Convert any HTML tutorial page, PDF, or image into MCQ questions using AI

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html2mcq

Convert any HTML tutorial page, PDF, or image into MCQ questions using AI.

Install

pip install html2mcq

Everything is included — HTML extraction, PDF support, OCR, all AI providers.

Quick Start

from html2mcq import MCQGenerator

gen = MCQGenerator(
    api_key="sk-or-v1-...",
    provider="openrouter",
    mcq_model="google/gemini-2.5-flash-lite",
)

# From an HTML tutorial page
mcq = gen.from_url("https://docs.python.org/3/tutorial/", n=15)

# From a PDF URL
mcq = gen.from_pdf_urls("https://example.com/tutorial.pdf", n=10)

# From a local PDF file
mcq = gen.from_pdf_paths("/path/to/notes.pdf", n=10)

# From image files (OCR → MCQ in one step)
mcq = gen.from_image_paths("screenshot.png", n=5)

# From image URLs (vision model direct)
mcq = gen.from_image_urls("https://example.com/diagram.png", n=5)

print(mcq.to_json())
print(mcq.to_pretty_str())

Input Methods

Input	Method	Batch support
HTML tutorial page URL	`from_url(url)`	❌ single URL
Raw HTML string	`from_html(html)`	❌ single string
PDF via URL	`from_pdf_urls(url)`	✅ `list[str]`
Local PDF file	`from_pdf_paths(path)`	✅ `list[str]`
Image files (local)	`from_image_paths(path)`	✅ `list[str]`
Image URLs	`from_image_urls(url)`	✅ `list[str]`
Pre-built content blocks	`from_blocks(blocks)`	❌ single list

Examples (10+)

1. Basic — HTML tutorial page

from html2mcq import MCQGenerator

gen = MCQGenerator(api_key="sk-or-v1-...")
mcq = gen.from_url("https://docs.python.org/3/tutorial/introduction.html", n=5)
print(mcq.to_pretty_str())

2. Raw HTML string

html_string = """
<html><body>
<h1>Python Lists</h1>
<p>Lists are ordered, mutable collections. Items are indexed from 0.</p>
<pre><code>fruits = ["apple", "banana", "cherry"]
fruits.append("date")
</code></pre>
</body></html>
"""
mcq = gen.from_html(html_string, n=3)
print(mcq.to_json())

3. Multiple PDF URLs in batch

mcq = gen.from_pdf_urls([
    "https://example.com/chapter1.pdf",
    "https://example.com/chapter2.pdf",
    "https://example.com/chapter3.pdf",
], n=20)

4. Local PDF files

mcq = gen.from_pdf_paths([
    "C:/Users/Me/Documents/lecture_notes.pdf",
    "C:/Users/Me/Documents/textbook_ch5.pdf",
], n=15)

5. Image files with OCR (two-step)

gen = MCQGenerator(
    api_key="sk-or-v1-...",
    method="twostep",
    ocr_model="google/gemini-2.5-flash-lite",
    save_ocr_path="ocr_output.txt",
)
mcq = gen.from_image_paths("screenshot.png", n=5)

6. Image URLs via vision model (direct)

gen = MCQGenerator(
    api_key="sk-or-v1-...",
    method="images2mcq",
)
mcq = gen.from_image_urls("https://example.com/diagram.png", n=5)

7. Batch image files

mcq = gen.from_image_paths([
    "slide01.png",
    "slide02.png",
    "slide03.png",
], n=15)

8. Generate as many questions as possible

# When n=999, the AI covers every distinct topic in the content
mcq = gen.from_url("https://docs.python.org/3/tutorial/", n=999)
print(f"Generated {len(mcq.questions)} questions")

9. Difficulty mix and topic focus

mcq = gen.from_url(
    "https://docs.python.org/3/tutorial/",
    n=20,
    difficulty_mix="50% easy, 30% medium, 20% hard",
    focus_topics=["lists", "dictionaries", "loops"],
)

10. Custom instructions per call

mcq = gen.from_url(
    "https://docs.python.org/3/tutorial/",
    n=10,
    custom_instructions="All questions must be code-based. Include the code snippet in the question.",
)

11. Pre-extracted content blocks

from html2mcq import MCQGenerator, ContentExtractor

extractor = ContentExtractor()
title, blocks = extractor.from_url("https://docs.python.org/3/tutorial/")

# Filter to only code blocks
code_blocks = [b for b in blocks if b.type == "code"]
mcq = gen.from_blocks(code_blocks, n=5)

12. Save output to JSON file

import json

mcq = gen.from_url("https://docs.python.org/3/tutorial/", n=10)
with open("quiz.json", "w") as f:
    json.dump(mcq.to_json(), f, indent=2)

13. Filter questions by difficulty

mcq = gen.from_url("https://docs.python.org/3/tutorial/", n=20)
easy = mcq.filter_by_difficulty("easy")
medium = mcq.filter_by_difficulty("medium")
hard = mcq.filter_by_difficulty("hard")
print(f"Easy: {len(easy)}, Medium: {len(medium)}, Hard: {len(hard)}")

14. Different AI provider — Anthropic

gen = MCQGenerator(
    api_key="sk-ant-...",
    provider="anthropic",
    mcq_model="claude-3-5-sonnet-20241022",
)
mcq = gen.from_url("https://docs.python.org/3/tutorial/", n=10)

15. Local Ollama (no API key needed)

gen = MCQGenerator(
    provider="ollama",
    mcq_model="qwen2.5:7b",
    ollama_base_url="http://localhost:11434/v1",
)
mcq = gen.from_url("https://docs.python.org/3/tutorial/", n=5)

Constructor

gen = MCQGenerator(
    provider="openrouter",                         # default, also: "anthropic" | "openai" | "ollama"
    api_key="sk-or-v1-...",                        # or set env var OPENROUTER_API_KEY
    api_key_override="sk-or-v1-...",               # override key for this instance
    mcq_model="google/gemini-2.5-flash-lite",      # model for ALL MCQ generation
    mcq_model_list=["model1", "model2"],           # fallback list for mcq_model="auto"
    ocr_model="pytesseract",                       # OCR for HTML images: "pytesseract" | "auto" | model ID
    ocr_models=["model1", "pytesseract"],          # priority list for ocr_model="auto"
    method="twostep",                              # "twostep" (OCR→MCQ) | "images2mcq" (vision direct)
    save_ocr_path="ocr_output.txt",                # save OCR text when method=twostep
    prompt_log_path="stdout",                      # dump prompts: file path | "stdout" | "-"
    batch_size=10,
    max_tokens=4096,
    custom_instructions="Make answers tricky",
)

New in v2

Parameter	What it does
`mcq_model`	Single source of truth for all MCQ generation (text + vision). Renamed from `model`.
`mcq_model="auto"`	Tries `mcq_model_list` in order until one succeeds.
`mcq_model_list`	Priority-ordered models for auto mode. Runtime-reloadable via `HTML2MCQ_MCQ_MODELS` env var.
`ocr_model`	OCR backend: `"pytesseract"` (default), `"auto"` (priority list), or any OpenRouter model ID.
`ocr_model="auto"`	Tries `ocr_models` priority list.
`ocr_models`	Priority list for auto OCR. Reloadable via `HTML2MCQ_OCR_MODELS` env var.
`method`	`"twostep"` (OCR images → text → MCQs) or `"images2mcq"` (vision model direct).
`prompt_log_path`	Dump full prompts to file or terminal. Use `"stdout"` or `"-"` for terminal.
`api_key_override`	Override key for this instance
`save_ocr_path`	Save OCR text to file when method=twostep

Two-Step Image Pipeline (`method="twostep"`)

When method="twostep" (default), from_image_paths() and from_image_urls() automatically:

OCR — extract text from images using ocr_model
Generate — feed text into text-based MCQ generation

Optionally save the OCR text with save_ocr_path:

gen = MCQGenerator(method="twostep", ocr_model="google/gemini-2.5-flash-lite",
                   save_ocr_path="ocr_output.txt")

# OCR → save to file → generate MCQs
gen.from_image_paths("chart.png", n=5)

Per-Call Overrides

All 7 public methods accept api_key_override and prompt_log_path:

mcq = gen.from_url(
    "https://example.com/",
    n=10,
    api_key_override="sk-or-v1-...",       # different key for this call
    prompt_log_path="debug_prompt.txt",    # log prompts for this call only
)

Output Schema

{
  "total_exam_time": 20,
  "questions": [
    {
      "question_html": "Which of these are non-mutating array methods?",
      "options": ["push()", "map()", "filter()", "pop()"],
      "answers": [1, 2],
      "multi": true,
      "marks": 1,
      "negative_marks": 0,
      "difficulty": "medium",
      "explaination": "map() and filter() return new arrays without modifying the original."
    }
  ]
}

AI Providers

# OpenRouter (default) — 100+ models
gen = MCQGenerator(api_key="sk-or-...", provider="openrouter",
                   mcq_model="google/gemini-2.5-flash-lite")

# Anthropic Claude
gen = MCQGenerator(api_key="sk-ant-...", provider="anthropic")

# OpenAI
gen = MCQGenerator(api_key="sk-...", provider="openai", mcq_model="gpt-4o")

# Ollama (local, no API key needed)
gen = MCQGenerator(provider="ollama", mcq_model="qwen2.5:7b",
                   ollama_base_url="http://localhost:11434/v1")

OCR Priority (when `ocr_model="auto"`)

The default priority list is:

google/gemini-2.5-flash-lite (fast, cheap)
google/gemma-3-27b-it (free)
google/gemma-3-12b-it (free)
openai/gpt-4o (paid)
pytesseract (local fallback)

Override via ocr_models parameter or HTML2MCQ_OCR_MODELS env var.

MCQ Model Priority (when `mcq_model="auto"`)

Tries mcq_model_list in order. Override via HTML2MCQ_MCQ_MODELS env var (comma-separated):

export HTML2MCQ_MCQ_MODELS="model1,model2,model3"

Custom Instructions

# Apply to every call
gen = MCQGenerator(
    provider="openrouter",
    custom_instructions="Make answers very close and confusing."
)

# Or per individual call
mcq = gen.from_url("https://example.com/", n=10,
    custom_instructions="All questions must be code-based.")

API Reference

`MCQGenerator`

Parameter	Default	Description
`provider`	`"openrouter"`	`"openrouter"`
`api_key`	`None`	API key or env var
`api_key_override`	`None`	Override key for this instance
`mcq_model`	`""`	Model for all MCQ generation. `"auto"` tries `mcq_model_list`
`mcq_model_list`	`None`	Fallback models for auto mode
`ocr_model`	`"pytesseract"`	OCR backend: `"pytesseract"`
`ocr_models`	`None`	Priority list for auto OCR
`method`	`"twostep"`	`"twostep"` (OCR→MCQ)
`prompt_log_path`	`None`	Dump prompts to file/terminal
`batch_size`	`10`	Questions per API call
`custom_instructions`	`None`	Global custom instructions

Method	Description
`from_url(url, n, ...)`	HTML page
`from_html(html, n, ...)`	Raw HTML string
`from_pdf_urls(urls, n, ...)`	PDF via URL (str or list)
`from_pdf_paths(paths, n, ...)`	Local PDF file (str or list)
`from_image_urls(urls, n, ...)`	Image URLs → MCQ via vision
`from_image_paths(paths, n, ...)`	Local image files → MCQ
`from_blocks(blocks, n, ...)`	Pre-extracted `ContentBlock` list

All methods accept api_key_override, prompt_log_path, difficulty_mix, focus_topics, custom_instructions.

`MCQSet`

Property / Method	Description
`.questions`	`List[MCQQuestion]`
`.total_exam_time`	Minutes — auto-calculated as n × 2
`.to_json()`	Exam-ready JSON
`.to_pretty_str()`	Human-readable output
`.filter_by_difficulty(d)`	Filter by `"easy"` / `"medium"` / `"hard"`

CLI

# Basic — URL (n defaults to 999 = cover all topics)
html2mcq https://docs.python.org/3/tutorial/

# Local HTML file
html2mcq --html ./tutorial.html

# PDF URL (repeatable)
html2mcq --pdf-url https://example.com/chapter1.pdf --pdf-url https://example.com/chapter2.pdf

# Local PDF file (repeatable)
html2mcq --pdf-path ./textbook.pdf

# All PDFs from a folder
html2mcq --pdf-folder ./textbooks/

# Image URL (via vision model)
html2mcq --image-url https://example.com/diagram.png --method images2mcq

# Local image files (repeatable)
html2mcq --image-path ./slide1.png --image-path ./slide2.png

# All images from a folder (supports .png, .jpg, .jpeg, .gif, .bmp, .tiff, .webp)
html2mcq --image-folder ./slides/ --method images2mcq

# Combine image folder + PDF folder
html2mcq --image-folder ./diagrams/ --pdf-folder ./notes/

# Specify question count
html2mcq https://example.com/tutorial --n 20

# Output to JSON file
html2mcq https://example.com/tutorial --output quiz.json --format json

# Difficulty mix and topic focus
html2mcq https://example.com/tutorial --difficulty "40% easy, 40% medium, 20% hard" --topics variables functions

# Custom instructions
html2mcq https://example.com/tutorial -i "Make answers very close and confusing"

# AI provider and model
html2mcq https://example.com/tutorial --provider openai --mcq-model gpt-4o --api-key sk-...

# Auto-detect API key from env var (OPENROUTER_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY)
html2mcq https://example.com/tutorial

# Local Ollama (no API key needed)
html2mcq https://example.com/tutorial --provider ollama --mcq-model qwen2.5:7b

# Show version
html2mcq --version

All output is printed to stdout by default. Use --output / -o to save to a file.

Project Structure

html2mcq/
├── html2mcq/
│   ├── __init__.py
│   ├── extractor.py        # HTML parser
│   ├── generator.py        # MCQGenerator — main API
│   ├── image_ocr.py        # OCR (pytesseract + vision API)
│   ├── models.py           # ContentBlock, MCQQuestion, MCQSet
│   ├── pdf.py              # PDF extractor
│   ├── prompts.py          # System + user prompt builders
│   └── cli.py              # CLI entry point
├── tests/
│   ├── test_html2mcq.py    # Unit tests (fully mocked)
│   └── scripts/            # Debug / scratch scripts
├── pyproject.toml
├── README.md
└── CHANGELOG.md

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html2mcq 2.4.0

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Project links

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Project description

html2mcq

Install

Quick Start

Input Methods

Examples (10+)

1. Basic — HTML tutorial page

2. Raw HTML string

3. Multiple PDF URLs in batch

4. Local PDF files

5. Image files with OCR (two-step)

6. Image URLs via vision model (direct)

7. Batch image files

8. Generate as many questions as possible

9. Difficulty mix and topic focus

10. Custom instructions per call

11. Pre-extracted content blocks

12. Save output to JSON file

13. Filter questions by difficulty

14. Different AI provider — Anthropic

15. Local Ollama (no API key needed)

Constructor

New in v2

Two-Step Image Pipeline (method="twostep")

Per-Call Overrides

Output Schema

AI Providers

OCR Priority (when ocr_model="auto")

MCQ Model Priority (when mcq_model="auto")

Custom Instructions

API Reference

MCQGenerator

MCQSet

CLI

Project Structure

License

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distribution

File details

File metadata

File hashes

File details

File metadata

File hashes

Two-Step Image Pipeline (`method="twostep"`)

OCR Priority (when `ocr_model="auto"`)

MCQ Model Priority (when `mcq_model="auto"`)

`MCQGenerator`

`MCQSet`