Learn Python Coding

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Convert PDF to structured JSON — in a couple of lines and without hassle! 📄✨ Today, we'll create a mini-service that takes a PDF document, extracts the text from it, and asks GPT to neatly organize the content into sections: title, author, date, and a list of sections. 🚀 First, let's connect the necessary libraries and API key:

import os
from PyPDF2 import PdfReader
from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

Now, let's extract the text from the PDF. We'll loop through all the pages and combine them into a single string:

reader = PdfReader("document.pdf")
text = "
".join(page.extract_text() for page in reader.pages)

Next, we'll send the obtained text to GPT. We'll ask the model to return a structured JSON with the necessary fields:

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": (
            "You are a PDF parser. Return a JSON with the fields: title, author, date, sections. "
            "Each section is an object with name and summary."
        )},
        {"role": "user", "content": text}
    ]
)

Output the result:

structured = response.choices[0].message.content.strip()
print(structured)

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Shuffling without repetitions:

import random

# Initial list of candidates or prizes
participants = ["Alexey", "Maria", "Ivan", "Olga", "Dmitry"]

# 1. Selecting 3 unique winners (sample without replacement)
winners = random.sample(participants, k=3)
print(f"Winners: {winners}") 
# The result is different each time, but there will be no repetitions within the list of winners!

# 2. Shuffling an entire string (creating an anagram)
word = "python"
shuffled_word = "".join(random.sample(word, len(word)))
print(f"Anagram: {shuffled_word}")

# 3. Important difference: random.choices allows repetitions
print(f"With repetitions: {random.choices(participants, k=3)}")

✨ Honest selection and generation of unique sets When it's necessary to implement the logic of prize draws, random task distribution, or generating test questions, developers often use random.choice() in a loop. But this approach requires manually ensuring that the same element is not selected twice. The random.sample function takes on this routine. — Guarantee of uniqueness: The main property of random.sample is "without replacement". The extracted element no longer participates in the next selection cycle, which completely eliminates duplicates in the resulting list. — Safety of the original: The function does not modify the original list (unlike random.shuffle()), but creates a completely new array with the results. This allows the structure of the original data to remain intact. — Strict control of size: If you pass a parameter k (the number of elements) that exceeds the length of the original list, Python will not start duplicating elements and will immediately throw an ValueError error. This protects the program logic from incorrect data. #Python #Random #Coding #NoRepetition #DataScience #UniqueSets ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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Limiting program resources using the resource module 🛡️

import resource
import sys

# 1. Limiting the size of RAM (soft and hard limits in bytes)
# Limit the memory to ~50 MB
memory_limit = 50 * 1024 * 1024 
resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))

# 2. Checking the protection's working
try:
    print("Trying to allocate a huge array of memory...")
    huge_list = [i for i in range(10_000_000)]
except MemoryError:
    print("The limit worked! The program didn't crash, but caught the error.")

# 3. Finding out how many resources the script has already consumed
usage = resource.getrusage(resource.RUSAGE_SELF)
print(f"Peak memory consumption (in KB): {usage.ru_maxrss}")

Protecting the server from "greedy" code 🔧 When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites. — Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds. — File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs. — Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation. #Python #ResourceManagement #ServerSafety #Coding #DevOps #Linux ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

Advice for Python, UV, and Docker 🐍🐳 Sometimes dependencies are better installed separately from the code — this noticeably speeds up the compilation of Docker images 🚀 The idea is simple: first, we install dependencies, then we add the project 🛠 Why is this necessary: • Docker caches layers and does not rebuild them unnecessarily ⚡️ • if only the code changes — the dependencies are taken from the cache 💾 • if the dependencies change — only the corresponding layer is rebuilt 🔁 • without this, any minor change triggers a full reinstallation 🔄 Example:

RUN --mount=type=cache,target=/root/.cache/uv  --mount=type=bind,source=uv.lock,target=uv.lock  --mount=type=bind,source=pyproject.toml,target=pyproject.toml  uv sync --locked --no-install-project

COPY . /app
RUN --mount=type=cache,target=/root/.cache/uv  uv sync --locked

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When you're doing a parser or migrating a site, there's often a pile of unreadable HTML markup on the screen. Converting this into neat Markdown is usually a hassle. In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown. The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result. The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured. It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.

pip install python-markdownify

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