Doubt Dashboard

A dashboard built to track and visualize common doubts across different project groups. It began as a security research exercise and grew into a full-stack project with a clean interface and custom data handling.

✨ Features

• Periodic Monitoring: Visibility into new doubts synced every 5 days.
• Advanced Filtering: Quickly isolate groups "With Doubts" or filter by "Recent Updates".
• Intelligent Sorting: Sort by group number, volume of doubts, or recency.
• Multi-Format Export: Export aggregated data as professional CSV or formatted Markdown.
• UI/UX: Dark-themed, responsive interface optimized for Desktop and Mobile (iOS/Safari).
• CDC Logic: Custom hashing mechanism to trigger updates only on actual content changes.

🛠️ Tech Stack

Frontend

• Framework: React 19
• Build Tool: Vite 8
• Language: TypeScript
• Styling: Vanilla CSS (CSS3 Variable-based Design System)

Backend & Data

• Language: Python 3.10+
• Database/Cache: Redis (for high-performance data retrieval)
• Deployment: Configured for Vercel Serverless

🏁 Getting Started

Prerequisites

• Node.js (v18+) & pnpm
• Python 3.10+
• Redis Server (Local or Managed)

Setup & Run

1. Clone & Install:

   git clone https://github.com/manas/doubt-dashboard.git
   cd doubt-dashboard
   pnpm install

2. Backend Setup:

   # Create a virtual environment
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   
   # Install dependencies
   pip install -r dev-requirements.txt

3. Variables: Copy .env.example to .env and configure your Redis credentials.

4. Launch:

   pnpm dev # Runs both frontend and backend concurrently

📂 Project Structure

.
├── api/                # Vercel Serverless Functions (Python)
├── scripts/            # Backend utility scripts & Scrapers
├── src/                # Frontend source code
│   ├── components/     # Modular UI components
│   └── helpers.ts      # Data processing & Export logic
└── index.html          # Entry point

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🚀 Behind the Project

The Backstory

The project sparked when I discovered an IDOR (Insecure Direct Object Reference) vulnerability in my university's doubt submission portal. By manipulating URL parameters, it was possible to access submissions from any group. What started as a simple Python script to gather academic data turned into an engineering exercise to experiment with caching, modular architectures, and custom data processing.

Why Framework-Free?

One of the goals was to implement the backend without any framework.

• It was a challenge to gain hands-on experience with raw Python server logic and manual request handling.
• While the backend is intentionally minimal, the frontend uses React to maintain an organized, modular developer experience.

🛡️ Privacy & Ethics

• Privacy First: All personal data (PII), names, and images were permanently removed from the data pipeline to comply with GDPR.
• Infrastructure Respect: The scraper runs on a low-frequency cycle (every 5 days) with strict rate-limiting to avoid any strain on university systems.
• Security: The scraping logic is decoupled from the dashboard to ensure the dashboard only handles processed, sanitized data.

📜 License & Disclaimer

This project is for academic and educational purposes only.

• Ethics: Before using or adapting this code, ensure you comply with your institution's ethical guidelines and terms of service.
• Liability: The author is not responsible for any misuse of this tool. It was built as a proof-of-concept for research and learning.

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