semvec/pypi-packages huggingface dataset:
fastapi-logging 0.1.1
Logging Wrapper for fastapi with Cloud Logging
Requires Python: >=3.6

# Wrapper para trabalhar com a API do Cloud Logging

## Procedimento para upload no Pypi

### Pré requisitos
Instalar pacotes python
`python3 -m pip install --upgrade setuptools build wheel twine`

### Build e enviar ao PYPI

`python3 -m build`

`python3 -m twine upload dist/*`


### Instalar o pacote no projeto

`pip install fastapi-logging`


## Utilização da biblioteca

Essa biblioteca espera que você passe o schema dos dados da tabela

Aconselho criar um arquivo de schema.py ou model.py onde seja definido a estrutura de dados da tabela.

### Instanciar o Middleware

Com o schema definido, você deve instanciar um classe

```python
from starlette.middleware import Middleware
from fastapi.middleware.cors import CORSMiddleware
from starlette_context.middleware.raw_middleware import RawContextMiddleware
from fastapi_logging.logging import LogMidddleware
from fastapi_logging.logging import logging
from fastapi_logging.logging import TracePlugin
from starlette_context import context, plugins

middlewares = [
    Middleware(
        RawContextMiddleware,
        plugins=(
            plugins.CorrelationIdPlugin(),
            TracePlugin(),
            plugins.RequestIdPlugin(),
        ),
    ),
    Middleware(LogMidddleware),
    Middleware(CORSMiddleware,
    
    allow_origins=BACKEND_CORS_ORIGINS,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],)
]

app = FastAPI(
    title='Iris Typification', 
    openapi_url="/api/v1/openapi.json",
    middleware=middlewares
    )

```

### Principais operações

Usando a classe, você pode executar algumas operações para auxiliar no densenvolvimento

Logging

`logging.info({"key": "value"})`
NumPlex 1.0.0
Complex numbers compatable with numpy
Requires Python: >=3.6

NumPlex Numpy Compatable Complex Library
* operators (+ - * / ~)

Examples
	from numplex import complex, ctp, ctr
	print(complex(3,3)+complex(1,1))
	–––
	from numplex import complex, ctp, ctr
	print(ctr(complex(3,3)))
roboflowtest 0.0.2
Ergonomic machine learning
Requires Python: >=3.6

# Roboflow Python Library

---
![roboflow logo](https://i.imgur.com/lXCoVt5.png)

[Website](https://roboflow.com) • [Docs](https://docs.roboflow.com) • [Blog](https://blog.roboflow.com)
• [Twitter](https://twitter.com/roboflow) • [Linkedin](https://www.linkedin.com/company/roboflow-ai)
• [Universe](https://universe.roboflow.com)

**Roboflow** makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. This is
the official Roboflow python package that interfaces with the [Roboflow API](https://docs.roboflow.com). Key features of
Roboflow:

- Import and Export image datasets into any supported [formats](https://roboflow.com/formats)
- [Preprocess](https://docs.roboflow.com/image-transformations/image-preprocessing)
  and [augment](https://docs.roboflow.com/image-transformations/image-augmentation) data using Roboflow's dataset
  management tools
- Train computer vision models using [Roboflow Train](https://docs.roboflow.com/train) and deploy
  to [production](https://docs.roboflow.com/inference)
- Use [community curated projects](https://universe.roboflow.com/) to start building your own vision-powered products

## Installation:

To install this package, please use `Python 3.6` or higher. We provide three different ways to install the Roboflow
package to use within your own projects.

Install from Pypi (Recommended):

```
pip install roboflow
```

Install from Conda:

```
conda install roboflow
```

Install from Source:

```
git clone https://github.com/roboflow-ai/roboflow-python.git
cd roboflow-python
python3 -m venv
source venv/bin/activate 
pip3 install -r requirements.txt
```

## Quickstart

```python
import roboflow

# Instantiate Roboflow object with your API key
rf = Roboflow(api_key=YOUR_API_KEY_HERE)

# List all projects for your workspace
workspace = rf.workspace()

# Load a certain project, workspace url is optional
project = rf.project("PROJECT_ID")

# List all versions of a specific project
project.versions()

# Upload image to dataset
project.upload("UPLOAD_IMAGE.jpg")

# Retrieve the model of a specific project
project.version("1").model

# predict on a local image
prediction = model.predict("YOUR_IMAGE.jpg")

# Predict on a hosted image
prediction = model.predict("YOUR_IMAGE.jpg", hosted=True)

# Plot the prediction
prediction.plot()

# Convert predictions to JSON
prediction.json()

# Save the prediction as an image
prediction.save(output_path='predictions.jpg')
```