image2mcq 1.1.1

Convert images (screenshots, scanned pages, diagrams) into MCQ questions using AI

image2mcq

Convert images — screenshots, scanned pages, diagrams, charts, and photographs — into high-quality MCQ questions using AI.

Built on top of html2mcq's image pipeline, extracted as a standalone library focused purely on image-to-MCQ generation.

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Features

• Two processing methods:
  • twostep (default) — OCR image text, then generate MCQs from extracted text
  • images2mcq — send images directly to a vision LLM for MCQ generation
• Multiple AI providers: OpenRouter, Anthropic, OpenAI, Ollama
• Auto model failover: if one model fails (e.g. quota exhausted), automatically tries the next
• Local OCR fallback: Tesseract OCR when vision APIs are unavailable
• CLI & Python API — use from terminal or integrate into your code

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Quick Start

CLI

# Single image file
image2mcq --image-path diagram.png -n 5

# Multiple image URLs
image2mcq --image-url https://example.com/chart1.png --image-url https://example.com/chart2.png

# Scan a folder of images
image2mcq --image-folder ./lecture-slides/ --method images2mcq

# Output as JSON
image2mcq --image-path notes.png -o questions.json --format json

# Use n=999 to generate as many as the content supports
image2mcq --image-path textbook-page.png

Python API

from image2mcq import ImageMCQGenerator

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

# From local files
mcq = gen.from_image_paths("screenshot.png", n=5)
print(mcq.to_pretty_str())

# From URLs
mcq = gen.from_image_urls("https://example.com/diagram.png", n=3)
print(mcq.to_json())

# From multiple images
mcq = gen.from_image_paths(["page1.png", "page2.png", "page3.png"])

Two-Step (OCR → MCQ)

gen = ImageMCQGenerator(
    api_key="sk-or-v1-...",
    method="twostep",  # default
)
mcq = gen.from_image_paths("scanned-page.png", n=10)

Images2MCQ (Vision Direct)

gen = ImageMCQGenerator(
    api_key="sk-or-v1-...",
    method="images2mcq",
    mcq_model="openai/gpt-4o",  # vision model
)
mcq = gen.from_image_paths("architecture-diagram.png", n=5)

Custom Instructions

mcq = gen.from_image_paths(
    "graph.png",
    n=5,
    difficulty_mix="50% easy, 50% hard",
    focus_topics=["data structures", "time complexity"],
    custom_instructions="Make answers very close and confusing",
)

Auto Model Selection

gen = ImageMCQGenerator(
    api_key="sk-or-v1-...",
    mcq_model="auto",
    mcq_model_list=[
        "nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free",
        "google/gemma-4-31b-it:free",
    ],
)

Environment Variables

Variable	Purpose
OPENROUTER_API_KEY	Default API key for OpenRouter
ANTHROPIC_API_KEY	API key for Anthropic
OPENAI_API_KEY	API key for OpenAI
IMAGE2MCQ_MCQ_MODELS	Comma-separated MCQ model priority list for model="auto"
IMAGE2MCQ_OCR_MODELS	Comma-separated OCR model priority list for ocr_model="auto"

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Output Format

# Pretty-print
print(mcq.to_pretty_str())

# JSON
print(mcq.to_json())
# {
#   "total_exam_time": 20,
#   "questions": [
#     {
#       "question_html": "What is the time complexity of binary search?",
#       "options": ["O(n)", "O(log n)", "O(n^2)", "O(1)"],
#       "answers": [1],
#       "multi": false,
#       "marks": 1.0,
#       "negative_marks": 0.25,
#       "difficulty": "easy",
#       "explaination": "Binary search halves the search space each iteration."
#     }
#   ]
# }

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Installation

pip install image2mcq

For OCR support, also install Tesseract.

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License

MIT