semvec/pypi-packages huggingface dataset:
SmartModelVision 0.1.0
A simple CNN wrapper with training, evaluation, and plotting utilities and Simple powerful object detection
License: MIT
Requires Python: >=3.7

# 🧠 SmartModelCNN

> A unified deep learning library for image **classification**, **object detection**, and **automatic dataset labeling** — powered by TensorFlow, OpenCV, and NumPy — but easy to use with **just one line of code**!

[![PyPI version](https://img.shields.io/pypi/v/smartmodelcnn)](https://pypi.org/project/smartmodelcnn/)
[![Python](https://img.shields.io/pypi/pyversions/smartmodelcnn)](https://pypi.org/project/smartmodelcnn/)
[![License](https://img.shields.io/github/license/YKandoloJean/SMARTVISIONCNN)](https://github.com/YKandoloJean/SMARTVISIONCNN)

---

## 🔥 Features

### 🎯 Object Detection — `SVOL` (Smart Vision Object Locator)
- 🚀 Pre-trained EfficientDet D0 model from TensorFlow Hub
- 🖼️ Automatic image preprocessing, bounding box detection, and drawing
- 📹 Real-time detection from webcam feed
- 🎯 High accuracy object localization on COCO dataset classes

### 🧠 Image Classification — `SmartVisionCNN`
- 🧱 Customizable convolutional neural network architecture
- 🧪 Supports training, evaluation, and accuracy/loss visualization
- 💾 Save and load trained models seamlessly
- 🔄 Easily add custom layers like Dropout for regularization

### 📝 Automatic Dataset Labeling — `AutoLabeler`
- 🤖 Automatically generate bounding box annotations for unlabeled image datasets
- 📁 Supports saving annotations in YOLO `.txt` and Pascal VOC `.xml` formats
- 🔍 Uses SVOL detection results for labeling with configurable confidence threshold
- 🎯 Requires user to provide class labels for accurate annotation generation

### 🔒 Model Protection — `ModelProtector`
- 🔐 Password-protect your trained models to restrict unauthorized access
- 🔓 Unlock models via password prompt to enable predictions and saving
- 🔒 Simple and secure file-based locking mechanism
- 🛡️ Prevents accidental or malicious model usage without permission

---

## 📦 Installation

Install the latest release from PyPI:

```bash
pip install SmartModelVision
my-package-charu 0.0.7
Zunno package created by charu
License: MIT
Requires Python: >=3.7

# my_package_charu

`my_package_charu` is a custom Python package for interacting with large language models and generating vector embeddings using a locally running [Ollama](https://ollama.com/) instance. It includes:

- `Zunno`: A simple chat interface with LLMs (like Gemma:2b)
- `ZunnoEmbeddings`: Generate vector embeddings for any text using models like `nomic-embed-text`

## Installation

```bash
pip install my_package_charu
```

Make sure you have Ollama installed and running models like gemma:2b or nomic-embed-text
```bash
ollama pull gemma:2b
ollama pull nomic-embed-text
```

# Features
- Chat with any LLM using Ollama's HTTP API
- Generate embeddings using Ollama's embedding models
- Plug-and-play classes, easy to integrate into projects
- FastAPI endpoint for Swagger UI 

# Usage

## Chat with a Local LLM

```python
from my_package_charu import Zunno

z = Zunno(model_name="gemma:2b")
z.run()
```

## Generate Vector Embeddings

```python
from my_package_charu import ZunnoEmbeddings

ze = ZunnoEmbeddings()
embedding = ze.get_embedding(model_name="nomic-embed-text", text="hello world")
print(embedding[:5]) 
```


## Run FastAPI for Swagger UI
```bash
uvicorn zunno_embed_server:app --reload 
```

## open your browser and go to
`http://localhost:8000/docs`
dlcopilot 0.1.0
An assistant for training and analyzing deep learning models in PyTorch.
License: MIT
Requires Python: >=3.8
Keywords: deep learning pytorch training analysis copilot


### ✅ `README.md`

# DLCopilot 🚀

**DLCopilot** is your assistant for training and analyzing deep learning models in PyTorch. It provides a simple and extensible interface for visualizing model structure, input-output dimensions, training progress, and performance metrics — making your development process faster and more intuitive.

---

## 📦 Features

- 🔍 Inspect model layer-wise shapes and input-output dimensions
- 📊 Visualize class distribution in datasets
- 📈 Track and plot training and validation loss
- 🧪 Easy integration with your PyTorch training loop
- ✅ Lightweight and modular design

---

## 🛠️ Installation

Clone the repository and install the dependencies:

```bash
git clone https://github.com/KIREN2612/dlcopilot.git
cd dlcopilot
pip install -r requirements.txt
````

You can also install as a package (for development):

```bash
pip install -e .
```

---

## 🧪 Example Usage

```python
from dlcopilot import DLCopilot
from your_model import MyModel  # Replace with your model
from torch.utils.data import DataLoader

# Initialize model, optimizer, loss, data loaders
model = MyModel()
optimizer = torch.optim.Adam(model.parameters())
criterion = torch.nn.CrossEntropyLoss()
train_loader = DataLoader(...)
val_loader = DataLoader(...)

# Create DLCopilot instance
copilot = DLCopilot(model, optimizer, criterion, train_loader, val_loader)

# Inspect architecture
copilot.inspect_input_output_shapes()
copilot.inspect_layer_shapes()

# Analyze data
copilot.class_distribution()

# Train and visualize
copilot.train_and_analyze(epochs=5)
```

See [docs/usage.md](docs/usage.md) for full examples.

---

## 🧪 Testing

Run tests with:

```bash
pytest tests/
```

Make sure to set the `PYTHONPATH` to the root of the project if needed:

```bash
export PYTHONPATH=$(pwd)  # On Linux/macOS
$env:PYTHONPATH = (Get-Location)  # On Windows PowerShell
```

---

## 📄 Documentation

* [Usage Guide](docs/usage.md)
* [API Reference (Coming Soon)]()

---

## 🧑‍💻 Author

**Kiren**
📧 [kiren2612@gmail.com](mailto:kiren2612@gmail.com)
🔗 [github.com/KIREN2612](https://github.com/KIREN2612)

---

## 📜 License

This project is licensed under the terms of the **MIT License**. See [LICENSE](LICENSE) for more details.
CustomTkinterTitlebar 1.0.8.7
This is a 📚project can help you to have a custom titlebar! 这是一个可以创建自定义标题栏的📚项目
License: MIT

# _自定义标题栏_ _CustomTkinterTitlebar_

### 📃 *这是一个项目可以帮助你拥有一个自定义的标题栏！小组件可以被放到这个标题栏里面*

*This is a project can help you to have a custom titlebar! Widgets can add into titlebar!*

### 安装 Install
```console
pip install CustomTkinterTitlebar --user
```

### Gallery
![image](https://github.com/user-attachments/assets/018c1315-fc94-4ee5-a2a5-396dfb48a471)

repo: https://github.com/littlewhitecloud/Terminal

### 快速开始 Quick Start
```python
from customtitlebar import CTT
from tkinter import ttk
from sv_ttk import set_theme

example = CTT()
example.title("TitleBar")
example.geometry("1030x570")
set_theme("dark")
en = ttk.Entry(example.titlebar)
en.pack(fill = "y", expand = True, pady = 1)

example.mainloop()
```

![image](https://github.com/user-attachments/assets/c04deef7-a2d2-4fb1-be19-3632e4149f6c)
![image](https://github.com/user-attachments/assets/181b8bcf-85ec-4dbc-81b2-086c36bbed11)
product-connections-manager 1.0.1
A comprehensive platform for managing Product Connections operations with automated EDR printing
License: MIT
Requires Python: >=3.8
Keywords: walmart, retail-link, edr, event-detail-report, automation, printing, pdf, business, workflow

# Product Connections Manager - Enhanced EDR Printer

A comprehensive platform for managing Product Connections operations, including automated EDR printing, event management, and retail link integrations.

## Features

- **Enhanced PDF Generation**: Professional-grade PDF reports with custom formatting
- **PDF Consolidation**: Merge multiple reports into single documents  
- **Signature Fields**: Automated signature blocks with date/time stamps
- **Event Code Translation**: Human-readable event descriptions
- **Batch Processing**: Automated processing of multiple events
- **Console Scripts**: Command-line tools for easy usage

## Installation

```bash
pip install product-connections-manager
```

For PDF features:
```bash
pip install "product-connections-manager[pdf]"
```

## Quick Start

```python
from product_connections_manager import EnhancedEDRPrinter

# Initialize the printer
printer = EnhancedEDRPrinter()

# Process events with PDF consolidation
printer.process_multiple_events(['12345', '67890'])
```

## Command Line Usage

```bash
# Enhanced printing
edr-printer --events 12345,67890 --output consolidated_report.pdf

# Automated batch processing
edr-automated --batch-size 10
```

## License

MIT License