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LabProject

Project Description

LabProject is a comprehensive repository that includes various projects and experiments primarily written in C#, JavaScript, Jupyter Notebook, SCSS, Python, and CSS.

Table of Contents

Installation

  1. Clone the repository:

    git clone https://github.com/FatSijarina/LabProject.git

  2. Navigate to the project directory:

    cd LabProject

  3. Install the necessary dependencies:

    npm install

  4. (Optional) Set up virtual environments for Python projects:

    python -m venv venv source venv/bin/activate pip install -r requirements.txt

Usage

Instructions on how to use the various projects and experiments in this repository.

Features

  • Case Management: Create, update, and track multiple criminal cases with details such as status (open, in-progress, closed) and associated documents.
  • Evidence Handling: Upload and categorize evidence (biological or physical), with automatic checks and potential compressions for files (PDF, images, videos).
  • Task Management: Assign and manage tasks related to specific cases, including due dates, notifications, and completion statuses.
  • Role-Based Access: Utilize .NET Identity to enforce user roles and authorization policies, ensuring only authorized personnel can view or modify sensitive data.
  • AI-Assisted Investigations:
    • Chatbot: Offers immediate suggestions and investigative tips by leveraging AI (Python + Django backend).
    • Suspect Generation: Employs machine learning algorithms to compare evidence and statements, generating potential suspects based on predefined parameters.
  • Document & File Management: Store and organize case-related files (PDF, .webm, images in .webp or compressed form).
  • Responsive User Interface: A React-based frontend with SCSS styling, ensuring a clean and intuitive layout accessible across devices.

Technologies Used

  • C#: Main programming language for backend development.
  • JavaScript: Used for frontend development and scripting.
  • Jupyter Notebook: For data analysis and visualization.
  • SCSS: For advanced styling and theming.
  • Python: Used for scripting and data manipulation.
  • CSS: For basic styling.

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