shaurya-blip

INSTALLED_APPS = [
    'main',
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
]

shaurya-blip

from django.db import models

# Create your models here.

class Book(models.Model):
    title = models.CharField(max_length=150)
    author = models.CharField(max_length=100)

shaurya-blip

from django.contrib import admin
from .models import Book
# Register your models here.

admin.site.register(Book)

shaurya-blip

INSTALLED_APPS = [
    'main',
    ...
    'rest_framework'
]

shaurya-blip

from rest_framework import viewsets

from .serializer import BookModelSerializer
from main.models import Book


class BookViewSet(viewsets.ModelViewSet):
    queryset = Book.objects.all()
    serializer_class = BookModelSerializer

shaurya-blip

from django.urls import include, path
from rest_framework import routers
from .views import BookViewSet


router = routers.DefaultRouter()
router.register(r'books', BookViewSet)

urlpatterns = [
    path('', include(router.urls)),

shaurya-blip

from rest_framework import serializers

from main.models import Book


class BookModelSerializer(serializers.ModelSerializer):
    class Meta:
        model = Book
        fields = '__all__'
        

shaurya-blip

import requests

def create_book(title, author):
    headers={
        'title':title,
        'author':author
    }
    response = requests.post('http://127.0.0.1:8000/books/', headers)
    print(f"New book {title} by {author} has been created,\n\n")

shaurya-blip

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql_psycopg2', 
        'NAME': 'sample', # DATABASE NAME
        'USER': 'postgres', # NAME OF USER (DEFAULT IS postgres)
        'PASSWORD': 'xyz', # whatever password you have kept.
        'HOST': '127.0.0.1', # HOST in which the psql server is running on
        'PORT': '5432', # PORT in which the psql server is running on
    }
}

shaurya-blip

# Coefficients
print('Coefficients: \n', regressor.coef_)
# The mean squared error
print("Mean squared error: %.2f" % np.mean((regressor.predict(X_test) - y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regressor.score(X_test, y_test))