# Cyber Assistant v4.0

![Python Version](https://img.shields.io/badge/Python-3.8%2B-blue)
![Flask](https://img.shields.io/badge/Flask-v2.0+-blue)
![License](https://img.shields.io/badge/License-MIT-green)

Cyber Assistant v4.0 is an interactive web application that combines an intelligent chat assistant with advanced forensic investigation capabilities and image metadata extraction. This project utilizes a neural model (Mistral-7B-Instruct) to generate responses in multiple languages and modes (Chat, Investigation, and Metadata), integrating various libraries for text processing, image analysis, and forensic data extraction.

## Table of Contents

- [Features](#features)
- [Project Architecture](#project-architecture)
- [Installation and Setup](#installation-and-setup)
- [How to Run](#how-to-run)
- [Operation Modes](#operation-modes)
  - [Chat Mode](#chat-mode)
  - [Investigation Mode](#investigation-mode)
  - [Metadata Mode](#metadata-mode)
- [Advanced Forensic Analysis](#advanced-forensic-analysis)
- [Contribution](#contribution)
- [License](#license)

## Features

- **Intelligent Chat:** Interact with the assistant in various languages and styles (Technical or Freeform).
- **Forensic Investigation:** Conduct online investigations based on a target query, retrieving data from multiple websites and performing detailed forensic extraction.
- **Image Metadata Extraction:** Analyze the EXIF metadata of an image provided via URL, including GPS data conversion for Google Maps integration.
- **Neural Model Integration:** Uses the Mistral-7B-Instruct model via `llama_cpp` to generate responses and perform language translation when needed.
- **Query Caching:** Implements caching to improve performance on repeated queries.
- **Interactive Web Interface:** A responsive, configurable front-end built with HTML, CSS, and JavaScript.

## Project Architecture

The project consists of:

- **Backend (Flask):** Handles user requests, generates responses using the neural model, and executes forensic analysis and image metadata extraction functions.
- **Neural Model:** Loaded using the `llama_cpp` library and downloaded from the Hugging Face Hub if not available locally.
- **Front-end:** An interactive web interface that allows users to interact with the assistant in different operational modes.
- **Supporting Libraries:** Includes `nltk`, `langdetect`, `cachetools`, `duckduckgo_search`, `Pillow`, among others, for language processing, image analysis, and data extraction.

## Installation and Setup

### Prerequisites

- Python 3.8 or higher
- [Git]((https://github.com/g0dux/CHATCYBER-BETA-.git))
- Virtualenv (recommended)

### Installation Steps

1. **Clone the Repository:**

   ```bash
   git clone https://github.com/g0dux/CHATCYBER-BETA-.git
   cd cyber-assistant-v4

2. Create a Virtual Environment (recommended):

   python -m venv venv
   source venv/bin/activate   # Linux/MacOS
   venv\Scripts\activate      # Windows

3. Install the Dependencies:

   pip install -r requirements.txt

   The main dependencies include:

   • Flask
   • nltk
   • langdetect
   • cachetools
   • requests
   • psutil
   • llama_cpp
   • huggingface_hub
   • duckduckgo_search
   • Pillow

4. Initial NLTK Setup:

   On first run, the project will download the necessary NLTK data (such as punkt and vader_lexicon).

How to Run

After installing the dependencies, start the application with:

python app.py

The Flask server will start at http://0.0.0.0:5000/. Open your web browser and navigate to this URL to use Cyber Assistant v4.0.

Operation Modes

The application supports three operation modes, configurable via the web interface:

Chat Mode

• Description: Interact with the assistant in a conversational chat format.
• Features: Choose the language (Português, English, Español, Français, Deutsch) and response style (Technical or Freeform).

Investigation Mode

• Description: Conduct online investigations based on a provided target query, collecting data from multiple websites.
• Features: Adjust the number of websites to search, set a specific focus, and enable options for retrieving news or leaked data.
• Output: Displays a detailed report with extracted information and forensic analysis.

Metadata Mode

• Description: Extract and display the metadata of an image using its URL.
• Features: Includes EXIF data extraction and GPS coordinate conversion for a Google Maps link, if available.

Advanced Forensic Analysis

The advanced forensic analysis function has been enhanced to extract a wide range of relevant information from text, including:

• IPv4 and IPv6 Addresses
• Emails and Phone Numbers
• URLs and MAC Addresses
• Hashes: MD5, SHA1, and SHA256
• Vulnerability IDs: CVE (Common Vulnerabilities and Exposures)

This functionality helps in identifying and correlating critical data useful for digital forensic investigations and security analysis.

Contribution

Contributions are welcome! If you wish to help improve Cyber Assistant, follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix: git checkout -b my-new-feature
3. Make your changes and commit them: git commit -m 'Add new feature'
4. Push your branch: git push origin my-new-feature
5. Open a Pull Request on GitHub.

License

This project is licensed under the MIT License.

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Feel free to open issues and suggest improvements. Enjoy exploring the various features of Cyber Assistant v4.0!