semvec/pypi-packages huggingface dataset:
selfrevolve 0.1.3
A project for generating RESTful apis given a database
Requires Python: >=3.11

## 🚀 AI-Powered Database CRUD API & UI Generator  
#### _Instantly generate full-stack admin panels, APIs, and UI from your database schema—powered by AI agents & LLMs_

[![PyPI version](https://img.shields.io/pypi/v/selfrevolve.svg?color=blue)](https://pypi.org/project/selfrevolve/)
[![License](https://img.shields.io/github/license/self-evolving-runtimes/revolve)](https://github.com/self-evolving-runtimes/revolve/blob/main/LICENSE)
[![Model](https://img.shields.io/badge/model-Mixtral%20%F0%9F%94%93-brightgreen)](https://huggingface.co/kramster/evolve-mistral)
[![Open Source](https://img.shields.io/badge/open--source-yes-brightgreen)](https://github.com/self-evolving-runtimes/revolve)

---

**⚡ Automate 80% of your boilerplate code** – Say goodbye to manual CRUD coding! Let AI generate clean, production-ready database interfaces in seconds.

- **✨ AI Agents** analyze your schema and generate optimized REST APIs  
- **🖥️ Auto-generated Admin UI** with filters, search, and CRUD operations  
- **🔌 Multi-DB Support** (SQL & NoSQL) with intelligent schema detection  
- **🎨 Customizable Templates** (React + MUI DataGrid)  
- **📦 One-Codebase Export** for easy integration  

---

### 🌟 New: Open-Source Model Support (Mixtral)  
We now support a fine-tuned **Mixtral-based open model**, delivering performance almost at **on par with GPT‑4.1** —for Revolve use cases.

✅ Open weights, self-hostable  
✅ No OpenAI keys or API fees  
✅ Matches GPT‑4.1-level quality in schema-driven generation and agent workflows

Use it directly through the UI — just select the `mixtral-finetuned` model at runtime.  
🔗 [See the model on Hugging Face →](https://huggingface.co/kramster/evolve-mistral)

> Enterprise-grade quality without vendor lock-in.

---

### 🚀 Perfect for:
- Anyone wanting to quickly interact with their data for admin / internal purposes  
- Devs tired of writing repetitive CRUD code  
- Teams managing complex data without dedicated frontend resources  
- AI engineers who want database UIs without full-stack work  

#### Supported Databases  
✅ MongoDB • ✅ PostgreSQL • 🔜 MySQL • 🔜 Redis • 🔜 Cassandra ⚪ DynamoDB  

---
![Revolve](https://raw.githubusercontent.com/self-evolving-runtimes/revolve/refs/heads/main/screenshots/animated.gif)

---

### 📦 Installation

Install the package from PyPI:

```sh
pip install selfrevolve
```

Run the API and UI:

```sh
revolve-api
```

For local development instructions, see [contributor-README.md](https://huggingface.co/kramster/evolve-mistral).

---

### 🛣️ Roadmap

- [ ] Authz and Authn configuration  
- [ ] Support for other databases  
- [ ] Support foreign key testing  
- [ ] Enhance UI to support lookups for foreign keys and editing JSON elegantly  
- [X] Support for enums validation via the discovery APIs  
- [X] Multi-turn conversations  
- [X] Fine-tuned open-source model (Mixtral) support alongside paid LLM APIs  

---

### ⚖️ License

MIT
cosyvoice-ttsfrd 0.4.3
A wrapper package to install the ttsfrd package for CosyVoice.
Requires Python: <3.11,>=3.10

# CosyVoice-ttsfrd

This package is a wrapper for the `ttsfrd` library used in CosyVoice. It simplifies installation by bundling the required wheels and resources.

## !! Important

- **Platform**: This package will only work on **64-bit Linux with Python 3.10**.
- **License**: This package redistributes software under the [Apache 2.0 License], which is the original license used in CosyVoice.
- **Purpose**: This is a convenience wrapper. All credit goes to the original authors of CosyVoice and the `ttsfrd` library.

## Installation

```bash
# Step 1: Install this wrapper package
pip install cosyvoice-ttsfrd
# Step 2: After the wrapper is installed, you must run the following command in your terminal. This will install the actual `ttsfrd` libraries and unzip the required resources.
cosyvoice-post-install
```

The installation process will automatically install the bundled `ttsfrd` wheels and unzip the required resources into the package directory. Installation log looks like:

```bash
▶ cosyvoice-post-install
CosyVoice-ttsfrd initialized. Resources are expected in: /mnt/user-ssd/songxingchen/.conda/envs/touchnet/lib/python3.10/site-packages/cosyvoice_ttsfrd/resource
--- Running Post-Installation Setup for CosyVoice-TTSFRD ---
Unzipping resources to /mnt/user-ssd/songxingchen/.conda/envs/touchnet/lib/python3.10/site-packages/cosyvoice_ttsfrd...
Resources unzipped. Package contents:
- __init__.py
- __pycache__
- bundled_files
- post_install.py
- resource
Installing ttsfrd_dependency-0.1-py3-none-any.whl from bundled file...
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Processing /mnt/user-ssd/songxingchen/.conda/envs/touchnet/lib/python3.10/site-packages/cosyvoice_ttsfrd/bundled_files/ttsfrd_dependency-0.1-py3-none-any.whl
Installing collected packages: ttsfrd-dependency
Successfully installed ttsfrd-dependency-0.1
Installing ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl from bundled file...
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Processing /mnt/user-ssd/songxingchen/.conda/envs/touchnet/lib/python3.10/site-packages/cosyvoice_ttsfrd/bundled_files/ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl
Installing collected packages: ttsfrd
Successfully installed ttsfrd-0.4.2
--- ✅ Post-Installation Finished Successfully! ---
You can now use the 'ttsfrd' module in your Python scripts.
```

## Usage

After installation, you can import and use `ttsfrd` as you normally would. If you need to access the unzipped resources programmatically, you can do so like this:

```py
import json
import ttsfrd
from cosyvoice_ttsfrd import get_resource_path

resource_dir = get_resource_path()
print(f"Resources are located at: {resource_dir}")

frd = ttsfrd.TtsFrontendEngine()
frd.initialize(resource_dir)
frd.set_lang_type('pinyinvg')

text = "嘿，你是不是在纠结，家里到底是该买个55~60的投影仪呢，还是整个大屏幕电视啊？这俩玩意儿确实各有各的好，选哪个主要就看你的钱袋子、平时都用它干啥、对画质要求高不高，还有家里地方够不够大。"
texts = [i["text"] for i in json.loads(frd.do_voicegen_frd(text))["sentences"]]
text = ''.join(texts)
print(text)
```
content-analysis-tool 0.1.4
Phân tích nội dung và nhận diện thương hiệu, chủ đề

# TikTok Collector

A Python library for collecting TikTok data including hashtags and keywords.

## Installation

```bash
pip install content-analysis
```

## Features

## Usage

### Basic Usage

# Collect posts by keyword


### Configuration

You can configure the collector using environment variables:

```bash
export TIKTOK_API_KEY=your_rapidapi_key
```

## Requirements

- Python 3.7+
- requests
- pandas
- numpy
- python-dotenv
- boto3
- pytz
- httplib2
- sqlalchemy
- openpyxl
- pyspark
- hadoop-aws
- aws-java-sdk-bundle

## License

This project is licensed under the MIT License - see the LICENSE file for details.
ed-optimization-service 0.2.2
EasyDrop auth model Package
Requires Python: >=3.12
Keywords: ed_optimization, pypi, package

# py
Template class for python backend servers.

## Features
- [x] RabbitMQ
- [x] Logging
- [X] Generic Helpers
- [ ] Kafka
- [ ] Redis
- [x] API: RequestHandler, FastAPI
- [x] Dockerfile
- [x] Makefile
- [x] CI/CD
- [x] Entry point file
- [x] Configfile / Configuration
- [x] Websockets

## Usage
The project's dependencies are managed by `poetry`. You can install the dependencies with:
```bash
poetry install
```

`package.py` is the entry point file.

### Running the package
You can run the package inside of a virtual environment:
```bash
poetry shell
make run
```

or with `docker`:
```bash
make run_docker
```


### Running the tests
You can run the tests inside of a virtual environment:
```bash
poetry shell
make test
```
or with `docker`:
```bash
make test_docker
```