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# ============================================================================
# Phase 6: Generate Final LaTeX Report
# ============================================================================
def escape_latex(text):
    """Escape special LaTeX characters in text."""
    if not isinstance(text, str):
        text = str(text)
    # Escape special characters
    replacements = {
        '\\': '\\textbackslash{}',

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# ============================================================================
# Phase 5.5: Generate Visualization Images for LaTeX Report
# ============================================================================
print("=== Phase 5.5: Generating Visualization Images ===")

import os

# Create images directory if it doesn't exist
os.makedirs('images', exist_ok=True)
print("✓ Created/verified 'images' directory")

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PWA Implementation Technical Documentation

Overview

This document describes the technical implementation of converting the MyAbhyasa web application into a Progressive Web App (PWA). The PWA implementation enables offline functionality, app-like installation, and improved performance through service worker caching.

Project: MyAbhyasa - Self-Study Made Easy
Technology Stack: Next.js 15.0.3, React 19, TypeScript
PWA Library: next-pwa v5.6.0
Date: November 2024

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export const STUDENT_TEST_TYPES: TestType[] = [
  {
    id: "previous-year",
    title: "Previous Year Papers",
    description: "Solve actual past exam papers for realistic practice",
    icon: "/teachers/test/previous-paper-icon.svg",
    route: "/student/test/pyp",
    dbID: 1,
  },
  // {

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Quick Start Guide - Local Testing

NEW: One-command setup available! Run ./scripts/setup-local.sh for automated setup.

🚀 Quick Setup (5 minutes)

1. Start Supabase

supabase start

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Step-by-Step Setup Guide

This guide will walk you through setting up the local development environment from scratch.

Prerequisites

Before starting, ensure you have:

1. Docker Desktop installed and running

• Download: https://www.docker.com/products/docker-desktop

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Tokenization Implementation Guide

Current Status

The codebase uses cl100k_base encoding as an approximation for Llama 4 and Gemini Pro models, which is accurate enough for most use cases (within 10-20% of actual token counts).

Option 4: Full Model-Specific Tokenization

For Llama 4 Models

To implement proper Llama tokenization, you have two options:

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/**
 * Example implementation of proper tokenization for Llama and Gemini
 * This file shows how to implement Option 4 with full model-specific tokenization
 * 
 * To use this:
 * 1. Install required dependencies: npm install @huggingface/tokenizers
 * 2. Replace the current token-counter.util.ts with this implementation
 * 3. Update all countTokens calls to handle async properly
 */

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import { encoding_for_model, get_encoding, type Tiktoken } from 'tiktoken';
import { Model } from '../../debate/enums/model.enum';

export class TokenCounter {
  private static readonly FALLBACK_CHARS_PER_TOKEN = 4;
  private static encodingCache = new Map<string, Tiktoken>();

  private static getEncodingForModel(model: Model): Tiktoken | null {
    const cacheKey = model;

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/**
 * These hooks re-implement the now removed useBlocker and usePrompt hooks in 'react-router-dom'.
 * Thanks for the idea @piecyk https://github.com/remix-run/react-router/issues/8139#issuecomment-953816315
 * Source: https://github.com/remix-run/react-router/commit/256cad70d3fd4500b1abcfea66f3ee622fb90874#diff-b60f1a2d4276b2a605c05e19816634111de2e8a4186fe9dd7de8e344b65ed4d3L344-L381
 */
import { useContext, useEffect, useCallback } from 'react';
import { UNSAFE_NavigationContext as NavigationContext } from 'react-router-dom';
/**
 * Blocks all navigation attempts. This is useful for preventing the page from
 * changing until some condition is met, like saving form data.