AutoStudy AI — Interactive Flashcards & UI/UX Polish

Today I expanded AutoStudy AI’s study pipeline by introducing AI-generated flashcards with an interactive flip-card interface, alongside a cleaner and more polished user experience.

What works

• AI Flashcard Generation: Integrated structured JSON generation via local Qwen2.5:3b (Ollama) to automatically parse PDF text into 5–8 study flashcards (Questions & Answers).
• Interactive Flashcard Viewer: Built a smooth, 3D CSS-flipped card component allowing users to click to reveal answers and navigate seamlessly through their study deck.
• Safe State & JSON Parsing: Implemented robust backend cleaning and frontend IIFE scoping to securely pass and render dynamic JSON datasets without client-side conflicts.
• UI/UX Overhaul: Upgraded the visual hierarchy with modern card containers, responsive controls, clear progress counters (Card X of Y), and sleek loading animations during document processing.

Why this matters

AutoStudy AI is evolving from a basic text extractor into a true, active-recall study tool. By combining local AI intelligence with interactive flashcards, users can now test their knowledge directly from their study materials while maintaining 100% data privacy and offline capability.

Tech stack

• Flask (Python backend)
• Ollama (Qwen2.5:3b for local structured JSON extraction)
• PyMuPDF (PDF text processing)
• Vanilla HTML5, CSS3 (3D transforms & transitions), & JavaScript (IIFE modules)

Next step

Implement custom study tool selection on the homepage (allowing users to choose between summaries, flashcards, or AI assistants per upload) and build a history sidebar for quick access to past documents.

Second milestone achieved: active recall study workflows are now fully operational offline. 🚀

AutoStudy AI — PDF Explanation via AI

Today I integrated Ollama with Qwen2.5:3b to generate AI-powered summaries directly from extracted PDF text.

What works

• Upload a PDF via the web interface
• Extract text from all pages using PyMuPDF
• Generate 5-point AI summaries using local Qwen2.5 via Ollama
• Display both summary and extracted text in the browser
• Fully offline — zero cloud API calls, complete privacy

Why this matters

AutoStudy AI now has local intelligence. The model runs locally on GPU (~4GB VRAM), allowing fast iteration and proving that small open-source LLMs handle Greek/English PDFs effectively for flashcards, quizzes, and concept extraction.

Tech stack

• Flask
• PyMuPDF (fitz)
• Ollama + Qwen2.5:3b
• Python 3.11
• Vanilla HTML/CSS/JS

Next step

Add CSS styling for a polished UI (dark mode, card layouts, mobile responsiveness, and loading animations).

AutoStudy AI — PDF Upload & Text Extraction

Today I implemented the core document pipeline for AutoStudy AI.

What works

• Upload a PDF through the web interface
• Save the file locally
• Extract text from all pages using PyMuPDF
• Display the extracted text in the browser

Why this matters

This is the first end-to-end workflow of the project. AutoStudy AI can now take a document and convert it into machine-readable text, which will be the foundation for:

• AI summaries
• Flashcards
• Quiz generation
• Spaced repetition

Tech stack

• Flask — web backend
• PyMuPDF (fitz) — PDF text extraction
• Python 3.11

Next step

Integrate Ollama + Qwen3 to generate local AI summaries directly from the extracted text.

First milestone achieved: documents can now enter the AI pipeline completely offline. 🚀