updated readme

README.md

@@ -1,14 +1,27 @@
+---
+title: ImageMosaicGenerator
+emoji: 🏆
+colorFrom: green
+colorTo: pink
+sdk: gradio
+sdk_version: 5.45.0
+app_file: app.py
+pinned: false
+---
+
 # Image Mosaic Generator
 
 Turn any image into a photomosaic composed of color-matched tiles while collecting detailed performance metrics. This Gradio app ships with built-in benchmarking tools, profiling utilities, and reproducible analysis documented in `profiling_analysis.ipynb`.
 
 ## Features
+
 - **Interactive mosaic builder** with selectable grid size, color palette, and reference image size.
 - **Performance benchmark tab** that sweeps multiple configurations, plots the results, and logs cProfile + line_profiler stats.
 - **Automatic tile caching & vectorized mapping** for sub-second generation on 1024×1024 inputs.
 - **Validation & error messaging** to catch invalid grid/image combinations before processing.
 
 ## Installation
+
 ```bash
 # clone the repository
  git clone https://huggingface.co/spaces/meryadri/ImageMosaicGenerator
@@ -23,29 +36,38 @@ Turn any image into a photomosaic composed of color-matched tiles while collecti
 ```
 
 ## Usage
+
 ### Local Gradio app
+
 ```bash
 python app.py
 ```
+
 Visit `http://127.0.0.1:7860` and either upload an image or choose one of the curated examples. Ensure the selected grid size divides the target image size (e.g., 512px image with 32×32 grid) for best results.
 
 ### Command-line benchmarking
+
 The easiest way to reproduce the profiling numbers is to run the Performance Benchmark tab once. The first pass warms the tile cache; subsequent runs deliver the <10ms timings shown in the notebook.
 
 ## Deployed Demo
+
 The project is available as a Hugging Face Space: [ImageMosaicGenerator](https://huggingface.co/spaces/meryadri/ImageMosaicGenerator). The space automatically stays in sync with this repository.
 
 ## Profiling Report
+
 See [`profiling_analysis.ipynb`](profiling_analysis.ipynb) for:
+
 - benchmark data (before/after speedups)
 - cProfile summaries & line-profiler excerpts
 - discussion of optimized bottlenecks (tile caching, vectorized grid processing, preprocessing reuse)
 
 ## Contributing
+
 1. Fork the repository and open a feature branch.
 2. Run `python app.py` and ensure both Gradio tabs still function.
 3. Update the profiling notebook or README if your change affects performance.
 4. Submit a pull request with a brief summary of the improvement.
 
 ## License
+
 This project inherits the usage terms of the Hugging Face Space it powers. Refer to the Space card for additional information.