StudyForge AI

Adaptive Learning & AI-Powered Video Interview Intelligence Platform

StudyForge AI is an enterprise-grade technical preparation and learning platform. Utilizing Generative AI, Retrieval-Augmented Generation (RAG), and computer vision telemetry, StudyForge AI delivers adaptive study sessions, live video interview simulations, code execution evaluation, and posture and speech coaching.

Whether preparing for software engineering roles, system architecture design, or executive technical evaluations, StudyForge AI functions as an intelligent mentor by identifying technical knowledge gaps, analyzing communication delivery, and generating adaptive improvement roadmaps.

• Real-Time Video Interviews: Live interview simulations featuring conversational AI powered by NVIDIA NIM (Llama 3.1 70B) streamed directly to the frontend via WebSockets.
• Posture & Body Language Analysis: Non-invasive posture scoring, shoulder alignment metrics, and slouching detection using MediaPipe.
• Eye Contact & Attention Tracking: Gaze direction monitoring, camera attention percentages, and looking-away telemetry.
• Voice Delivery & Pacing Analytics: Real-time evaluation of speaking speed (WPM), filler word breakdown (um, uh, like), silence/pause estimation, and speech clarity scoring.

• Cloudflare R2 Integration: Fast, secure, and highly scalable cloud storage for uploading resumes and cover letters.
• Resume Extraction: Automated extraction of technical skills, projects, and work experience from resumes using PyMuPDF and Llama 3.1.
• ATS Keyword Recommendations: Missing resume keyword recommendations and bullet point optimization compared against provided Job Descriptions.
• Job Description Alignment: Custom interview question generation aligned to target role requirements and company cultures.

• Monaco Editor Integration: Multi-language code editor supporting Python, TypeScript, JavaScript, Java, C++, Go, and Rust.
• Automated Code Execution & Review: Test case validation, execution metrics, and automated AI code reviews analyzing time and space complexity ($O(N)$).

• Interactive Floating Assistant: Context-aware AI mentor accessible across all application views for concept clarification, progressive hints, and study guidance.

• 6-Axis Radar Reports: Multi-dimensional evaluation covering Communication, Technical Accuracy, Confidence, Problem Solving, Coding, and STAR Behavioral structure.
• Adaptive Learning Plans: Personalized 7-day, 14-day, and 30-day preparation roadmaps based on historical performance metrics.

• Core: React 18, TypeScript, Vite
• Styling: Tailwind CSS, Framer Motion
• State & Data Fetching: TanStack Query, Axios
• UI Components: Monaco Editor, Recharts, Lucide React
• Computer Vision: MediaPipe Tasks Vision

• Framework: FastAPI (Python 3.11+)
• Database & ODM: Supabase PostgreSQL, SQLAlchemy ORM, asyncpg
• Authentication: Local (JWT + bcrypt) & OAuth (Google/GitHub integration)
• Communication: WebSockets (Bidirectional AI Streaming), RESTful API
• Cloud Storage: Cloudflare R2 (S3-compatible)
• Transactional Email: Brevo API

• Inference Engine: NVIDIA NIM (meta/llama-3.1-70b-instruct)
• Vector Engine & RAG: ChromaDB / Retrieval-Augmented Generation
• Third-Party Agent Integration: Lyzr AI Studio

StudyForge-AI/
├── backend/
│   ├── app/
│   │   ├── api/          # REST & WebSocket route handlers
│   │   ├── core/         # Security, OAuth, configuration, and logging
│   │   ├── db/           # PostgreSQL initialization and seeding
│   │   ├── models/       # Beanie document models
│   │   └── services/     # ATS Scanner, R2 Storage, AI Engine, Resume Parser
│   └── venv/
├── frontend/
│   ├── src/
│   │   ├── app/          # Router & Authentication context
│   │   ├── components/   # Shared UI & Chatbot layout components
│   │   ├── features/     # Dashboard, Interviews, Practice, and Reports
│   │   └── lib/          # Axios HTTP client configuration
│   ├── package.json
│   └── vite.config.ts
├── render.yaml           # Deployment configuration blueprint
└── README.md

• Node.js: v18.0.0 or higher
• Python: v3.11.0 or higher
• PostgreSQL: Supabase PostgreSQL Cluster connection URI

git clone https://github.com/JAY4IGNITE/StudyForge-AI.git
cd StudyForge-AI

Create a .env file in the project root containing the following configurations:

# Core API & Database
APP_NAME="StudyForge AI"
API_VERSION="v1"
DATABASE_URL="postgresql+asyncpg://<username>:<password>@<host>:6543/postgres"

# Security (JWT & OAuth)
JWT_ACCESS_SECRET="your-access-secret-key"
JWT_REFRESH_SECRET="your-refresh-secret-key"
GOOGLE_CLIENT_ID="your_google_client_id"
GOOGLE_CLIENT_SECRET="your_google_client_secret"
GITHUB_CLIENT_ID="your_github_client_id"
GITHUB_CLIENT_SECRET="your_github_client_secret"

# AI & LLM Endpoints
NVIDIA_NIM_API_KEY="nvapi-your-nvidia-nim-key"

# Cloud Storage (Cloudflare R2 for Resumes)
R2_ACCOUNT_ID="your-cloudflare-account-id"
R2_ACCESS_KEY_ID="your-r2-access-key"
R2_SECRET_ACCESS_KEY="your-r2-secret-key"
R2_BUCKET_NAME="studyforge-bucket"
R2_ENDPOINT="https://<account-id>.r2.cloudflarestorage.com"

# Mailing (Brevo)
BREVO_API_KEY="your-brevo-api-key"
BREVO_SENDER_EMAIL="[email protected]"

cd backend
python -m venv venv

# On Windows:
venv\Scripts\activate
# On macOS/Linux:
# source venv/bin/activate

pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000

cd frontend
npm install
npm run dev

Navigate to http://localhost:5173 in your browser.

The repository includes a standardized render.yaml configuration for automated deployment on Render as a single unified service:

• Build Command: npm install --prefix frontend && npm run build --prefix frontend && pip install -r backend/requirements.txt
• Start Command: cd backend && uvicorn app.main:app --host 0.0.0.0 --port $PORT

Jaya Sai Krishna Vasamsetti

• GitHub: @JAY4IGNITE
• Email: [email protected]