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45 nuggets found
Encapsulating Queries in a Custom Django Manager (Premium)
Sprinkling filter(is_published=True) across your codebase invites bugs. A custom manager gives your model expressive, reusable query methods like Article.objects.published().
from django.db import models
from django.utils import timezone
class ArticleQuerySet(models.QuerySet):
def published(self):
return self.filter(is_published=True, published_at__lte=timezone.now())
def by_author(self, user):
return self.filter(author=user)
class Article(mode...Reacting to Model Events with Django Signals (Premium)
Need a profile created automatically whenever a user signs up? Signals let you run code in response to model events without cluttering your save logic.
from django.db.models.signals import post_save
from django.dispatch import receiver
from django.contrib.auth import get_user_model
from .models import Profile
User = get_user_model()
@receiver(post_save, sender=User)
def create_profile(sender, instance, created, **kwargs):
if created:...Cleaner Data Objects with Python Dataclasses: Strict Typing & Immutability
Replacing Plain Dictionaries with Type-Safe Python Dataclasses Relying on loose dictionaries across Python applications causes runtime KeyError exceptions. Python's built-in dataclasses module provides an elegant, memory-efficient domain model structure. frozen=True:...
from dataclasses import dataclass, field
from datetime import datetime
from decimal import Decimal
from typing import Optional
@dataclass(frozen=True, slots=True, kw_only=True)
class MpesaTransactionRecord:
transaction_id: str
phone_number: str
amount: Decimal
account_reference: str...Concurrent HTTP Requests with AsyncIO and HTTPX in Python
Speeding Up Batch API Integrations with Asynchronous Concurrency When fetching data from 500 external endpoints sequentially, execution takes hundreds of seconds. Using asyncio and httpx, requests run concurrently in parallel...
import asyncio
import httpx
from typing import List, Dict, Any
async def fetch_status(client: httpx.AsyncClient, semaphore: asyncio.Semaphore, url: str) -> Dict[str, Any]:
async with semaphore:
try:
resp = await client.get(url, timeout=10.0)
return {'url': url, '...Streaming Large Files with Python Generators (Premium)
Loading a multi-gigabyte file into memory will crash your process. A generator yields one chunk (or line) at a time, so memory stays flat no matter how big the file...
def read_in_chunks(path, size=1024 * 1024):
"""Yield a file one megabyte at a time."""
with open(path, "rb") as f:
while chunk := f.read(size):
yield chunk
def count_lines(path):
total = 0
for chunk in read_in_chunks(path):
total += chunk.count(b"\n")...A Minimal Image Classifier with Keras (Premium)
You do not need a research lab to train a working classifier. This compact Keras model learns to tell classes apart from a folder of labelled images and reports its...
import tensorflow as tf
from tensorflow.keras import layers, models
model = models.Sequential([
layers.Rescaling(1./255, input_shape=(128, 128, 3)),
layers.Conv2D(32, 3, activation="relu"),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, activation="relu"),
layers.MaxPooling2D(),...Building an Image Preprocessing Pipeline with tf.data (Premium)
Clean, well-batched input is half the battle in machine learning. tf.data loads images from disk, resizes and augments them, then prefetches batches so your GPU never sits idle.
import tensorflow as tf
AUTOTUNE = tf.data.AUTOTUNE
train_ds = tf.keras.utils.image_dataset_from_directory(
"data/train", image_size=(128, 128), batch_size=32, label_mode="int")
augment = tf.keras.Sequential([
tf.keras.layers.RandomFlip("horizontal"),
tf.keras.layers.RandomRotation(0....Serving a Trained Model Behind a REST Endpoint (Premium)
A model is only useful once other apps can call it. FastAPI wraps your saved model in a tiny JSON API: load once at startup, then return predictions on every...
from fastapi import FastAPI, UploadFile
import tensorflow as tf
import numpy as np
from PIL import Image
import io
app = FastAPI()
model = tf.keras.models.load_model("model.keras") # loaded once
CLASSES = ["normal", "review", "urgent"]
@app.post("/predict")
async def predict(file: UploadFile):...Fetching Server State with TanStack Query (React Query) v5
Mastering Server State Synchronization, Caching, and Optimistic Updates Managing server state manually with useEffect causes race conditions and stale UI data. TanStack Query (v5) provides battle-tested declarative caching and query...
import React from 'react';
import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query';
import axios from 'axios';
export const QuotesTable = () => {
const queryClient = useQueryClient();
const { data: quotes, isLoading } = useQuery({
queryKey: ['quotes'],
queryFn: asy...