PDF Study Assistant

PDF Study Assistant is a powerful, interactive tool that combines PDF viewing capabilities with AI-powered analysis, web search functionality, and advanced text recognition features. It's designed to enhance your study experience by providing intelligent assistance and easy access to information.

Features

1. PDF Viewing and Navigation

   • Load PDFs by clicking the "Browse PDF" button or dragging and dropping files
   • Navigate through pages using "Previous Page" and "Next Page" buttons
   • Smooth scrolling and zooming capabilities

2. Text Selection and Copying

   • Select text from PDFs using a blue rectangle selection tool
   • Copy selected text to clipboard automatically
   • Works with both text-based and image-based (scanned) PDFs

3. Optical Character Recognition (OCR)

   • Extract text from images and scanned documents within PDFs
   • Recognize and copy text from photographs or handwritten notes

4. AI-Powered Analysis

   • Highlight text and submit it for AI analysis
   • Submit entire PDF pages for comprehensive AI review
   • Interact with an AI assistant for explanations, summaries, and insights

5. Web Search Integration

   • Perform web searches directly from the application
   • AI can initiate searches based on context and provide summarized results

6. LaTeX Equation Recognition

   • Convert images of mathematical equations to LaTeX expressions
   • Render LaTeX equations as images in the chat history

7. Customizable Interface

   • Toggle the AI chat panel visibility
   • Adjust font settings for the chat history

Installation

1. Clone the repository:

   git clone https://github.com/your-username/pdf-study-assistant.git
   cd pdf-study-assistant
   

2. Create and activate a virtual environment:

   python -m venv environ
   source environ/bin/activate  # On Windows, use: environ\Scripts\activate
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Install Tesseract OCR:

   • Windows: Download and install from Tesseract GitHub
   • macOS: brew install tesseract
   • Linux: sudo apt-get install tesseract-ocr

5. Set up environment variables: Create a .env file in the project root and add your API keys:

   OPENROUTER_API_KEY=your_openrouter_api_key
   SERPAPI_API_KEY=your_serpapi_api_key
   

6. Run the application:

   python main.py
   

Usage and AI Interaction

1. Loading a PDF

   • Click "Browse PDF" or drag and drop a PDF file onto the application window

2. Text Selection and Copying

   • Click and drag to create a blue rectangle over the desired text
   • Text is automatically copied to clipboard upon release

3. Highlighting for AI Analysis

   • Click the "Highlight" button to enter highlight mode
   • Create an orange highlight over text or equations
   • Click "Submit Highlighted Text" to send for AI analysis

4. Chatting with AI

   • Type messages in the input field at the bottom of the AI panel
   • Use "/chat" command for general queries:

     /chat Explain the concept of quantum entanglement
     

   • Use "/search" command for web searches:

     /search Latest advancements in renewable energy
     

5. Submitting PDF Content

   • Click "Submit PDF to AI" to analyze the current page and last two pages

6. LaTeX Equation Recognition

   • Highlight an equation image and submit it for analysis
   • The AI will attempt to convert it to a LaTeX expression

7. OCR for Scanned Documents

   • Select text from scanned or image-based PDFs as you would with regular PDFs
   • The application will automatically use OCR to extract the text

Tips for Best Results

• Ensure good lighting and image quality when working with scanned documents
• For equation recognition, try to highlight only the equation, minimizing surrounding text
• Use specific queries when interacting with the AI for more accurate responses

Troubleshooting

• If OCR is not working, ensure Tesseract is properly installed and in your system PATH
• For LaTeX rendering issues, make sure you have a LaTeX distribution installed on your system

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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