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Aryan Raj
Aryan Raj

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Exam_tutor

#devchallenge #weekendchallenge #hf26challenge

What I Built

A local exam-prep tutor for my friend Nishant. He [his real problem, e.g. "studies from scattered notes and web pages and has no way to test himself"] before [course/exam]. Paste URLs or upload PDFs, then either ask questions (answered only from his notes, with sources shown) or get quizzed and graded on his own answers.

Demo

[video link]

Code

GitHub logo Aryan1092raj / Exam_tutor

Exam Tutor

Local RAG study partner built for my friend Nishant. Paste URLs or upload notes, then ask questions or get quizzed. Everything runs on-device with open-weight models via Ollama. No API key, no internet needed after setup, notes never leave the laptop.

Setup

# 1. install Ollama: https://ollama.com
ollama pull qwen2.5:3b
ollama pull nomic-embed-text

# 2. run
pip install -r requirements.txt
streamlit run app.py
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Use

  1. Sidebar: paste URLs (web pages or PDFs) or upload files, click Ingest.
  2. Ask mode: question gets answered only from the ingested notes, with sources shown.
  3. Quiz me mode: enter a topic, get an exam-style question, answer it, get graded.

Sample sources to try (data structures):

https://en.wikipedia.org/wiki/Binary_search_tree
https://en.wikipedia.org/wiki/AVL_tree
https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm

How it works

  • Chunk text (900 chars, 150 overlap)
  • Embed chunks with nomic-embed-text (Ollama)
  • Cosine similarity over numpy matrix, top 4 chunks
  • qwen2.5:3b answers using only retrieved chunks
  • Swap model: edit CHAT_MODEL in app.py

Stack

…
View on GitHub

How I Built It

  • Chat model: Qwen2.5 3B via Ollama, runs on a laptop CPU/GPU.
  • Embeddings: nomic-embed-text via Ollama.
  • Retrieval: notes chunked (900 chars, 150 overlap), embedded, cosine similarity over a numpy matrix, top 4 chunks fed to the model.
  • Grounding: system prompt restricts answers to retrieved notes; if notes don't cover it, it says so.
  • Quiz mode: model writes one exam-style question from retrieved chunks, then grades the student's answer against them.
  • UI: Streamlit. Ingestion via trafilatura (web) and pypdf (PDFs).
  • Whole thing is ~150 lines, one file.

Why Does Open Innovation Matter?

  • Offline: works with bad hostel wifi. After model pull, zero internet needed.
  • Private: his notes never leave his laptop.
  • Free: no API key, no per-query cost, no rate limit during exam week.
  • Swappable: changing the model is one line (CHAT_MODEL). [Add your real 3B vs 7B comparison, or delete this sentence.]
  • A closed API would need billing, a key, and a connection, which is exactly what a student doesn't want the night before an exam.

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