Let's start at the top...

NumPy contains a broad array of functionality for fast numerical & mathematical operations in Python

The core data-structure within #NumPy is an ndArray (or n-dimensional array)

Behind the scenes - much of the NumPy functionality is written in the programming language C

NumPy functionality is used in other popular #Python packages including #Pandas, #Matplotlib, & #scikitlearn!

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Topic: Python Script to Convert a Shared ChatGPT Link to PDF – Step-by-Step Guide

---

### Objective

In this lesson, we’ll build a Python script that:

• Takes a ChatGPT share link (e.g., https://chat.openai.com/share/abc123)
• Downloads the HTML content of the chat
• Converts it to a PDF file using pdfkit and wkhtmltopdf

This is useful for archiving, sharing, or printing ChatGPT conversations in a clean format.

---

### 1. Prerequisites

Before starting, you need the following libraries and tools:

#### • Install pdfkit and requests

pip install pdfkit requests

#### • Install wkhtmltopdf

Download from:
[https://wkhtmltopdf.org/downloads.html](https://wkhtmltopdf.org/downloads.html)

Make sure to add the path of the installed binary to your system PATH.

---

### 2. Python Script: Convert Shared ChatGPT URL to PDF

import pdfkit
import requests
import os

# Define output filename
output_file = "chatgpt_conversation.pdf"

# ChatGPT shared URL (user input)
chat_url = input("Enter the ChatGPT share URL: ").strip()

# Verify the URL format
if not chat_url.startswith("https://chat.openai.com/share/"):
    print("Invalid URL. Must start with https://chat.openai.com/share/")
    exit()

try:
    # Download HTML content
    response = requests.get(chat_url)
    if response.status_code != 200:
        raise Exception(f"Failed to load the chat: {response.status_code}")

    html_content = response.text

    # Save HTML to temporary file
    with open("temp_chat.html", "w", encoding="utf-8") as f:
        f.write(html_content)

    # Convert HTML to PDF
    pdfkit.from_file("temp_chat.html", output_file)

    print(f"\n✅ PDF saved as: {output_file}")

    # Optional: remove temp file
    os.remove("temp_chat.html")

except Exception as e:
    print(f"❌ Error: {e}")

---

### 3. Notes

• This approach works only if the shared page is publicly accessible (which ChatGPT share links are).
• The PDF output will contain the web page version, including theme and layout.
• You can customize the PDF output using pdfkit options (like page size, margins, etc.).

---

### 4. Optional Enhancements

• Add GUI with Tkinter
• Accept multiple URLs
• Add PDF metadata (title, author, etc.)
• Add support for offline rendering using BeautifulSoup to clean content

---

### Exercise

• Try converting multiple ChatGPT share links to PDF
• Customize the styling with your own CSS
• Add a timestamp or watermark to the PDF

---

#Python #ChatGPT #PDF #WebScraping #Automation #pdfkit #tkinter

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This repository contains a collection of everything needed to work with libraries related to AI and LLM.

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🎁⏳These 6 steps make every future post on LLMs instantly clear and meaningful.

Learn exactly where Web Scraping, Tokenization, RLHF, Transformer Architectures, ONNX Optimization, Causal Language Modeling, Gradient Clipping, Adaptive Learning, Supervised Fine-Tuning, RLAIF, TensorRT Inference, and more fit into the LLM pipeline.

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✸ 1️⃣ Data Collection (Web Scraping & Curation)

☆ Web Scraping: Gather data from books, research papers, Wikipedia, GitHub, Reddit, and more using Scrapy, BeautifulSoup, Selenium, and APIs.

☆ Filtering & Cleaning: Remove duplicates, spam, broken HTML, and filter biased, copyrighted, or inappropriate content.

☆ Dataset Structuring: Tokenize text using BPE, SentencePiece, or Unigram; add metadata like source, timestamp, and quality rating.

✸ 2️⃣ Preprocessing & Tokenization

☆ Tokenization: Convert text into numerical tokens using SentencePiece or GPT’s BPE tokenizer.

☆ Data Formatting: Structure datasets into JSON, TFRecord, or Hugging Face formats; use Sharding for parallel processing.

✸ 3️⃣ Model Architecture & Pretraining

☆ Architecture Selection: Choose a Transformer-based model (GPT, T5, LLaMA, Falcon) and define parameter size (7B–175B).

☆ Compute & Infrastructure: Train on GPUs/TPUs (A100, H100, TPU v4/v5) with PyTorch, JAX, DeepSpeed, and Megatron-LM.

☆ Pretraining: Use Causal Language Modeling (CLM) with Cross-Entropy Loss, Gradient Checkpointing, and Parallelization (FSDP, ZeRO).

☆ Optimizations: Apply Mixed Precision (FP16/BF16), Gradient Clipping, and Adaptive Learning Rate Schedulers for efficiency.

✸ 4️⃣ Model Alignment (Fine-Tuning & RLHF)

☆ Supervised Fine-Tuning (SFT): Train on high-quality human-annotated datasets (InstructGPT, Alpaca, Dolly).

☆ Reinforcement Learning from Human Feedback (RLHF): Generate responses, rank outputs, train a Reward Model (PPO), and refine using Proximal Policy Optimization (PPO).

☆ Safety & Constitutional AI: Apply RLAIF, adversarial training, and bias filtering.

✸ 5️⃣ Deployment & Optimization

☆ Compression & Quantization: Reduce model size with GPTQ, AWQ, LLM.int8(), and Knowledge Distillation.

☆ API Serving & Scaling: Deploy with vLLM, Triton Inference Server, TensorRT, ONNX, and Ray Serve for efficient inference.

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☆ Performance Testing: Validate using HumanEval, HELM, OpenAI Eval, MMLU, ARC, and MT-Bench.
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html-to-markdown

A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.

Features:
⭐️ Full HTML5 Support: Comprehensive support for all modern HTML5 elements including semantic, form, table, ruby, interactive, structural, SVG, and math elements
⭐️ Enhanced Table Support: Advanced handling of merged cells with rowspan/colspan support for better table representation
⭐️ Type Safety: Strict MyPy adherence with comprehensive type hints
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
⭐️ Streaming Support: Memory-efficient processing for large documents with progress callbacks
⭐️ Highlight Support: Multiple styles for highlighted text (<mark> elements)
⭐️ Task List Support: Converts HTML checkboxes to GitHub-compatible task list syntax

nstallation

pip install html-to-markdown

Optional lxml Parser
For improved performance, you can install with the optional lxml parser:

pip install html-to-markdown[lxml]

The lxml parser offers:

🆘 ~30% faster HTML parsing compared to the default html.parser
🆘 Better handling of malformed HTML
🆘 More robust parsing for complex documents

Quick Start
Convert HTML to Markdown with a single function call:

from html_to_markdown import convert_to_markdown

html = """
<!DOCTYPE html>
<html>
<head>
    <title>Sample Document</title>
    <meta name="description" content="A sample HTML document">
</head>
<body>
    <article>
        <h1>Welcome</h1>
        <p>This is a <strong>sample</strong> with a <a href="https://example.com">link</a>.</p>
        <p>Here's some <mark>highlighted text</mark> and a task list:</p>
        <ul>
            <li><input type="checkbox" checked> Completed task</li>
            <li><input type="checkbox"> Pending task</li>
        </ul>
    </article>
</body>
</html>
"""

markdown = convert_to_markdown(html)
print(markdown)

Working with BeautifulSoup:
If you need more control over HTML parsing, you can pass a pre-configured BeautifulSoup instance:

from bs4 import BeautifulSoup
from html_to_markdown import convert_to_markdown

# Configure BeautifulSoup with your preferred parser
soup = BeautifulSoup(html, "lxml")  # Note: lxml requires additional installation
markdown = convert_to_markdown(soup)

Github: https://github.com/Goldziher/html-to-markdown

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