semvec/pypi-packages huggingface dataset:
jpzipcode 0.3.0
日本の郵便番号から住所を取得するAPI
Requires Python: >=3.12

# jpzipcode
郵便番号から住所を取得するためのライブラリ


## Usage

```python
import os

from jpzipcode import OFFICIAL_FILE_NAME, read_csv_file, read_official_URL

zip_code = "5650871"

# ファイルを読み込んで取得
resolver = read_csv_file(os.path.join("tests", "testdata", OFFICIAL_FILE_NAME))
zips = resolver.resolve(zip_code)

# 日本郵便のサイトからダウンロードして郵便番号を取得
resolver = read_official_URL()
zips = resolver.resolve(zip_code)

print(zips)
# [Zip(zip_code='5650871', address=Address(prefecture='大阪府', city='吹田市', town='山田丘'), address_kana=Address(prefecture='オオサカフ', city='スイタシ', town='ヤマダオカ'))]


from fastapi import FastAPI
from fastapi.testclient import TestClient

from jpzipcode.fastapi.endpoint import create_endopoint

app = FastAPI()

# appに郵便番号解決エンドポイントを追加
app.get("/zip")(create_endopoint())

client = TestClient(app)
res = client.get(f"/zip?zipcode={zip_code}")
result = res.json()
assert res.status_code == 200
assert result[0]["address"]["prefecture"] == "大阪府"
assert result[0]["address"]["city"] == "吹田市"
assert result[0]["address"]["town"] == "山田丘"
```
kalapaocr 0.0.6
A boiler plate to make pip package for AI model
Requires Python: >=3.8

# KALAPA OCR Package

This is a Package ```kalapaocr``` Model ONNX. The package will simply load onnx model and init an ocr engine. Give an image path and the engine will return text result.

## Install Locally

After git clone, you can access the codebase and simply run the following command line:

```
conda create -n kalapa_env python=3.8
conda activate kalapa_env
pip install -e .
```

After installed, you can import ```kalapaocr``` package at anywhere when you are in ```kalapa_env``` environment

### Download models
```
python src/kalapaocr/tool/model_downloads.py -p cached/
```
## Basic Usage

After installing, You can view examples/sample.py to get usage of ```kalapaocr``` lib

You can run ```examples/sample.py``` file as following:

```
python example/sample.py -cnn cached/cnn.onnx -en cached/encoder.onnx -de cached/decoder.onnx -i 'image/test.jpg'
```

You can run ```examples/create_submission.py``` file as following for creating submission:

```
python example/create_submisson.py -cnn cached/cnn.onnx -en cached/encoder.onnx -de cached/decoder.onnx -i "your local data path/OCR/public_test" -o "your local path/your file name.csv"
```

## Config TextRecognitor

You can initialize ```TextRecognitor``` from ```kalapaocr```.

Let us show you:
```
from kalapaocr import TextRecognitor
# Using Deep Learning Models to Extract ocr results
predictor = TextRecognitor(
                        cnn_path="your cnn model path",
                        encoder_path="your encoder model path",
                        decoder_path="your decoder model path",
                    )
img = cv2.imread("your image path")
s = predictor(img)
print(s)
```
drf-privatbank 0.0.7
This module is designed for quick interaction with the privatbank API.
Requires Python: >=3.6
Keywords: files speedfiles

# drf-privatbank
This module provides quick integration of the Privatbank API for applications in the Django REST Framework.

## Installation
This framework is published at the PyPI, install it with pip:

    pip install drf_privatbank

Django REST Framework application, you need to install and configure drf_privatbank. To get started, add the following packages to INSTALLED_APPS:

    INSTALLED_APPS = [
        ...
        'rest_framework',
        'drf_privatbank',
    ]

Include social auth urls to your urls.py:

    urlpatterns = [
        ...
        path('privatbank/', include('drf_privatbank.urls', namespace='drf_privatbank')),
    ]

## Usage
1. First, install the "Autoclient" module of the "Privat24 for Business" complex designed to serve corporate clients and private entrepreneurs."Autoclient" is software that allows you to set up periodic automatic receipt of statements / account balances and import payments into Privat24.
2. Finally, use this token and your account's iban to retrieve your personal data and create payments.