semvec/pypi-packages huggingface dataset:
LynqX 1.0.6
A Textual-based terminal chat client offering real-time messaging, file sharing, typing indicators, and emoji support.
License: MIT
Requires Python: >=3.12

# LynqX

**LynqX** is a terminal-based peer-to-peer chat client built using [Textual](https://textual.textualize.io/). It supports:

* Real-time messaging
* File sharing (with resume and cancel support)
* Typing indicators
* Emoji rendering
* Reconnection logic
* Custom commands

LynqX connects to a backend server (e.g., [LynqX server](https://github.com/SwagCoder18/ChatX)) for managing chat rooms and delivering messages over Server-Sent Events (SSE).

---

## 🚀 Features

* 📦 File transfers with resume capability
* 🔒 Cancel transfers anytime
* 🧠 Reconnect logic with exponential backoff
* 😃 Emoji support via `emoji` Python package
* 🧑‍💻 Typing indicators for active participants
* 📿 Textual UI for a clean terminal interface

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## 📆 Installation

```
pip install lynqx
```

This will automatically install all required dependencies:

* `textual`
* `httpx`
* `requests`
* `sseclient`
* `emoji`

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## 🧑‍💻 Usage

### Create a new chat room:

```
lynqx -c
```

### Join an existing room:

```
lynqx -j <room_id>
```

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## ⚙️ Requirements

* Python 3.8+

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## 🧠 Customization

* You can change the base server URL in `app.py` via the `BASE_URL` constant.
* For advanced use, fork or modify the [LynqX server backend](https://github.com/SwagCoder18/ChatX).

---

## 📄 License

MIT — see the [LICENSE](./LICENSE) file for details.
tdd-chatbot 0.1.0
Voice-based chatbot using Ollama LLM with TDD
License: MIT
Requires Python: ==3.11.0

# 🎙️ Ollama Voice Q&A Bot

> 🎯 **A voice-activated chatbot that answers only data science-related questions using Ollama (Mistral LLM).**
>
> Powered by **Streamlit**, **Speech Recognition**, and **Test-Driven Development (TDD)**.

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## ✨ Features

✅ Ask your question with your voice  
✅ Responses only for **data science-related** queries  
✅ Built-in **Text-to-Speech** replies  
✅ Detects non-English and politely declines  
✅ Locally hosted LLM via **Ollama + Mistral**  
✅ Unit tested with **TDD** practices

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## 🛠️ Tech Stack

| Category           | Tools / Libraries Used                            |
|-------------------|---------------------------------------------------|
| UI                | Streamlit                                         |
| Speech-to-Text    | `speech_recognition`                              |
| Text-to-Speech    | `pyttsx3`                                         |
| LLM               | [Ollama](https://ollama.com) with `mistral` model |
| Language Detection| `langdetect`                                      |
| Testing           | Pytest + Mocking                                  |

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## 🧪 Test-Driven Development (TDD)

This project follows TDD principles:
- Write failing tests first
- Build functionality to pass them
- Refactor and improve

📁 Tests included:
- `test_llm.py`: Ensures LLM answers are relevant
- `test_voice.py`: Mocks voice input and checks result
- `test_language.py`: Validates correct language detection

---