semvec/pypi-packages huggingface dataset:
txtemo 0.1.0
ONNX-based emotion detection using quantized RoBERTa
Requires Python: >=3.8

# 📦 txtemo

**txtemo** is a lightweight, production-ready Python package for sentiment/emotion detection using a quantized RoBERTa model exported to ONNX.  
It runs fast on CPU, making it suitable for local apps, servers, and even lightweight devices.

---

## 🚀 Features

- ✅ Quantized RoBERTa ONNX model for speed & efficiency
- ✅ Runs fully on CPU (no GPU required)
- ✅ Easy-to-use Python API
- ✅ Hugging Face Hub integration (auto-downloads model + tokenizer)
- ✅ Returns both labels (Positive/Negative/Neutral) and confidence score

---

## 📥 Installation

```sh
pip install txtemo
```

---

## 📝 Usage

```python
from txtemo import predict

print(predict("I love this AI model!"))  
# ('Positive 😃', 0.92)

print(predict("This is the worst thing ever."))  
# ('Negative 😡', 0.89)

print(predict("Pranesh"))  
# ('Neutral 😐', 0.75)
```

---

## 🖥️ Command Line Interface (CLI)

You can also use txtemo directly from the command line:

```sh
txtemo "This library is amazing!"
# Output: Positive 😃 (0.92)
```

---

## 📊 Labels

- Negative 😡
- Neutral 😐
- Positive 😃

---

## ⚡ Performance

- **Model:** RoBERTa-base (quantized, ONNX)
- **Average inference speed:** ~3x faster than PyTorch version
- **Memory footprint:** Reduced by 50%+

---

## 🌍 Use Cases

- Chatbots 🤖
- Customer feedback analysis 📢
- Social media monitoring 📱
- Product reviews sentiment 🛒

---

## 🔗 Model Source

Hosted on Hugging Face Hub:  
[PraneshJs/Emotion-detection-Text](https://huggingface.co/PraneshJs/Emotion-detection-Text)

---

## 📌 Author

**Pranesh S**  
📧 Contact: [praneshmadhan646@gmail.com]
tiny-retinaface 0.1.0
A fast and simplified implementation of biubug6/Pytorch_Retinaface inference module
Requires Python: >=3.9
Keywords: face-detection, retinaface

# Tiny RetinaFace

![ci](https://github.com/ddfabbro/tiny-retinaface/actions/workflows/ci.yaml/badge.svg)
![python](https://img.shields.io/badge/python-3.9+-blue)
![license](https://img.shields.io/badge/license-MIT-blue)

Tiny RetinaFace is a fast and simplified implementation of [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) inference module

## Installation

```
pip install tiny-retinaface
```

## Usage

```python
from retinaface import read_image, detect_faces

# Read image as RGB ndarray
image = read_image("images/test.jpg")

# Detect faces as list of Face objects
faces = detect_faces(image)
```

## Face object

```python
Face(
    score=0.9997380375862122,
    x=142,
    y=322,
    width=60,
    height=72,
    landmarks=[
        Point(x=160, y=348),
        Point(x=187, y=345),
        Point(x=177, y=362),
        Point(x=165, y=376),
        Point(x=188, y=374)
    ]
)
```

## Tiny RetinaFace Results on World Largest Selfie

```python
from retinaface import read_image_url, detect_faces, draw_faces

url = "https://github.com/yakhyo/retinaface-pytorch/blob/main/assets/large_selfi.jpg?raw=true"

image = read_image_url(url)
faces = detect_faces(image, score_threshold=0.1, nms_threshold=0.1)

draw_faces(image, faces, filename="images/results.jpg")
```

![largest_selfie](https://github.com/ddfabbro/tiny-retinaface/blob/master/images/results.jpg?raw=true)

## Acknowledgements

- Original RetinaFace implementation: [deepinsight/insightface](https://github.com/deepinsight/insightface)
- Pytorch RetinaFace implementation: [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)
- RetinaFace pretrained onnx weights: [yakhyo/retinaface-pytorch](https://github.com/yakhyo/retinaface-pytorch)

## References

```
@inproceedings{Deng2020CVPR,
title = {RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild},
author = {Deng, Jiankang and Guo, Jia and Ververas, Evangelos and Kotsia, Irene and Zafeiriou, Stefanos},
booktitle = {CVPR},
year = {2020}
}
```

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
ai-assistant-provider 0.0.7
Sub Module for AI Assistant Project
Requires Python: >=3.7

# AI Assistant Provider

Version : 0.0.7
## Installation

```bash
pip install ai-assistant-provider
```

## Usage
```python
from ai_assistant_provider import AiProvider
class MyAiProvider(AiProvider):
    def __init__(self):
        super().__init__()
    # You must implement this method
    @property
    def name(self) -> str:
        return "Some AI Provider"
    
    def ask(self, prompt: str) -> str:
        self.add_message("user", prompt)
        response = f"Response to: {prompt}"
        self.add_message("assistant", response)
        self.answer = response
        return response
# Usage
provider = MyAiProvider()
response = provider.ask("Hello, how are you?")
print(f"Status: {provider.status}")
print(f"Response: {response}")
```