<p align="center" style="margin: 0; padding: 0;">
<img src="llamarker/assets/Llamarker_logo.png" alt="LlaMarker Logo" width="200">
</p>
<h1 align="center">🖍️ LlaMarker</h1>
<p align="center">
<b>Your go-to tool for converting and parsing documents into clean, well-structured Markdown!</b><br>
<i>Fast, intuitive, and entirely local 💻🚀.</i>
</p>
<div align="center">
[](https://www.python.org/downloads/release/python-380/)
[](https://github.com/VikParuchuri/marker)
[](https://pypi.org/project/llamarker/)
</div>
---
## ✨ Key Features
- ✨ **All-in-One Parsing**
Supports **TXT**, **DOCX**, **PDF**, **PPT**, **XLSX**, and more—even processes images inside documents.
- 🖼️ **Visual Content Extraction**
Utilizes **Llama 3.2 Vision** to detect images, tables, charts, and diagrams, converting them into rich Markdown.
- 🏗️ **Built with Marker**
Extends the open-source [Marker](https://github.com/VikParuchuri/marker) parser to handle complex content types **locally**.
- 🛡️ **Local-First Privacy**
No cloud, no external servers—**all processing** happens on your machine.
---
## 🚀 How It Works
1. **Parsing & Conversion**
- Parses and converts multiple file types (.txt, .docx, .pdf, .ppt, .xlsx, etc.) into Markdown.
- Leverages **Marker** for accurate and efficient parsing of both text and visual elements.
- Extracts images, charts, and tables, embedding them in Markdown.
- _(Optional)_ Converts documents into PDFs using **LibreOffice** for easy viewing.
2. **Visual Analysis**
- Distinguishes logos from content-rich images.
- Extracts and preserves the original language from images.
- Uses multiple agents to extract useful information from the images.
3. **Fast & Efficient**
- Supports parallel processing for faster handling of large folders.
4. **Streamlit GUI**
- A user-friendly interface to upload and parse files (or multiple files at once!) or entire directories.
- Download results directly from the GUI.
---
## 📑 Table of Contents
1. [Features](#features)
2. [Prerequisites](#prerequisites)
3. [Installation Options](#installation-options)
- [Install via PyPI](#install-via-pypi)
- [Local Development Setup](#local-development-setup)
4. [Basic Usage](#basic-usage)
- [CLI Usage](#cli-usage)
- [Streamlit GUI](#streamlit-gui)
5. [Advanced Usage](#advanced-usage)
- [Command-Line Arguments](#command-line-arguments)
- [Example Commands](#example-commands)
6. [Output Structure](#output-structure)
7. [Code Example](#code-example)
8. [Contributing](#contributing)
9. [License](#license)
10. [Acknowledgments](#acknowledgments)
---
## ✨ Features
- 📄 **Document Conversion**
Converts `.txt`, `.docx`, and other supported file types into `.pdf` using **LibreOffice** (optional if you only need to parse PDFs).
- 📊 **Page Counting**
Automatically counts pages in PDFs using **PyPDF2**.
- 🖼️ **Image Processing**
Analyzes images to differentiate logos from content-rich images. Extracts relevant data and updates the corresponding Markdown file.
- ✍️ **Markdown Parsing**
Uses **Marker** to generate clean, structured Markdown files from parsed PDFs.
- 🌐 **Multilingual Support**
Maintains the original language of the content during extraction.
- 📈 **Data Visualization**
Generates analysis plots based on the page counts of processed documents.
---
## ⚙️ Prerequisites
Before installing or running **LlaMarker**, please ensure you meet the following requirements:
1. **Python 3.10+**
- Core language for running **LlaMarker**.
- Verify your Python version:
```bash
python --version
```
2. **Marker**
- [Marker](https://github.com/VikParuchuri/marker) is an open-source parser that **LlaMarker** extends.
- For a quick install, you can try:
```bash
pip install marker-pdf
```
This installs Marker’s **PDF** parsing capabilities.
- If you plan to leverage GPUs, ensure **PyTorch** is installed with **CUDA** support (e.g., via `pytorch-cuda` or the official PyTorch distribution).
- For advanced installation or customization, refer to the [official Marker GitHub repository](https://github.com/VikParuchuri/marker) for detailed instructions on cloning and building from source.
- If installed, ensure Marker is in your `PATH` or specify its location with the `--marker_path` argument.
3. **LibreOffice**
- Required for converting `.docx`, `.ppt`, `.xlsx`, etc., into `.pdf` before parsing.
- **Linux** (Ubuntu/Debian example):
```bash
sudo apt update
sudo apt install libreoffice
```
- **Windows**:
[Download the installer](https://www.libreoffice.org/download/download/) and consider adding LibreOffice to your system `PATH`.
- **macOS**:
- Download from [LibreOffice’s website](https://www.libreoffice.org/download/download/) or
- Use Homebrew:
```bash
brew install --cask libreoffice
```
4. **Ollama & Vision Models**
- [Install Ollama](https://github.com/jmorganca/ollama) for your OS.
- Pull the required model:
```bash
ollama pull llama3.2-vision
```
- Test run to ensure your model is set up correctly.
5. **Poetry** (for local development only)
- Recommended dependency manager if you’re cloning the repository to develop or modify **LlaMarker**.
- **Linux/Mac**:
```bash
curl -sSL https://install.python-poetry.org | python3 -
# (If not added to PATH automatically)
export PATH="$HOME/.local/bin:$PATH"
```
- **macOS (Homebrew)**:
```bash
brew install poetry
```
- **Windows**:
Follow instructions on [Poetry’s official site](https://python-poetry.org/docs/#installation).
---
## 🚀 Installation Options
### 1. Install via PyPI
The simplest approach—ideal if you just want to **use** LlaMarker rather than develop it:
```bash
pip install llamarker
```
- **Requires**: Python 3.10+
- After installing, you have access to two main commands:
1. `llamarker` — CLI tool.
2. `llamarker_gui` — Streamlit-based GUI for interactive use.
> **Note**: LibreOffice, Marker, and any optional OCR components need to be installed separately, if you plan to use their respective features.
---
### 2. Local Development Setup
If you plan to contribute or dive into the source code:
1. **Clone the repository**:
```bash
git clone https://github.com/RevanKumarD/LlaMarker.git
cd LlaMarker
```
2. **Install dependencies** using **Poetry**:
```bash
poetry install
```
3. **Run LlaMarker locally**:
- **CLI**:
```bash
poetry run python llamarker/llamarker.py --directory <directory_path>
```
- **GUI**:
```bash
poetry run streamlit run llamarker/llamarker_gui.py
```
> No `requirements.txt` is provided; **Poetry** is the recommended (and supported) method for local development.
---
## 📌 Basic Usage
### CLI Usage
#### Installed via PyPI
- **Process a folder**:
```bash
llamarker --directory <directory_path>
```
- **Process a single file**:
```bash
llamarker --file <file_path>
```
#### Local Development
- **CLI**:
```bash
poetry run python llamarker/llamarker.py --directory <directory_path>
```
---
### Streamlit GUI
A user-friendly interface to upload files/directories, parse them, and download results.
- **Installed via PyPI**:
```bash
llamarker_gui
```
- **Local Development**:
```bash
poetry run streamlit run llamarker/llamarker_gui.py
```
Open the link (e.g., `http://localhost:8501`) in your browser to start using **LlaMarker** via GUI.
---
## 🔧 Advanced Usage
### Command-Line Arguments
| Argument | Description |
| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--directory` | **Root directory** containing documents to process. |
| `--file` | Path to a single file to process (optional). |
| `--temp_dir` | Temporary directory for intermediate files (optional). |
| `--save_pdfs` | Flag to **save PDFs** in a separate directory (`PDFs`) under the root directory. |
| `--output` | Directory to **save output** files (optional). By default, parsed Markdown files are stored in `ParsedFiles` and images go under `ParsedFiles/pics`. |
| `--marker_path` | Path to the **Marker** executable (optional). Auto-detects if `Marker` is in your `PATH`. |
| `--force_ocr` | Force **OCR** on all pages, even if text is extractable. Useful for poorly formatted PDFs or PPTs. |
| `--languages` | Comma-separated list of languages for OCR (default: `"en"`). |
| `--qa_evaluator` | Enable **QA Evaluator** for selecting the best response during image processing. |
| `--verbose` | Set verbosity level: **0** = WARNING, **1** = INFO, **2** = DEBUG (default: **0**). |
| `--model` | **Ollama** model for image analysis (default: `llama3.2-vision`). A local vision model is required for this to work. |
---
### Example Commands
1. **Directory processing**:
```bash
llamarker --directory /path/to/documents
```
2. **Single file with verbose output**:
```bash
llamarker --file /path/to/document.docx --verbose 2
```
3. **Parsing with OCR in multiple languages**:
```bash
llamarker --directory /path/to/docs --force_ocr --languages "en,de,fr"
```
4. **Save parsed PDFs to a custom folder**:
```bash
llamarker --directory /path/to/docs --save_pdfs --output /path/to/output
```
---
## Output Structure
After processing, **LlaMarker** organizes files as follows:
- **`ParsedFiles`**
- Contains the generated Markdown files.
- **`pics`** — subfolder for extracted images.
- **`PDFs`**
- Stores converted PDF files (if `--save_pdfs` is used).
- **`OutDir`**
- Contains processed PDF files (used by the GUI).
- **`logs`**
- Holds log files for each run (processing status, errors, etc.).
---
## Code Example
For local development, you can programmatically use **LlaMarker**:
```python
from llamarker import LlaMarker
llamarker = LlaMarker(
input_dir="/path/to/documents",
save_pdfs=True,
output_dir="/path/to/output",
verbose=1
)
# Process all documents in the specified directory
llamarker.process_documents()
# Generate summary info
results = llamarker.generate_summary()
for file, page_count in results:
print(f"{file}: {page_count} pages")
# Generate analysis plots
llamarker.plot_analysis(llamarker.parent_dir)
```
---
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request. Let’s make **LlaMarker** even more powerful—together. 🤝
---
## License
This project references the [Marker](https://github.com/VikParuchuri/marker) repository, which comes with its own license. Please review the Marker repo for licensing restrictions and guidelines.
© 2025 Revan Kumar Dhanasekaran. Released under the GPLv3 License.
---
## Acknowledgments
- **Huge thanks** to the [Marker](https://github.com/VikParuchuri/marker) project for providing an excellent foundation for parsing.
- **Special thanks** to the open-source community for continuous support and contributions.
---
<p align="center">
<b>Happy Parsing!</b> 🌟
</p>
Raw data
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"description": "<p align=\"center\" style=\"margin: 0; padding: 0;\">\n <img src=\"llamarker/assets/Llamarker_logo.png\" alt=\"LlaMarker Logo\" width=\"200\">\n</p>\n\n<h1 align=\"center\">\ud83d\udd8d\ufe0f LlaMarker</h1>\n\n<p align=\"center\">\n <b>Your go-to tool for converting and parsing documents into clean, well-structured Markdown!</b><br>\n <i>Fast, intuitive, and entirely local \ud83d\udcbb\ud83d\ude80.</i>\n</p>\n\n<div align=\"center\">\n \n[](https://www.python.org/downloads/release/python-380/)\n[](https://github.com/VikParuchuri/marker)\n[](https://pypi.org/project/llamarker/)\n\n</div>\n\n---\n\n## \u2728 Key Features\n\n- \u2728 **All-in-One Parsing** \n Supports **TXT**, **DOCX**, **PDF**, **PPT**, **XLSX**, and more\u2014even processes images inside documents.\n\n- \ud83d\uddbc\ufe0f **Visual Content Extraction** \n Utilizes **Llama 3.2 Vision** to detect images, tables, charts, and diagrams, converting them into rich Markdown.\n\n- \ud83c\udfd7\ufe0f **Built with Marker** \n Extends the open-source [Marker](https://github.com/VikParuchuri/marker) parser to handle complex content types **locally**.\n\n- \ud83d\udee1\ufe0f **Local-First Privacy** \n No cloud, no external servers\u2014**all processing** happens on your machine.\n\n---\n\n## \ud83d\ude80 How It Works\n\n1. **Parsing & Conversion**\n\n - Parses and converts multiple file types (.txt, .docx, .pdf, .ppt, .xlsx, etc.) into Markdown.\n - Leverages **Marker** for accurate and efficient parsing of both text and visual elements.\n - Extracts images, charts, and tables, embedding them in Markdown.\n - _(Optional)_ Converts documents into PDFs using **LibreOffice** for easy viewing.\n\n2. **Visual Analysis**\n\n - Distinguishes logos from content-rich images.\n - Extracts and preserves the original language from images.\n - Uses multiple agents to extract useful information from the images.\n\n3. **Fast & Efficient**\n\n - Supports parallel processing for faster handling of large folders.\n\n4. **Streamlit GUI**\n - A user-friendly interface to upload and parse files (or multiple files at once!) or entire directories.\n - Download results directly from the GUI.\n\n---\n\n## \ud83d\udcd1 Table of Contents\n\n1. [Features](#features)\n2. [Prerequisites](#prerequisites)\n3. [Installation Options](#installation-options)\n - [Install via PyPI](#install-via-pypi)\n - [Local Development Setup](#local-development-setup)\n4. [Basic Usage](#basic-usage)\n - [CLI Usage](#cli-usage)\n - [Streamlit GUI](#streamlit-gui)\n5. [Advanced Usage](#advanced-usage)\n - [Command-Line Arguments](#command-line-arguments)\n - [Example Commands](#example-commands)\n6. [Output Structure](#output-structure)\n7. [Code Example](#code-example)\n8. [Contributing](#contributing)\n9. [License](#license)\n10. [Acknowledgments](#acknowledgments)\n\n---\n\n## \u2728 Features\n\n- \ud83d\udcc4 **Document Conversion** \n Converts `.txt`, `.docx`, and other supported file types into `.pdf` using **LibreOffice** (optional if you only need to parse PDFs).\n\n- \ud83d\udcca **Page Counting** \n Automatically counts pages in PDFs using **PyPDF2**.\n\n- \ud83d\uddbc\ufe0f **Image Processing** \n Analyzes images to differentiate logos from content-rich images. Extracts relevant data and updates the corresponding Markdown file.\n\n- \u270d\ufe0f **Markdown Parsing** \n Uses **Marker** to generate clean, structured Markdown files from parsed PDFs.\n\n- \ud83c\udf10 **Multilingual Support** \n Maintains the original language of the content during extraction.\n\n- \ud83d\udcc8 **Data Visualization** \n Generates analysis plots based on the page counts of processed documents.\n\n---\n\n## \u2699\ufe0f Prerequisites\n\nBefore installing or running **LlaMarker**, please ensure you meet the following requirements:\n\n1. **Python 3.10+**\n\n - Core language for running **LlaMarker**.\n - Verify your Python version:\n ```bash\n python --version\n ```\n\n2. **Marker**\n\n - [Marker](https://github.com/VikParuchuri/marker) is an open-source parser that **LlaMarker** extends.\n - For a quick install, you can try:\n ```bash\n pip install marker-pdf\n ```\n This installs Marker\u2019s **PDF** parsing capabilities.\n - If you plan to leverage GPUs, ensure **PyTorch** is installed with **CUDA** support (e.g., via `pytorch-cuda` or the official PyTorch distribution).\n - For advanced installation or customization, refer to the [official Marker GitHub repository](https://github.com/VikParuchuri/marker) for detailed instructions on cloning and building from source.\n - If installed, ensure Marker is in your `PATH` or specify its location with the `--marker_path` argument.\n\n3. **LibreOffice**\n\n - Required for converting `.docx`, `.ppt`, `.xlsx`, etc., into `.pdf` before parsing.\n - **Linux** (Ubuntu/Debian example):\n ```bash\n sudo apt update\n sudo apt install libreoffice\n ```\n - **Windows**: \n [Download the installer](https://www.libreoffice.org/download/download/) and consider adding LibreOffice to your system `PATH`.\n - **macOS**:\n - Download from [LibreOffice\u2019s website](https://www.libreoffice.org/download/download/) or\n - Use Homebrew:\n ```bash\n brew install --cask libreoffice\n ```\n\n4. **Ollama & Vision Models**\n\n - [Install Ollama](https://github.com/jmorganca/ollama) for your OS.\n - Pull the required model:\n ```bash\n ollama pull llama3.2-vision\n ```\n - Test run to ensure your model is set up correctly.\n\n5. **Poetry** (for local development only)\n - Recommended dependency manager if you\u2019re cloning the repository to develop or modify **LlaMarker**.\n - **Linux/Mac**:\n ```bash\n curl -sSL https://install.python-poetry.org | python3 -\n # (If not added to PATH automatically)\n export PATH=\"$HOME/.local/bin:$PATH\"\n ```\n - **macOS (Homebrew)**:\n ```bash\n brew install poetry\n ```\n - **Windows**: \n Follow instructions on [Poetry\u2019s official site](https://python-poetry.org/docs/#installation).\n\n---\n\n## \ud83d\ude80 Installation Options\n\n### 1. Install via PyPI\n\nThe simplest approach\u2014ideal if you just want to **use** LlaMarker rather than develop it:\n\n```bash\npip install llamarker\n```\n\n- **Requires**: Python 3.10+\n- After installing, you have access to two main commands:\n 1. `llamarker` \u2014 CLI tool.\n 2. `llamarker_gui` \u2014 Streamlit-based GUI for interactive use.\n\n> **Note**: LibreOffice, Marker, and any optional OCR components need to be installed separately, if you plan to use their respective features.\n\n---\n\n### 2. Local Development Setup\n\nIf you plan to contribute or dive into the source code:\n\n1. **Clone the repository**:\n ```bash\n git clone https://github.com/RevanKumarD/LlaMarker.git\n cd LlaMarker\n ```\n2. **Install dependencies** using **Poetry**:\n ```bash\n poetry install\n ```\n3. **Run LlaMarker locally**:\n - **CLI**:\n ```bash\n poetry run python llamarker/llamarker.py --directory <directory_path>\n ```\n - **GUI**:\n ```bash\n poetry run streamlit run llamarker/llamarker_gui.py\n ```\n\n> No `requirements.txt` is provided; **Poetry** is the recommended (and supported) method for local development.\n\n---\n\n## \ud83d\udccc Basic Usage\n\n### CLI Usage\n\n#### Installed via PyPI\n\n- **Process a folder**:\n ```bash\n llamarker --directory <directory_path>\n ```\n- **Process a single file**:\n ```bash\n llamarker --file <file_path>\n ```\n\n#### Local Development\n\n- **CLI**:\n ```bash\n poetry run python llamarker/llamarker.py --directory <directory_path>\n ```\n\n---\n\n### Streamlit GUI\n\nA user-friendly interface to upload files/directories, parse them, and download results.\n\n- **Installed via PyPI**:\n ```bash\n llamarker_gui\n ```\n- **Local Development**:\n ```bash\n poetry run streamlit run llamarker/llamarker_gui.py\n ```\n\nOpen the link (e.g., `http://localhost:8501`) in your browser to start using **LlaMarker** via GUI.\n\n---\n\n## \ud83d\udd27 Advanced Usage\n\n### Command-Line Arguments\n\n| Argument | Description |\n| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |\n| `--directory` | **Root directory** containing documents to process. |\n| `--file` | Path to a single file to process (optional). |\n| `--temp_dir` | Temporary directory for intermediate files (optional). |\n| `--save_pdfs` | Flag to **save PDFs** in a separate directory (`PDFs`) under the root directory. |\n| `--output` | Directory to **save output** files (optional). By default, parsed Markdown files are stored in `ParsedFiles` and images go under `ParsedFiles/pics`. |\n| `--marker_path` | Path to the **Marker** executable (optional). Auto-detects if `Marker` is in your `PATH`. |\n| `--force_ocr` | Force **OCR** on all pages, even if text is extractable. Useful for poorly formatted PDFs or PPTs. |\n| `--languages` | Comma-separated list of languages for OCR (default: `\"en\"`). |\n| `--qa_evaluator` | Enable **QA Evaluator** for selecting the best response during image processing. |\n| `--verbose` | Set verbosity level: **0** = WARNING, **1** = INFO, **2** = DEBUG (default: **0**). |\n| `--model` | **Ollama** model for image analysis (default: `llama3.2-vision`). A local vision model is required for this to work. |\n\n---\n\n### Example Commands\n\n1. **Directory processing**:\n ```bash\n llamarker --directory /path/to/documents\n ```\n2. **Single file with verbose output**:\n ```bash\n llamarker --file /path/to/document.docx --verbose 2\n ```\n3. **Parsing with OCR in multiple languages**:\n ```bash\n llamarker --directory /path/to/docs --force_ocr --languages \"en,de,fr\"\n ```\n4. **Save parsed PDFs to a custom folder**:\n ```bash\n llamarker --directory /path/to/docs --save_pdfs --output /path/to/output\n ```\n\n---\n\n## Output Structure\n\nAfter processing, **LlaMarker** organizes files as follows:\n\n- **`ParsedFiles`**\n - Contains the generated Markdown files.\n - **`pics`** \u2014 subfolder for extracted images.\n- **`PDFs`**\n - Stores converted PDF files (if `--save_pdfs` is used).\n- **`OutDir`**\n - Contains processed PDF files (used by the GUI).\n- **`logs`**\n - Holds log files for each run (processing status, errors, etc.).\n\n---\n\n## Code Example\n\nFor local development, you can programmatically use **LlaMarker**:\n\n```python\nfrom llamarker import LlaMarker\n\nllamarker = LlaMarker(\n input_dir=\"/path/to/documents\",\n save_pdfs=True,\n output_dir=\"/path/to/output\",\n verbose=1\n)\n\n# Process all documents in the specified directory\nllamarker.process_documents()\n\n# Generate summary info\nresults = llamarker.generate_summary()\nfor file, page_count in results:\n print(f\"{file}: {page_count} pages\")\n\n# Generate analysis plots\nllamarker.plot_analysis(llamarker.parent_dir)\n```\n\n---\n\n## Contributing\n\nContributions are welcome! Feel free to open an issue or submit a pull request. Let\u2019s make **LlaMarker** even more powerful\u2014together. \ud83e\udd1d\n\n---\n\n## License\n\nThis project references the [Marker](https://github.com/VikParuchuri/marker) repository, which comes with its own license. Please review the Marker repo for licensing restrictions and guidelines.\n\n\u00a9 2025 Revan Kumar Dhanasekaran. Released under the GPLv3 License.\n\n---\n\n## Acknowledgments\n\n- **Huge thanks** to the [Marker](https://github.com/VikParuchuri/marker) project for providing an excellent foundation for parsing.\n- **Special thanks** to the open-source community for continuous support and contributions.\n\n---\n\n<p align=\"center\">\n <b>Happy Parsing!</b> \ud83c\udf1f\n</p>\n",
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