🚀 AutoPilot — a free automation suite that replaces a dozen services at once

If you love tools that save time, money, and nerves — this is 100% for you.

This is an open-source panel on Python + Streamlit, packed with a whole arsenal of useful automations.

You open it — and it’s like gaining a superpower: doing everything faster.

What it can do:
🖼 Background Remover — removes photo backgrounds in a second.
🧾 QR Generator — creates QR codes for anything.
💻 Fake Data Generator — generates realistic test data.
🎧 Audiobook Converter — turns PDFs into audiobooks.
📥 YouTube Downloader — downloads video and audio.
💬 Bulk Email Sender — mass email sending.
📸 Image Downloader — searches and downloads images by keywords.
📝 Article Summarizer — creates well-written concise summaries.
📊 Resource Monitor — monitors your system resources.
🔍 Code Analyzer — checks code with Pylint and Flake8.
🧹 Clipboard Manager — stores clipboard history.
🔗 Link Checker — checks which links are alive.
📷 Image Editor — a mini-Photoshop: crop, blur, resize, watermark, formatting, and lots of effects.
🗞 News Reader — reads out current news.

And that’s just part of the list.

Why do you need this?
🟢 a ready set of utilities for developers, marketers, designers, or SMM;
🟢 huge time savings;
🟢 local, free, and without limits;
🟢 can be integrated into your projects, bots, or workflow.

⚡️ How to run (quickly)

git clone https://github.com/Ai-Quill/automated.git
cd automated
pip install -r requirements.txt
streamlit run app.py

🖥Open in your browser: http://localhost:8501

And enjoy the panel where all tools are just one click away.

♎️ GitHub/Instructions

#python #soft #github

https://t.me/DataScienceN 🌟

⚡️ Running DeepSeek on our computer using Python

Do you want an LLM on your computer: to work offline, not leak data, and seamlessly integrate into a bot? Then let's take DeepSeek Coder and get started!

⚙️ Installing dependencies:

pip install -U transformers accelerate torch

▶️ Example code:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "deepseek-ai/deepseek-coder-6.7b-base"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.float16,   # if the GPU supports fp16
    device_map="auto"            # if there's a GPU — it will use it
)
model.eval()

prompt = "Write a Telegram feedback bot on aiogram"

inputs = tokenizer(prompt, return_tensors="pt")
device = next(model.parameters()).device
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=180,
        do_sample=True,      # IMPORTANT: otherwise the temperature doesn't affect
        temperature=0.7,
        top_p=0.9
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

➕ Advantages:
— works locally (after downloading the weights);
— easily integrates into Telegram/Discord/CLI;
— can be accelerated on the GPU via device_map="auto".

If memory is limited — there are quantized versions (4bit/8bit) and GGUF.

👍 Saving

#python #soft #code