Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.

Admin: @HusseinSheikho || @Hussein_Sheikho
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💻 GHOSTCREW — A Python AI tool for penetration testers and security professionals that conducts vulnerability searches in any services.

It works like a red team within your system. You describe the task in plain language — then it plans the attack itself, selects tools, and proceeds through the entire process: from reconnaissance to reporting. Without manual fiddling and endless commands.

What it can do in practice:

➡️ Checks everything: code, business logic, network traffic, protocols.
➡️ Analyzes the found vulnerabilities and explains where the problem is and how to fix it.
➡️ Works autonomously — you just launch it and get a full-fledged research.
➡️ Connects MCP servers and tools (nmap, metasploit, ffuf, etc.) itself.
➡️ Uses Pentesting Task Trees for meaningful decision-making, not just brute force.
➡️ Supports ready-made workflows for comprehensive checks.
➡️ Generates detailed reports in Markdown with facts and recommendations.
➡️ Remembers the dialogue context and doesn't "get lost" after a couple of requests.
➡️ Sees real files: wordlists, payloads, configs — and uses them in its work.
➡️ Allows you to choose an AI model and customize its behavior.
➡️ No registration and no restrictions.

⚙️ Installation:
git clone https://github.com/GH05TCREW/ghostcrew.git
cd ghostcrew
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt


▶️ Usage:
python main.py


⚠️ The information is provided solely for informational purposes. And it encourages to pay attention to security issues.

♎️ GitHub/Instructions

#python #soft #github
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😰 A repository of more than 100+ ready-made Python scripts that solve a bunch of tasks - without reinventing the wheel and suffering at night.

💬 parsing and searching on the internet;
💬 working with photos and videos;
💬 keyloggers and password managers;
💬 cloning websites;
💬 automating routines;
💬 and dozens of other useful things for real cases.

🔥 Ready-made practice + code, suitable for both learning and work.

⬇️ Save it, it will definitely come in handy!

#python #soft #github
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👨‍💻 Removing the background from any image in a few seconds with Python!

Do you want to automate image processing without Photoshop? 
A script project based on the rembg library:

📦 Installation:
pip install rembg


Example code:
from rembg import remove


def remove_background(input_path: str, output_path: str) -> None:
    """Removes the background from the image and saves the result."""
    with open(input_path, 'rb') as input_file:
        with open(output_path, 'wb') as output_file:
            image_bytes: bytes = input_file.read()
            output_bytes: bytes = remove(image_bytes)
            output_file.write(output_bytes)

if __name__ == "__main__":
    remove_background('input.png', 'output.png')


🟢Opens the file;
🟢Removes the background;
🟢Saves the finished image.

5️⃣ GitHub/Instructions

#python #soft #code
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⚡️ Colorizing old black-and-white videos and "bringing faces to life" for FREE

SVFR — a full-fledged framework for restoring faces in videos.

It can:
💬 BFR — improve blurry faces.
💬 Colorization — colorize black-and-white videos.
💬 Inpainting — redraw damaged areas.
💬 and combine all of this in one pass.

Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.

⚙️ Installation locally:

1. Create an environment

conda create -n svfr python=3.9 -y
conda activate svfr


2. Install PyTorch (for your CUDA)

pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2


3. Install dependencies

pip install -r requirements.txt


4. Download models

conda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt


5. Start processing videos

python infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region


Where task_ids:

* 0 — face enhancement
* 1 — colorization
* 2 — redrawing damage

An ideal tool if:
🟢you're restoring archival videos;
🟢you're creating historical content;
🟢you're working with neural networks and video effects;
🟢you want a wow result without paid services.

▶️ Demo on Hugging Face

♎️ GitHub/Instructions

#python #soft #github

https://t.me/CodeProgrammer
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