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

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Project description

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]`
Raw HTML string	`from_html_string(html)` / `from_html(html)`	❌ single string
Local HTML file	`from_html_path(path)`	❌ single path
HTML folder	`from_html_folder(folder)`	✅ folder scan
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. Local HTML file

mcq = gen.from_html_path("/path/to/tutorial.html", n=5)

4. Scan HTML folder

mcq = gen.from_html_folder("./tutorials/", n=20)

5. 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)

6. Local PDF files

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

7. 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)

8. 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)

9. Batch image files

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

8. Page range for PDFs

# Only process specific pages (1-indexed)
mcq = gen.from_pdf_urls("https://example.com/textbook.pdf", n=10, pages="1-10,15,20-25")

9. Progress bar during generation

mcq = gen.from_pdf_urls("https://example.com/textbook.pdf", n=10, show_progress=True)

11. 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.",
)

12. 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)

14. 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)

15. 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)}")

16. 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)

17. 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, prompt_log_path, ocr_model, and mcq_model:

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
    ocr_model="google/gemini-2.5-flash-lite", # override OCR model for this call
    mcq_model="openai/gpt-4o",               # override MCQ model for this call
)

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" | "anthropic" | "openai" | "ollama" | | 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" | "auto" | model ID | | ocr_models | None | Priority list for auto OCR | | ocr_fallback | True | Fall back to Tesseract when vision API fails | | ocr_lang | "eng" | Tesseract language code | | method | "twostep" | "twostep" (OCR→MCQ) | "images2mcq" | | save_ocr_path | None | Save OCR text to file when method="twostep" | | prompt_log_path | None | Dump prompts to file/terminal | | batch_size | 10 | Questions per API call | | max_tokens | 4096 | Max tokens per API response | | custom_instructions | None | Global custom instructions | | extractor_kwargs | None | Keyword args forwarded to ContentExtractor | | pdf_backend | "auto_detect" | PDF extraction backend | | pdf_scanned_max_pages | 50 | Max pages to OCR for scanned PDFs | | pdf_chunk_size | 1500 | Characters per chunk for PDFs |

Method	Description
`from_url(url, n, ...)`	HTML page
`from_html(html, n, ...)`	Raw HTML string
`from_html_string(html, n, ...)`	Alias for `from_html`
`from_html_path(path, n, ...)`	Local HTML file
`from_html_folder(folder, n, ...)`	Scan folder for .html files
`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, ocr_model, mcq_model, difficulty_mix, focus_topics, custom_instructions, show_progress. PDF methods additionally accept pages.

`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

# Raw HTML string
html2mcq --html-string '<html><body><h1>Python</h1></body></html>'

# Scan HTML folder
html2mcq --html-folder ./tutorials/

# 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"

# Override OCR model per call
html2mcq --image-folder ./slides/ --ocr-model "google/gemini-2.5-flash-lite"

# Override MCQ model per call
html2mcq --pdf-folder ./notes/ --mcq-model "openai/gpt-4o"

# Save OCR text to file
html2mcq --image-folder ./slides/ --method twostep --save-ocr-path ocr_output.txt

# Page range (only process specific pages)
html2mcq --pdf-url https://example.com/textbook.pdf --pages "1-10,15,20-25"

# Show progress bar during MCQ generation
html2mcq --pdf-folder ./textbooks/ --progress

# PDF processing options
html2mcq --pdf-folder ./textbooks/ --pdf-backend pymupdf --scanned-max-pages 100

# 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 2.7.0

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Project description

html2mcq

Install

Quick Start

Input Methods

Examples (10+)

1. Basic — HTML tutorial page

2. Raw HTML string

3. Local HTML file

4. Scan HTML folder

5. Multiple PDF URLs in batch

6. Local PDF files

7. Image files with OCR (two-step)

8. Image URLs via vision model (direct)

9. Batch image files

8. Page range for PDFs

9. Progress bar during generation

11. Generate as many questions as possible

9. Difficulty mix and topic focus

10. Custom instructions per call

12. Pre-extracted content blocks

14. Save output to JSON file

15. Filter questions by difficulty

16. Different AI provider — Anthropic

17. 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`