Misconception Detector

A high-performance machine learning application built with FastAPI and PostgreSQL designed to detect, log, and analyze misconceptions or factual inaccuracies in text data.

🚀 Features

• FastAPI Backend: High-performance, asynchronous REST API endpoints.
• Machine Learning Pipeline: Integrated ML model for real-time text classification and analysis.
• PostgreSQL Storage: Robust relational database storage for managing logged entries, detection history, and model metadata.
• Scalable Architecture: Decoupled application layer ready for containerisation and cloud deployment.

🛠️ Tech Stack

• Language: Python 100%
• Framework: FastAPI
• Database: PostgreSQL
• ORM / Migration: SQLAlchemy / Alembic

📁 Repository Structure

misconception-detector/
├── app/                  # Main application source code
│   ├── api/              # API routers and endpoints
│   ├── core/             # Configuration, security, and database connection
│   ├── models/           # SQLAlchemy / database models
│   ├── schemas/          # Pydantic validation schemas
│   └── services/         # Machine learning logic and core detection algorithms
├── .gitignore            # Git ignore configurations
└── README.md             # Project documentation

⚙️ Getting Started

Prerequisites

• Python 3.10+
• PostgreSQL Database v18

Installation

1. Clone the repository:

   git clone https://github.com
   cd misconception-detector

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install the dependencies (ensure you create a requirements.txt or pyproject.toml file):

   pip install -r requirements.txt

Configuration

Create a .env file in the root directory and add your system configuration details:

DATABASE_URL=postgresql://user:password@localhost:5432/misconception_db
MODEL_PATH=app/services/models/your_model.pkl
SECRET_KEY=your_super_secret_key

Running the Application

Start the local FastAPI development server using Uvicorn:

python -m fastapi dev app/main.py                                     

The application will be accessible at http://127.0.0.1:8000. You can explore the interactive Swagger API documentation at http://127.0.0.1:8000/docs.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.