semvec/pypi-packages huggingface dataset:
youtube-search-tool-using-subtitles 0.1.0
Search and query YouTube videos using AI embeddings
Requires Python: >=3.8

# YouTube Search Tool

Search and query YouTube videos using AI embeddings. Add individual videos or entire channels, then search through their content using natural language.

## Features

- 🎥 Process individual YouTube videos or entire channels
- 🔍 Semantic search through video transcripts  
- 🤖 Multiple AI providers (OpenAI, Google, Local models)
- 🌐 Web interface with Gradio
- ⚡ Fast batch processing with concurrent downloads

## Installation

```bash
pip install youtube-search-tool
```

## Quick Start

### 1. Set up environment
Create a `.env` file:
```bash
# Choose your AI provider
USE_EMBEDDING_PROVIDER=openai  # or 'google' or 'local'
USE_LLM_PROVIDER=openai

# API Keys (if using cloud providers)
OPENAI_API_KEY=your_openai_key
GEMINI_API_KEY=your_google_key

# Optional: Customize processing
MAX_WORKERS=4
MAX_CHANNEL_VIDEOS=100
```

### 2. Add videos
```bash
# Add a single video
yt-search add "https://www.youtube.com/watch?v=QPM0WNqwJBc"

# Add an entire channel
yt-search add "https://www.youtube.com/@Lamediainglesa"
```

### 3. Search
```bash
# Search through your videos
yt-search search "machine learning basics"
yt-search search "python tutorial"
```

### 4. Web Interface
```bash
# Launch the web interface
yt-search web
```

## Requirements

- Python 3.8+
- Chrome/Chromium browser (for channel scraping)
- API keys for cloud providers (optional, can use local models)

## Supported AI Providers

- **OpenAI**: `text-embedding-ada-002` (requires API key)
- **Google**: `text-embedding-004` (requires API key)  
- **Local**: `all-MiniLM-L6-v2` (no API key needed)

## License

MIT License
dashai-test-classification-package 0.0.21
Image Classification Plugin for DashAI
Requires Python: >=3.8
Keywords: DashAI, Dataloader, Model

# Image Classification Plugin

This plugin provides the necessary tools to implement image classification tasks using the DashAI framework. The plugin includes a data loader and an MLP (Multi-Layer Perceptron) based classification model.

## Components

### ImageDataLoader
- Specialized data loader for images
- Compatible with ZIP files containing image folders
- Converts images to bytes format for processing
- Integration with HuggingFace Dataset format

### MLPImageClassifier
- Image classification model based on Multi-Layer Perceptron
- Features:
  - Configurable hidden layers
  - Adam optimizer
  - CrossEntropyLoss function
  - GPU support (CUDA)
  - Automatic image transformations (resizing to 30x30)
  - Ability to save and load trained models

## Model Parameters

- `epochs`: Number of training epochs (default: 10)
- `learning_rate`: Learning rate (default: 0.001)
- `hidden_dims`: Hidden layer dimensions (default: [128, 64])

## Requirements

- Python 3.x
- PyTorch
- DashAI
- datasets (HuggingFace)
- torchvision

## Usage

1. Load the image dataset using `ImageDataLoader`
2. Configure the `MLPImageClassifier` model with desired parameters
3. Train the model using the `fit` method
4. Make predictions using the `predict` method

## Notes

This plugin is designed to integrate with the DashAI framework and does not implement a specific model itself, but rather provides the necessary infrastructure for image classification.
energy-stats 0.1.0
A python statistical library for energy statistics methods.
Requires Python: >=3.7

# Estats
Estats is a python statistical package containing a set of method based upon energy statistics.

Energy statistics are a set of distribition-free non-parametrics methods first expounded by GÃ¡bor J. SzÃ©kely and later on expanded by several others. They are of particular interest because they very little about the data generating distribution, merely that either its first or second moment is defined.

As well as being non-parametrc, energy statistics are valuable because its methods take no notice of the size of the data matrix $X$, since all its methods are functions of the distance matrix of $X$ not of $X$ itself, therefore there is no trouble in analysisng multidimensional data