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Architecture

Architecture Below is a typical architecture flow for a Next.js + Node-based solution:

            +---------------------------------+
            |           Frontend (UI)         |
            |  Next.js React pages and hooks  |
            |   (e.g. /dashboard, /alerts )   |
            +----------------^-----------------+
                             |
                             | (HTTP/HTTPS, API calls)
                             v
            +---------------------------------+
            |       Next.js API Routes        |
            |   or a separate Node.js server  |
            +----------------^-----------------+
                             |
 +---------------------------+----------------------------+
 |                          |                            |
 |                          |                            |
 v                          v                            v

+---------+ +-------------+ +--------------+ | Web | | Social Media| | NLP Service | | Crawler | | APIs | | (e.g. HF) | +---------+ +-------------+ +--------------+ | | | | | (Fetched or crawled data) | | v | +------------------------------------------------------+ | v +-------------------+ | Database (Mongo) | +-------------------+ | v +----------------------------------+ | Aggregated & Processed Data | +----------------------------------+ | v [Dashboard UI]

Frontend (Next.js):

Pages: index.js for home, dashboard.js for analysis results, alerts.js for flagged items. React components to display real-time flagged data. Next.js API Routes:

Could have routes like POST /api/crawl, GET /api/results, GET /api/flagged. Integrate a schedule or call out to a serverless function (or cron job) that crawls data. Crawler:

A separate microservice or script that runs periodically (via cron, GitHub Actions, or a job scheduler) to scrape or fetch data from AI sources & social media. Possibly store data in the DB, setting processed: false initially. NLP Service:

Could be integrated into the Node server or run as a separate Python-based service if you use Python libraries like spaCy, transformers, or specialized ML. The NLP pipeline marks documents as flagged or not flagged based on your “indicators.” Database:

Store raw text documents, analysis results, and logs of suspicious findings. MongoDB recommended for flexibility with semi-structured data.

Architectural Style

  • Monolithic Architecture: Provides a straightforward structure suitable for small to medium-sized applications.

System Architecture Diagram

Architecture Diagram

Technology Stack

  • Backend: FastAPI
  • Database: In-memory (for simplicity; can be replaced with PostgreSQL or another database as needed)
  • Authentication: JWT tokens
  • Containerization: Docker

Data Models and Schemas

Key Components

  • API Endpoints: Handling HTTP requests related to users and tasks.
  • Data Storage: Managing in-memory data persistence for users and tasks.

Reflection

  • In-Memory Database: Chosen for simplicity in the sample project; recommended to use a persistent database for production.
  • Possible Enhancements: Integrate a database like PostgreSQL, implement more robust authentication mechanisms, and add more comprehensive error handling.