Update README.md

README.md

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   <img src="https://huggingface.co/spaces/matanzig/Interior-Design-GenAI/resolve/main/viss1.jpeg" width="800"/>
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-<h1 align="center">VISIONARY: AI-Powered Interior Design Engine </h1>
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-**Matan Zigelman**  
-*Reichman University*
+  <h1>VISIONARY: AI-Powered Interior Design Engine</h1>
 
+  **Matan Zigelman**<br>
+  *Reichman University*
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 ## 📖 Project Overview
-Traditional visual search engines often rely on basic pixel-matching or color histograms, which struggle to grasp the structural and semantic nuances of architectural spaces. **Visionary** was developed as a comprehensive academic and technical showcase to solve this limitation. 
+**Visionary** is a machine learning application designed to provide context-aware interior design recommendations through deep semantic feature extraction. The core objective of the project is to explore how neural networks can understand the complex structural and stylistic nuances of an architectural space.
 
-By utilizing the `openai/clip-vit-base-patch32` vision-language model, this project projects raw images into a 512-dimensional semantic space. As a result, the system autonomously understands abstract concepts—such as "minimalist," "industrial," or "warm lighting"—and retrieves highly accurate room designs based on user inspiration photos or textual descriptions.
+By utilizing the `openai/clip-vit-base-patch32` vision-language model, the system converts raw images into 512-dimensional semantic embeddings. This mathematical representation enables the application to group and retrieve rooms based on abstract design concepts—such as "minimalist," "industrial," or "warm lighting"—allowing users to seamlessly discover relevant interior designs using either visual inspiration photos or text prompts.
 
 **Tech Stack:** `Python` | `PyTorch` | `Hugging Face (Transformers, Datasets)` | `Scikit-Learn (K-Means, t-SNE)` | `Pandas` | `Gradio`
 
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 ## 🗄️ 1. Dataset Architecture
 
-For the foundation of this engine, I selected the `my_interior_design_dataset` from Hugging Face. This dataset is exceptionally well-suited for high-level visual recommendation tasks.
+For the foundation of this engine, I selected the `my_interior_design_dataset` from Hugging Face. I specifically chose this dataset because its dual-label metadata provides a rich, multi-dimensional ground truth, allowing the evaluation of both functional and stylistic semantic similarities.
 
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