VITEEE Rank Predictor

VITEEE Rank Predictor & Mock Test Platform

A comprehensive platform designed by students for students to prepare for the VITEEE entrance exam. This application offers rank prediction based on past trends and a full-fledged mock test environment to practice effectively.

🚀 Features

• Rank Predictor: Estimate your potential rank and eligible colleges based on your expected score.
• Mock Tests: Take full-length, timed mock tests that simulate the actual VITEEE exam pattern.
• Detailed Analytics: Track your performance with detailed subject-wise analysis and score history.
• Syllabus & Pattern: Access the complete syllabus and exam pattern for VITEEE 2027.
• Mobile Optimized: Fully responsive design ensuring a seamless experience on all devices.
• No Signup Required: Instant access to mock tests without the hassle of registration.

🛠️ Tech Stack

• Framework: Next.js (App Router)
• Language: TypeScript
• Styling: Tailwind CSS & shadcn/ui
• Database: MongoDB
• Caching: Upstash Redis
• Storage: Cloudflare R2
• Deployment: Vercel

⚙️ How It Works

1. Question Storage & Retrieval

Questions are stored in a MongoDB database, categorized into separate collections for each subject: maths, physics, chemistry, and apt_eng (Aptitude & English).

2. Exam Generation

When a user starts a mock test, the backend (/api/mocktest/questions) dynamically generates a unique question paper. It uses MongoDB's aggregation pipeline with the $sample stage to randomly select questions from each subject collection according to the VITEEE pattern (e.g., 40 Math, 35 Physics, 35 Chemistry, 10 Aptitude, 5 English).

3. Image Handling & Caching

Many questions contain images (diagrams, equations). To ensure high performance and low latency:

• Storage: Original images are stored in Cloudflare R2 (an S3-compatible object storage).
• Caching: We use Upstash Redis as a caching layer.
  • When an image is requested, the API first checks Redis.
  • Cache Hit: If found, the image is served directly from Redis (stored as a base64 string), resulting in sub-millisecond response times.
  • Cache Miss: If not found, the image is fetched from Cloudflare R2, served to the user, and simultaneously cached in Redis for future requests (with a 24-hour TTL).
  • This strategy significantly reduces bandwidth costs and improves load times for users.

4. Result Processing

Upon submission, the user's answers are compared against the correct options stored in the database. The score is calculated instantly, and the result is saved to the test-results collection in MongoDB for analytics.

📦 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/viteee-rank-predictor.git
   

2. Install dependencies:

   npm install
   

3. Set up environment variables in .env.local:

   MONGODB_URI=your_mongodb_uri
   MONGODB_DB=your_database_name
   UPSTASH_REDIS_REST_URL=your_redis_url
   UPSTASH_REDIS_REST_TOKEN=your_redis_token
   NEXT_PUBLIC_R2_PUBLIC_URL=your_r2_public_url
   NEXT_PUBLIC_CLOUDFLARE_TURNSTILE_SITE_KEY=your_turnstile_site_key
   CLOUDFLARE_TURNSTILE_SECRET_KEY=your_turnstile_secret
   TURNSTILE_HOSTNAMES=yourdomain.com,www.yourdomain.com
   

4. Run the development server:

   npm run dev
   

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

contact@malabarmatrix.site