semvec/pypi-packages huggingface dataset:
dijkstra-tg 0.1.0.post2
A simple Dijkstra's Algorithm implementation
Requires Python: >=3.8

# Dijkstra's Algorithm Implementation
[![Upload Python Package](https://github.com/TOMG-A/DijkstrasAlgorithm/actions/workflows/python-publish.yml/badge.svg)](https://github.com/TOMG-A/DijkstrasAlgorithm/actions/workflows/python-publish.yml)
[![Sample Project - View](https://img.shields.io/badge/Sample_Project-View-informational?logo=github)](https://github.com/TOMG-A/DjikstraGraphGeneration)
[![Made with Python](https://img.shields.io/badge/Python->=3.8-blue?logo=python&logoColor=white)](https://python.org "Go to Python homepage")
[![License](https://img.shields.io/badge/License-MIT-blue)](#license)
[![GitHub tag](https://img.shields.io/github/tag/TOMG-A/DijkstrasAlgorithm?include_prereleases=&sort=semver)](https://github.com/TOMG-A/DijkstrasAlgorithm/releases/)


A simple Dijkstra's Algorithm implementation


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the package.

```bash
pip install -i https://test.pypi.org/simple/ dijkstras-algorithm-TOMG-A
```

## Usage



```python
from dijkstra import *

NodeA=Node("A")
graph=Graph(NodeA)
dist,prev,_=Dijkstra(graph,NodeA)
```

## License

Released under [MIT](/LICENSE) by [@TOMG-A](https://github.com/TOMG-A).
neuranet 0.0.1
NeuraNET Python API library
Keywords: ai,machine learning,neuranet


# 🌐 NeuraNET Python API library

The Official NeuraNET Python Library

---

## 🛠 Installation

To get started with the library, simply run the following command:

```bash
pip install neuranet
```

## 💬 Chat Usage
Using the Chat AIs library is straightforward. Below is an example of a conversation:
```python
import neuranet

api_key = "YOUR_API_KEY_HERE"
mode = 'chat'
client = neuranet.client(api_key, mode)

# Set up the conversation history and model
conversation_history = [
    {"sender": "instruct", "content": "You are a helpful assistant."},
    {"sender": "user", "content": "Hello."},
    {"sender": "assistant", "content": "Hello, how can I assist you?"},
    {"sender": "user", "content": "How are you?"}
]

model = "nlite"  # options: nlite, npro, npro-vision

# Get the response
try:
    response = client.generate(conversation_history, model)
    print(response)
except Exception as e:
    print(f"An error occurred: {e}")
```

## 🎨 Image Usage
Using the Image generation AIs library is straightforward. Below is an example of a conversation:
```python
import neuranet

api_key = "YOUR_API_KEY_HERE"
mode = 'image'
client = neuranet.client(api_key, mode)
model = "vinci-mini" # options: vinci-mini, vinci-max

prompt = "A large oak tree"

response = client.generate(prompt, model)
print(response)
```