Update README.md

README.md

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-# SVG-SAMurai 🗡️
-
-**SVG-SAMurai** is an interactive, Streamlit-based web application that leverages the power of Meta's **Segment Anything Model (SAM)** to transform raster (PNG, JPG) and vector (SVG) images into precisely segmented, editable SVG paths.
-
-Whether you're starting from a flat image or an existing SVG file, SVG-SAMurai allows you to click on regions of interest, predict their boundaries, and inject those precise vector paths back into a master SVG document.
-
-## 🌟 Features
-
-- **Interactive Segmentation:** Click on any part of an uploaded image to instantly generate an accurate mask using the SAM Vision Transformer (`facebook/sam-vit-base`).
-- **Support for Multiple Formats:** Upload PNG, JPG, or SVG files. Vector images are rasterized cleanly in the backend for processing, allowing you to segment them seamlessly.
-- **Smart Vectorization:** Extracted masks are converted into optimized SVG `<path>` elements using the Ramer-Douglas-Peucker algorithm (via OpenCV) for smooth, simplified contours.
-- **Adjustable Simplification:** Fine-tune the vectorization epsilon factor directly from the UI to control the complexity of the generated paths.
-- **Segment Management:** Name your segments and save them into a live-updating SVG document.
-- **In-Memory Caching:** Heavy image embeddings are cached securely using Streamlit's `@st.cache_data` and `@st.cache_resource`, ensuring snappy performance and instant mask prediction on subsequent clicks.
-- **Easy Export:** Download the final composed SVG with all your tagged, labeled segments neatly organized in `<g>` groups.
-
-## 🛠️ Tech Stack
-
-* **Frontend:** [Streamlit](https://streamlit.io/), [streamlit-image-coordinates](https://pypi.org/project/streamlit-image-coordinates/)
-* **Machine Learning:** [PyTorch](https://pytorch.org/), [Hugging Face Transformers](https://huggingface.co/docs/transformers/index) (Segment Anything Model)
-* **Image Processing:** [OpenCV](https://opencv.org/) (Contour extraction & smoothing), [Pillow (PIL)](https://python-pillow.org/)
-* **SVG / DOM Manipulation:** [lxml](https://lxml.de/) (XML parsing and injection), [CairoSVG](https://cairosvg.org/) (SVG rasterization)
-* **Dependency Management:** [Poetry](https://python-poetry.org/)
-
-## 🚀 Quick Start
-
-### Prerequisites
-
-- Python `>=3.10, <3.13` (Required for PyTorch and Triton compatibility)
-- [Poetry](https://python-poetry.org/docs/#installation) installed on your system.
-- System dependencies for CairoSVG and OpenCV (e.g., `libcairo2-dev`, `libgl1-mesa-glx` on Ubuntu/Debian).
-
-### Installation
-
-1. **Clone the repository:**
-   ```bash
-   git clone <repository-url>
-   cd svg-samurai
-   ```
-
-2. **Install dependencies using Poetry:**
-   ```bash
-   poetry install
-   ```
-
-### Running the App
-
-Start the Streamlit development server:
-
-```bash
-poetry run streamlit run app.py
-```
-
-The application will launch in your default web browser at `http://localhost:8501`.
-
-## 📖 How to Use
-
-1. **Upload an Image:** Use the file uploader to select a PNG, JPG, or SVG file. The app will calculate the complex image embeddings once (this may take a few moments depending on your hardware).
-2. **Select Segments:** Click anywhere on the image in the left panel to prompt the model.
-   - *Tip:* You can toggle the "Next Click is Negative Prompt" checkbox in the sidebar to exclude specific regions from your mask.
-3. **Refine & Save:**
-   - Use the "Undo Last Click" or "Clear Current Selection" buttons to fix mistakes.
-   - Give your highlighted segment a descriptive name (e.g., `car_body`).
-   - Adjust the **Simplification (epsilon)** slider if you want fewer, smoother nodes in your final vector path.
-   - Click **Save Segment to SVG**.
-4. **Download:** Once you have saved all desired segments, click the **Download Final SVG** button to retrieve your newly layered vector graphic.
-
-## 📂 Project Structure
-
-```text
-svg-samurai/
-├── app.py                  # Main Streamlit user interface and application state
-├── pyproject.toml          # Poetry dependencies and project configuration
-├── src/                    # Backend logic
-│   ├── model.py            # PyTorch SAM loading, embedding generation, and mask prediction
-│   ├── vectorizer.py       # OpenCV contour extraction and SVG path conversion
-│   └── xml_manager.py      # lxml DOM manipulation and CairoSVG rasterization utilities
-└── tests/                  # Unit tests for core logic
-    ├── test_model.py
-    ├── test_vectorizer.py
-    └── test_xml_manager.py
-```
-
-## 🧪 Testing
-
-The project uses `pytest` for unit testing. To run the test suite, simply execute:
-
-```bash
-poetry run pytest
-```
-
 ---
-*Developed with Streamlit and Meta's Segment Anything Model.*
+title: SVG SAMurai
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: docker
+app_port: 8501
+tags:
+- streamlit
+pinned: false
+short_description: Tool to turn an image to an SVG with named image sections
+license: mit
+---
+
+# Welcome to Streamlit!