開発スタッフがアプリ関連の最新技術やニュースをご紹介します。

Sometimes, we need dummy data to add in our projects before the production or you just want to create a fun project with some fun data. Today I will share some interesting data resources , also APIs you can try.

Movies API

The API service is intended for anyone interested in using images and data from movies, TV shows, and actors in their applications. Our API is a system provided by you and your team to acquire and use data and images programmatically.

API : API Overview — The Movie Database (TMDB) (themoviedb.org)

Meals API

The meal API allows you to access random food data and use it in your application. This API includes all meal categories including images, materials, videos, country of each meal and many other features.

API : TheMealDB.com

Jokes API

The random jokes API allows you to fetch random jokes data in an easy way using Fetch, Ajax XHR and etc.

API : icanhazdadjoke

Unsplash API

Unsplash is a website that hosts “beautiful and free images and photos that you can download and use for any project.” You can use that API to fetch photos from Unsplash. You can use the Unsplash API to create an application with a background that changes to a random image at different times of the day.

API : Unsplash Image API | Free HD Photo API

Marvel API

The Marvel Comics API gives developers access to information about Marvel’s vast comic library from anywhere.

API : Marvel Developer Portal

I am sure there are other interesting APIs around the world but that is all from me now. Enjoy the data.

Yuuma

This time I would like to share about to create flutter design in mobile apps easily and quickly drag and drop. This name is flutter flow site that I found out.

Let’s take a look the site!

Firstly, we will create free account and after creation complete, we will see this page.

In the left side activity bar, we will see all of widgets, layouts, most popular form design etc. that you want to desire creation.

After running project or without running project, you can get your creation design code.And also if you want to get apk that also available. The below image is my creation of flutter code simple.

Hope you enjoyed my sharing article!

By Ami

今回はLaravelでPDF出力をおこなうためのライブラリ「laravel-dompdf」の導入方法と
使用方法について紹介したいと思います。

必要なファイルのインストール

laravel-dompdf
https://github.com/barryvdh/laravel-dompdf

プロジェクトのルートディレクトリに移動をおこない
以下のコマンドを実行します。

composer require barryvdh/laravel-dompdf

※この操作をおこなうためにはcomposer 2のインストールが必要です。

コントローラー側の出力処理

PDFのレイアウトはbladeテンプレートのViewを作成するのと同じ要領でhtml/cssを使用して作成できます。
以下のようにテンプレートのViewを指定します。

$pdf = \PDF::loadView('pdf_template');

生成されたPDFをダウンロードさせるには以下の記述をおこないます。
以下の場合、ファイル名が「generated_pdf.pdf」のPDFファイルがダウンロードされます。

return $pdf->download('generated_pdf.pdf');

viewの準備

htmlのbladeテンプレートViewと同様に以下の階層にbladeテンプレートを作成します。
\src\resources\views\pdf_template.blade.php

ページレイアウトはhtml/cssを使用して作成します。
PDFの改ページにはcssの page-break-after: always が使用できます。

<style>
    hr { 
        page-break-after: always; 
    }
</style>

PDFに日本語を表示する

標準の状態では日本語の表示に対応していませんので、
日本語用フォントを手動でインストールする必要があります。

今回はオープンソースの日本語フォント「IPAexフォント」を使用する方法を説明します。

IPAexフォントおよびIPAフォントについて
https://moji.or.jp/ipafont/

IPAexフォントは以下よりダウンロード可能です。
https://moji.or.jp/ipafont/ipafontdownload/

上記URLからダウンロードしたフォントファイル「ipag.ttf」を以下に配置します。

\src\storage\fonts\ipag.ttf

viewでは以下のように記述してフォント指定をおこないます。

<style>
    @font-face {
        font-family: ipag;
        font-style: normal;
        font-weight: normal;
        src: url('{{ storage_path('fonts/ipag.ttf') }}') format('truetype');
    }
    @font-face {
        font-family: ipag;
        font-style: bold;
        font-weight: bold;
        src: url('{{ storage_path('fonts/ipag.ttf') }}') format('truetype');
    }
</style>

木曜日担当：nishida

Today, I would like to share about deploying a machine learning model into a web application using Django.

Note : In this article, I will focus only how to deploy a trained machine learning model in a web application with django rather than about the steps of machine learning processes.

First, I will create a simple ML model using multinomial Naive Bayes Classifier with the spam text dataset (spam.csv). The following code is to create spam text detection model.

First, import the necessary libraries.

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB

---

And load dataset file with pandas.

data = pd.read_csv("D:\Python\datasets\spam.csv", encoding= 'latin-1')
data.head()
Then extract features and labels using CountVectorizer.
data = data[["class", "message"]]
x = np.array(data["message"])
y = np.array(data["class"])
cv = CountVectorizer()
X = cv.fit_transform(x) # Fit the Data

---

let’s split this dataset into training and test sets and train the model to detect spam messages

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
clf = MultinomialNB()
clf.fit(X_train,y_train)

---

And save the model.

import pickle
pickle.dump(clf,open("spamdetection_model.sav", "wb"))

This will output spamdetection_model.sav file. This file will be used in the web app later.

---

So, Let’s start to develop a Django web app.

mkdir Spamdetection – First make a directory named /Spamdetection for the Django project.

cd Spamdetection – Change directory into the folder created

And to create a Django project, it is needed to run the following command.

D:/Spamdetection>django-admin startproject spamdetectapp

This command create a new directory named spamdetectapp.

To run the djangoapp, the following command is typed in the spamdetectapp directory created.

python manage.py runserver

Copy and open the link http://127.0.0.1:8000/ in a web browser. You should see Django Home page.

Then create views.py inside the same folder to work for getting inputs from users. In the main project folder, create a new folder named ‘templates’ to work with html files and new folders named ‘datasets’ and ‘mlmodels’. Then move spam.csv dataset file into the datasets folder and spamdetection_model.sav file into the mlmodels folder.

Now our project folder will be like that.

Now open settings.py and add ‘templates’ to register in ‘DIRS’ list in the ‘TEMPLATES’ list.

---

Now inside the urls.py file, add the following codes to configure the urls.

from django.contrib import admin
from django.urls import path

from spamdetectapp import views # add this new line to import views files

urlpatterns = [
    path('admin/', admin.site.urls),
    
    # add these new two lines to configure for home page and result page
    path('', views.home, name='home'), 
    path('result/', views.result, name='result'),
]

---

And define two functions for home and result in views.py. And create a libs.py in mlmodels folder. In libs.py, we will define a getResult() function to get results from model prediction and use it in views.py.

views.py

from django.shortcuts import render
from mlmodels.libs import getResult

# for default home page view
def home(request):    
    return render(request, 'index.html')

# for result page view
def result(request):	
    message = request.POST['message']
    result = getResult(message)
    return render(request, 'result.html', {'result':result , 'message':message})

---

libs.py

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split

def getResult(message):
    import pickle
    data = pd.read_csv("D:\Spamdetection\spamdetectapp\datasets\spam.csv", encoding= 'latin-1')
    data.head()

    data = data[["class", "message"]]
    x = np.array(data["message"])
    y = np.array(data["class"])
    cv = CountVectorizer()
    X = cv.fit_transform(x) # Fit the Data

    model= pickle.load(open("D:\Spamdetection\spamdetectapp\mlmodels\spamdetection_model.sav", "rb"))

    data = cv.transform([message]).toarray()    
    prediction = model.predict(data)
    if prediction == 'ham':
        return 'OK'
    elif prediction == 'spam':
        return 'SPAM'

---

Now we have done for backend. So, for frontend pages, let’s create index.html and result.html in templates folder.

Index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Spam Text Detection</title>
</head>
<body>
    <h1>Spam Text detection</h1>
    <form action="{% url 'result' %}" method="post">
        {% csrf_token %}
        <p>Message:</p>
        <input type="text" name="message">
        <br>
        <br>
        <input type="submit" value='Predict’>
    </form>
</body>

</html>

---

result.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Spam Text Detection</title>
</head>
<body>
    <h1>Prediction</h1>
    The Message[{{message}}] is <b>{{result}}</b></body>
</html>

---

Now all is done. Once the above steps are completed, to stop and restart the server press Ctrl+C and run this command:

D:/Spamdetection/spamdetectapp> python manage.py runserver

And reopen the link in the browser. You will see the index home page.

Let’s test with some messages.

When message is ‘Hello, How are you?’, the result is as follow.

When the message is ‘You got $1000 prize at our shop.’, the result is as follow.

Hope you all enjoyed about this article.

By Asahi

Tweet