OM Analyzer

Add New Subject — Follow these steps to add a new subject to the analyzer. You only need a text editor and your question paper files — no coding required. Clone the repository from GitHub and add your subject data.

Contribute: Help us grow this analyzer! Add your university's question papers, improve the codebase, or suggest new features. Your contributions make this tool better for everyone.

AI Assistance: Use Claude Sonnet 4.6 (Extended Thinking) or Grok AI (Expert Mode) to convert PDFs/images to JSON format automatically.

Step 1 GitHub Repository ▶

Clone the repository

Visit the GitHub repository to clone the project and start adding your subject.

Step 2 Folder Structure ▶

Folder structure for subjects

src/ ├── BE_COMPUTER/ │ ├── Sem_VIII/ │ │ ├── IS/ │ │ │ ├── old_question_papers.json │ │ │ ├── repeated_questions.json │ │ │ ├── syllabus.json │ │ │ └── img/ │ │ │ └── 2023-S-6b.png │ │ └── DSAP/ │ └── Sem_II/ │ └── Thermal/ │ ├── old_question_papers.json │ ├── repeated_questions.json │ ├── syllabus.json │ └── img/

Required Files: Each subject folder must contain exactly 3 files: old_question_papers.json, repeated_questions.json, and syllabus.json. Create an img/ subfolder for images.

Step 3 Register in app-info.json ▶

Add subject to nested structure in app-info.json

"content": { "BE Computer": { "Sem VIII": [ { "subject": "Your Subject", "folder": "BE_COMPUTER/Sem_VIII/YourSubject", "default": false } ] } }

Add your subject under the appropriate Program → Semester. The app will automatically load old_question_papers.json, repeated_questions.json, and syllabus.json from the folder path. Keep subjects sorted alphabetically within each semester.

Step 4 JSON Data Files ▶

File 1: old_question_papers.json

{ "subject": "Thermal Engineering", "university": "Pokhara University", "programme": "BE — Mechanical Engineering", "level": "Bachelor", "full_marks": 100, "pass_marks": 45, "papers": [ { "id": "2024_fall", "year": "2024", "semester": "Fall", "questions": [ { "number": "1", "sub_no": "a", "marks": 7, "question": "Define thermodynamics...", "context": "Optional case study", "fig_url": "BE_COMPUTER/Sem_II/Thermal/img/2023-S-6b.png", "table_data": { "headers": ["Column 1", "Column 2"], "rows": [ {"col1": "Value 1", "col2": "Value 2"} ] } } ] } ] }

The subject, university, and programme fields are displayed in the Subject Information Bar.

Handling Images (Figures/Diagrams)

Store images in img/ subfolder and reference them using relative paths:

"fig_url": "BE_COMPUTER/Sem_VIII/IS/img/2024-F-3a.png"

Rules: Use relative paths from src/ folder. Supported formats: PNG, JPG, GIF, SVG. Images display at fixed 220px height with auto width.

Handling Tables

Add tables using table_data with headers and rows:

"table_data": { "headers": ["Parameter", "Value", "Unit"], "rows": [ {"col1": "Temperature", "col2": "100", "col3": "°C"}, {"col1": "Pressure", "col2": "1.5", "col3": "bar"} ] }

Rules: Each row object keys (col1, col2, col3...) must match the number of headers. Tables are responsive with horizontal scroll on small screens.

Handling Equations/Mathematical Expressions

For equations, use Unicode symbols or save as images:

"question": "Calculate using formula: E = mc²" OR "question": "Solve the equation shown below:", "fig_url": "BE_COMPUTER/Sem_VIII/Math/img/equation-1.png"

Tip: For complex equations, save them as PNG images using LaTeX editors or screenshot tools. Use Unicode for simple expressions: ∫, ∑, √, ², ³, α, β, π, ≤, ≥, ≠, ∞

File 2: repeated_questions.json

{ "repeated_questions": [ { "question": "Define management...", "topic": "1.1 Meaning and concept", "freq": 20, "papers": ["2025_spring", "2024_fall"] } ] }

Contains questions that appear frequently across multiple papers with their frequency count.

File 3: syllabus.json

{ "course_title": "Thermal Engineering", "course_code": "ME 501", "total_hours": 45, "course_contents": [ { "unit": 1, "title": "Introduction", "hours": 3, "topics": [ { "topic": "1.1 Basic concepts", "frequency": 15 } ] } ] }

Contains syllabus with frequency count for each topic showing how many times it appeared in papers.

Use AI to generate JSON files

Step 1: Copy the format example from above

Step 2: Open AI model in new tab:

Step 3: Upload your PDF/image and paste this prompt:

Convert this question paper to JSON following this exact format: [Paste the format example here] Rules: - Use "id": "YYYY_semester" (e.g. "2024_fall") - Extract all questions with number, sub_no, marks, question text - For OR questions use "sub_no": "or" - For images/diagrams, note them as "fig_url": "path/to/image.png" - For tables, extract as "table_data" with headers and rows - For equations, use Unicode symbols or note as image - Extract full question text accurately

Step 4: Copy the AI-generated JSON and save to your subject folder with the exact file name

Step 5 Test Locally ▶

Requirements

You need either Python or Node.js installed on your system to run a local server.

Step 6 Submit Pull Request ▶

Test your changes locally

Run the app locally and verify that your subject loads correctly, all data displays properly, and images/tables work as expected.

Commit your changes

git add . git commit -m "Add [Subject Name] question papers" git push origin main

Submit pull request to main repository

Go to the main GitHub repository and create a pull request from your fork. Include a description of what subject you added and confirm that you tested it locally.