Missing Nodes| F[Prerequisite Resolver]
F --> G[DAG Roadmap Generator]
G --> H((Neural Path))
Your Readiness Score is calculated using a weighted formula:
ArtPark_CodeForge_Hackathon/
│
├── README.md # This file
├── backend/
│ ├── app/
│ │ ├── main.py # FastAPI app backbone
│ │ ├── models/ # Pydantic schemas
│ │ ├── routes/ # Modular API routing
│ │ ├── services/ # Core AI & Business Logic
│ │ │ ├── skill_extractor.py # Gemini Skill Extraction
│ │ │ ├── gap_analyzer.py # Gap Scoring (Fuzzy Match)
│ │ │ └── learning_path_generator.py # DAG-powered Pathing
│ │ └── datasets/ # Curated Skill Knowledge Base
│ ├── requirements.txt # Backend Dependencies
│ └── .env # API Keys (Gitignored)
│
└── frontend/
├── src/
│ ├── App.jsx # Core State & Logic
│ ├── components/ # 46+ Atomic UI Units
│ │ ├── NeuralRoadmap.jsx # SVG Graph Rendering
│ │ ├── GapAnalysis.jsx # Data Viz Layout
│ │ └── CodingSandbox.jsx # Pair Programmer Hook
│ └── assets/ # Design Tokens
├── package.json # UI Dependencies
└── vite.config.js # Dev Server Config
- Mobile Neural Hub: Native iOS/Android app for on-the-go learning.
- GitHub Integration: Automatically sync roadmap progress with your GitHub contributions.
- Multi-Model Support: Expand beyond Gemini to support Claude and GPT-4o.
- Enterprise HR Portal: Collaborative hiring dashboards for technical teams.
- Clone:
git clone https://github.com/priyabratasahoo780/Resume-generater.git - Backend:
pip install -r requirements.txt&uvicorn app.main:app - Frontend:
npm install&npm run dev
Built with ⚡ by Team Invisible.Coding for the ArtPark CodeForge Hackathon 2026
