A modern PDF viewer application built with Rust and Dioxus, featuring AI-powered search capabilities using Gemini.

• Dynamic File Selection: Open and close PDF files through an intuitive file dialog interface
• High-Quality PDF Display: Renders PDF pages at 1000x1400 resolution for balanced quality and performance
• Continuous Scroll: View all PDF pages in a seamless continuous scroll interface
• AI-Powered Search: Integrated multi-provider AI API (Gemini, ChatGPT, Claude) for intelligent content analysis and term explanations
• Flashcard System: Automatic vocabulary flashcard creation from search terms with persistent storage and popup interface
• Reading Bookmark System: Page-level bookmark functionality to track reading progress across sessions
• Position Marker System: Precise in-page position markers with automatic scrolling navigation
• PDF Page Rotation: Individual page rotation with persistent state across sessions
• Recent Files History: Quick access to recently opened PDF files (up to 10 files)
• API Key Management: Automatic saving and restoration of AI provider API keys
• Modern UI: Clean, responsive interface built with Dioxus framework
• Optimized Layout: Specially designed for vertical PDF documents with horizontal AI search panel
• Smart Caching: Intelligent page caching system with optimized loading to prevent unnecessary re-rendering
• Cross-platform: Desktop application with native performance

• Dioxus - Modern reactive UI framework for Rust
• pdfium-render - PDF rendering using Google's PDFium library
• rfd - Native file dialog integration for file selection
• Reqwest - HTTP client for AI search functionality
• Tokio - Async runtime for non-blocking operations
• Serde - JSON serialization for API communication
• Base64 - Image encoding for web display

This application requires Rust and Cargo. Install them from the official website:

Windows, macOS, and Linux:

# Install Rust via rustup (recommended) curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh # On Windows, download and run rustup-init.exe from: # https://rustup.rs/ # Restart your terminal or run: source ~/.cargo/env # Verify installation cargo --version rustc --version

Alternative Installation Methods:

macOS (via Homebrew):

brew install rust

Ubuntu/Debian:

sudo apt update sudo apt install rustc cargo

CentOS/RHEL/Fedora:

# CentOS/RHEL sudo yum install rust cargo # Fedora sudo dnf install rust cargo

To use the AI search functionality, obtain one or more of the following API keys:

Gemini API (Google): https://makersuite.google.com/app/apikey

ChatGPT API (OpenAI): https://platform.openai.com/api-keys

Claude API (Anthropic): https://console.anthropic.com/

# Clone the repository git clone <repository-url> cd rust-pdf-viewer # Create lib directory for PDFium library mkdir -p lib # Download PDFium library for Intel Macs (x64) curl -L -o pdfium-mac-x64.tgz \ "https://github.com/bblanchon/pdfium-binaries/releases/latest/download/pdfium-mac-x64.tgz" && \ tar -xzf pdfium-mac-x64.tgz -C lib --strip-components=1 && \ rm pdfium-mac-x64.tgz # For Apple Silicon Macs (ARM64), use this instead: # curl -L -o pdfium-mac-arm64.tgz \ # "https://github.com/bblanchon/pdfium-binaries/releases/latest/download/pdfium-mac-arm64.tgz" && \ # tar -xzf pdfium-mac-arm64.tgz -C lib --strip-components=1 && \ # rm pdfium-mac-arm64.tgz # Verify the library is correctly placed ls -la lib/libpdfium.dylib # Build and run the project cargo run # To build a macOS app bundle (.app file) cargo install cargo-bundle cargo bundle --release # The app bundle will be created at: # target/release/bundle/osx/PDF Viewer.app

# Clone the repository git clone <repository-url> cd rust-pdf-viewer # Create lib directory for PDFium library mkdir -p lib # Download PDFium library for x64 systems curl -L -o pdfium-linux-x64.tgz \ "https://github.com/bblanchon/pdfium-binaries/releases/latest/download/pdfium-linux-x64.tgz" && \ tar -xzf pdfium-linux-x64.tgz -C lib --strip-components=1 && \ rm pdfium-linux-x64.tgz # For ARM64 systems, use this instead: # curl -L -o pdfium-linux-arm64.tgz \ # "https://github.com/bblanchon/pdfium-binaries/releases/latest/download/pdfium-linux-arm64.tgz" && \ # tar -xzf pdfium-linux-arm64.tgz -C lib --strip-components=1 && \ # rm pdfium-linux-arm64.tgz # Verify the library is correctly placed ls -la lib/libpdfium.so # Build and run the project cargo run

1. Launch the application - no arguments required
2. Open a PDF file:
   • Click the "📁 PDFを開く" (Open PDF) button in the header
   • Select a PDF file from the file dialog
3. View PDF content:
   • All pages are displayed in a continuous scroll format
   • Loading progress is shown at the top during initial page rendering
   • Individual page rotation controls (90° increments) with persistence
4. Reading Progress Management:
   • Bookmarks: Use "🔖 ブックマーク" to set page-level reading bookmarks
   • Position Markers: Enable "📍 マーカーモード" to place precise location markers within pages
   • Automatic Navigation: Click markers in "📋 マーカー一覧" for automatic page scrolling
   • Recent Files: Access recently opened files via "📋 最近のファイル"
5. AI Search functionality:
   • Select AI provider: Gemini, ChatGPT, or Claude
   • Enter your API key once - it will be automatically saved and restored in future sessions
   • Type your search query to ask about terms or concepts
   • Click "検索" (Search) to get AI-powered explanations
   • Searched terms are automatically saved to your flashcard collection
6. File management:
   • Use "❌ 閉じる" (Close) button to close the current PDF
   • Open different files without restarting the application

• Header Bar: File control buttons and application title
  • "🔖 ブックマーク" (Bookmarks) button for reading progress management
  • "📍 マーカーモード" (Marker Mode) toggle for position marking
  • "📋 マーカー一覧" (Marker List) for viewing and navigating to saved markers
  • "📋 最近のファイル" (Recent Files) for quick file access
  • "📁 PDFを開く" (Open PDF) button for file selection
  • "❌ 閉じる" (Close) button to close current PDF
• Status Bar: Loading progress and PDF information (when PDF is loaded)
• Main Content Area:
  • Left Panel: PDF display area optimized for vertical documents with continuous scroll
  • Right Panel: AI search interface
    • AI provider selection (Gemini, ChatGPT, Claude)
    • API key input (password field) with automatic save/restore
    • Search query input for term explanations
    • Search button with loading indicator
    • Results area with clean text formatting
    • Flashcard button to view saved search terms
• Welcome Screen: Displayed when no PDF is loaded with usage instructions

The application automatically saves search terms and their AI-generated definitions as flashcards for future reference.

Storage Location:

• macOS: ~/.config/pdf-viewer/
• Linux: ~/.config/pdf-viewer/
• Windows: %USERPROFILE%\.config\pdf-viewer\

Data Files:

• flashcards.json - Saved search terms and definitions
• recent_files.json - Recently opened PDF files (max 10)
• api_keys.json - Saved API keys for AI providers
• bookmarks.json - Reading bookmarks for each PDF file
• position_markers.json - Precise position markers within PDF pages
• page_rotations.json - Individual page rotation states

Data Format: Flashcards are stored in JSON format with the following structure:

[ { "id": "1734493200000-machine-lea", "term": "machine learning", "definition": "AI-generated explanation of the term...", "created_at": "2024-12-18 12:00:00 UTC" } ]

Features:

• Automatic saving when AI search is performed
• Duplicate term detection (updates existing definitions)
• Persistent storage across application sessions
• Popup interface for browsing saved flashcards
• Individual flashcard deletion capability

• PDF pages rendered at 1000x1400 pixels for balanced quality and performance
• Continuous scroll display for all pages at once
• Automatic color space conversion (BGRA → RGBA)
• PNG encoding with base64 data URLs for web display

• Asynchronous Gemini API calls that don't block the UI
• Markdown formatting removal for clean text display
• Real-time search status indicators
• Comprehensive error handling

• Smart page-level caching system using HashMap with duplicate loading prevention
• Optimized parallel page rendering with CPU-aware batch processing
• Memory-efficient image handling with color space optimization
• Responsive UI updates with non-blocking async operations
• Single-load policy to prevent unnecessary re-rendering of same PDF files

• Reactive state management with use_signal
• Memoized computations with use_memo
• Component-based design patterns
• CSS-in-Rust styling approach

├── src/ │ └── main.rs # Main Dioxus application ├── assets/ │ └── style.css # UI styling ├── lib/ │ └── libpdfium.dylib # PDFium library for macOS ├── Cargo.toml # Dependencies and configuration └── README.md # This file 

• PDF Rendering: render_pdf_page_with_text() function handles high-resolution page rendering with text extraction
• File Management: Dynamic PDF loading/closing with native file dialog integration and recent files history
• Reading Progress: Bookmark and position marker systems with JSON persistence and automatic navigation
• Page Rotation: Individual page rotation controls with state persistence
• AI Search: search_with_ai() async function for multi-provider API integration
• Text Processing: clean_markdown_text() for formatting search results
• UI Components: Reactive Dioxus components with modern state management and responsive layout
• JavaScript Integration: eval() function for automatic page scrolling and DOM manipulation

All dependencies are optimized for the Dioxus ecosystem, removing legacy egui dependencies for a cleaner, more maintainable codebase.

This project is licensed under the terms specified in the LICENSE file.