semvec/pypi-packages huggingface dataset:
webpredict 0.0.4
Python Wrapper to identify or predict sensitive content from web platforms
License: MIT
Requires Python: >=3.6.0

## webpredict
Python package to identify offensive web content (images/text/video)

## Platforms

Linux, MacOS

## Installation

Run the following to install:

```python
pip install webpredict
```

## Download the models

Open the Terminal
```
wget https://www.dropbox.com/s/cwfrocu5hcvleo4/pkl.zip && unzip "pkl.zip"
```

## Usage

## Web content: Img
```python
from webpredict import get_preds_img

# Predict the labels for a sample image!
get_preds_img("sample-image.png")

## Sample JSON output
{                                                                                                                                
   "confidence": {
      "overall": {
         "genre": "Normal",
         "score": "98.163",
         "severity": "Low"
      },
      "individual": {}
   },
   "type": "Image",
   "censored": "No",
   "approved": "Yes"
}
```

## Web content: Text
```python
from webpredict import get_preds_text

# Predict the labels for a sample image!
get_preds_text("text")

## Sample JSON output
{                                                                                                                                
   "type": "text",
   "confidence": {
      "overall": {
         "genre": "bullying",
         "score": "51.489%",
         "sentiment": "negative",
         "severity": "High"
      },
      "individual": {}
   },
   "censored": "Yes",
   "approved": "No"
}
```

## Web content : Video

```python
from webpredict import get_preds_video

# Predict the labels for a sample image!
get_preds_video("video_file.mp4")

## Sample JSON output

{                                                                                                                                
   "confidence": {
      "overall": {
         "genre": [
            "Normal"
         ],
         "score": 1.0,
         "graphic_duration": 12.43,
         "graphic_start_seek": 0,
         "graphic_end_seek": 12.43,
         "severity": "Low",
         "total_duration": 12
      },
      "individual": []
   },
   "type": "video",
   "sensored": "no",
   "approved": "yes"
}
```
idservice 1.2.3
Distributed ID generator microservice based on FastAPI
License: MIT
Requires Python: >=3.7,<4.0

# idservice

`ID-Service` is a distributed ID generator microservice based on FastAPI.

## Installation

```sh
pip3 install idservice
```

## Start Service

```sh
idservice-start
```

> List all available `uvicorn` options by `idservice-start --help`

## API Endpoint

Several endpoints:

-   `/api/snowflake`
-   `/api/random/64`
-   `/api/uuid`

Browse `/docs` (default to http://localhost:8000/docs) to read all APIs.

## Configuration

Environment variable:

| Environment Vars                  | Usage                          | Default |
| --------------------------------- | ------------------------------ | ------- |
| `ID_SERVICE_SNOWFLAKE_MACHINE_ID` | Snowflake Machine ID (10 bits) | Random  |
hyperkv 0.1.1
Reads from Hyper-V KV on a Linux guest
License: MIT

# hyperkv
## Get Hyper-V Key Value Pairs on Linux Guest

Simple parser of the files made by `hv_kvp_daemon` on a Linux guest. This allows a guest to determine its name, the host's name, etc. [Learn more about Hyper-V Data Exchange here](https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/dn798287(v=ws.11)#linux-guests).

## Install
```sh
pip install hyperkv
```

## Usage
```sh
hyperkv
```
will write the contents as a JSON to standard out like this:
```json
{
  "HostName": "thehost.example.com",
  "HostingSystemEditionId": "8",
  "HostingSystemNestedLevel": "0",
  "HostingSystemOsMajor": "10",
  "HostingSystemOsMinor": "0",
  "HostingSystemProcessorArchitecture": "9",
  "HostingSystemProcessorIdleStateMax": "0",
  "HostingSystemProcessorThrottleMax": "100",
  "HostingSystemProcessorThrottleMin": "5",
  "HostingSystemSpMajor": "0",
  "HostingSystemSpMinor": "0",
  "PhysicalHostName": "THEHOST",
  "PhysicalHostNameFullyQualified": "thehost.example.com",
  "VirtualMachineDynamicMemoryBalancingEnabled": "0",
  "VirtualMachineId": "461B2364-3901-4349-B3C2-FA4821CBEEFE",
  "VirtualMachineName": "my-guest-vm"
}
```
By default, this script reads from `/var/lib/hyperv/.kvp_pool_3`, but you can specify a different path with `-f`
```sh
hyperkv -f /var/lib/hyperv/.kvp_pool_2
```

## Requirements
`hv_kvp_daemon` must be installed and working. If it is working, you will find files in `/var/lib/hyperv`.

### Ubuntu
```sh
apt-get install linux-azure linux-azure-tools
grub-update
```
At boot time, you need to pick advanced startup and make sure it boots with the newly installed Azure kernel. To make that more permanent, you can open `/etc/default/grub`, add the line `GRUB_SAVEDEFAULT=true`, and change `GRUB_DEFAULT=0` to `GRUB_DEFAULT=saved`. The next time you boot, choose the Azure kernel and that will become the default until you pick a different one at the GRUB menu.