Complete per-user data isolation, bug fixes, and Admin wipe trigger

feat: surface exact firebase error message for google sign in

feat: rebuild auth from scratch using pure Firebase REST API

fix: make Streamlit startup resilient - handle import failures gracefully

feat: replace Firebase JS SDK with REST API + Google/GitHub OAuth redirect flow

Gracefully disable YouTube in Cloud Mode

feat: use Invidious API for YouTube transcripts

feat: browser-side YouTube transcript fetching

fix: pre-download embedding model during Docker build to avoid DNS failures on HF Spaces

Replace YouTube fetch with timedtext API - no API keys needed, works from any server

Add YouTube Data API v3 for cloud-reliable transcript fetching with local fallback

Add auto-generated caption support for YouTube: multi-priority fetch, shorts URLs, transcript cleanup

Fix YouTube transcript API: migrate to v1.x instance-based API (fetch + t.text)

Add OCR fallback for scanned/image PDFs using pytesseract + pdf2image

Fix PDF upload empty embeddings error: add validation for scanned/image-only PDFs

fix: Use langchain_core.documents instead of deprecated langchain.schema

fix: Define logger before use in import error handler

fix: Move youtube-transcript-api imports to top level

feat: Enable YouTube URLs in cloud with youtube-transcript-api

fix: Add logger and fix imports for URL ingestion

fix: Add missing logger import

fix: Improve URL ingestion error handling and connectivity check

Add health check endpoint

Add multi-user support with session-based student IDs

Fix: Ensure current_state exists before saving plan

Add Supabase persistent storage support

Fix: Handle AIMessage in all LLM invoke calls

Fix: Handle AIMessage response from ChatHuggingFace

Fix: Use ChatHuggingFace wrapper for chat models

Fix: Remove unsupported task parameter from HF API

Remove duplicate get_llm() from rag_engine.py - use config.py version

Fix: Use max_new_tokens instead of max_length for HF API

Fix: Use HuggingFace embeddings in cloud mode

Add hybrid deployment: Ollama (local) + Hugging Face (cloud)

Fix: Implement automatic date/day progression system

Production code cleanup - remove debug statements and duplicates

Add data persistence and comprehensive citation system

Major improvements: Multi-subject plan generation, enhanced quiz/lesson quality, fixed UI layout

feat: Implement AI Plan Generator (Lite Mode) and Focus Mode Layout

Update backend RAG logic, app UI, and add documentation

Initial commit of FocusFlow backend