Update README

Signed-off-by: Snehil Shah <[email protected]>

README.md

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 title: Multimodal Image Search Engine
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+emoji: 🔍
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 sdk: gradio
 sdk_version: 4.13.0
 app_file: app.py
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 license: mit
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<p align="center">
+  <h1 align="center">Multi-Modal Image Search Engine</h1>
+  <p align="center">
+    A Semantic Search Engine that understands the Content & Context of your Queries. 
+    <br>
+    Use Multi-Modal inputs like Text-Image or a Reverse Image Search to Query a Vector Database of over 15k Images. <a href="https://huggingface.co/spaces/Snehil-Shah/Multimodal-Image-Search-Engine">Try it Out!</a>
+    <br><br>
+    <img src="https://github.com/Snehil-Shah/Multimodal-Image-Search-Engine/blob/main/assets/demo.gif?raw=true">
+  </p>
+</p>
+
+<h3>• About The Project</h3>
+
+At its core, the Search Engine is built upon the concept of **Vector Similarity Search**.
+All the Images are encoded into vector embeddings based on their semantic meaning using a Transformer Model, which are then stored in a vector space.
+When searched with a query, it returns the nearest neighbors to the input query which are the relevant search results.
+
+<p align="center"><img src="https://raw.githubusercontent.com/Snehil-Shah/Multimodal-Image-Search-Engine/main/assets/encoding_flow.png"></p>
+
+We use the Contrastive Language-Image Pre-Training (CLIP) Model by OpenAI which is a Pre-trained Multi-Modal Vision Transformer that can semantically encode Words, Sentences & Images into a 512 Dimensional Vector. This Vector encapsulates the meaning & context of the entity into a *Mathematically Measurable* format.
+
+<p align="center"><p align="center"><img src="https://raw.githubusercontent.com/Snehil-Shah/Multimodal-Image-Search-Engine/main/assets/Visualization.png" width=1000></p>
+<p align="center"><i>2-D Visualization of 500 Images in a 512-D Vector Space</i></p></p>
+
+The Images are stored as vector embeddings in a Qdrant Collection which is a Vector Database. The Search Term is encoded and run as a query to Qdrant, which returns the Nearest Neighbors based on their Cosine-Similarity to the Search Query.
+
+<p align="center"><img src="https://raw.githubusercontent.com/Snehil-Shah/Multimodal-Image-Search-Engine/main/assets/retrieval_flow.png"></p>
+
+**The Dataset**: All images are sourced from the [Open Images Dataset](https://github.com/cvdfoundation/open-images-dataset) by Common Visual Data Foundation.
+
+<h3>• Technologies Used</h3>
+
+- Python
+- Jupyter Notebooks
+- Qdrant - Vector Database
+- Sentence-Transformers - Library
+- CLIP by OpenAI - ViT Model
+- Gradio - UI
+- HuggingFace Spaces - Deployment
+
+