Update README.md

README.md

@@ -1,14 +1,56 @@
 ---
-title: Finalproject VEN
-emoji: 📉
-colorFrom: red
-colorTo: gray
+title: VEN - AI Restaurant Matchmaker
+emoji: 🍔
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
-sdk_version: 6.3.0
+sdk_version: 5.0.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: recommendation system
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🍔 VEN: Semantic Restaurant Discovery System
+
+**VEN** is an AI-powered recommendation engine designed to find the perfect restaurant in Tel Aviv based on user context, vibe, and specific preferences.
+
+Unlike traditional filters (e.g., "Italian", "$$"), VEN uses **Semantic Search** and **Persona Matching** to understand the *intent* behind the search (e.g., "A romantic quiet place for a date" vs. "Lively place for drinks with friends").
+
+## 🚀 Key Features
+
+* **Semantic Search:** Uses `Sentence-Transformers` (NLP) to convert user preferences into vector embeddings.
+* **Persona Matching:** Instead of simple keyword matching, the system identifies the user's "Persona" (Reviewer Type) based on their inputs and finds restaurants loved by similar people.
+* **Smart Filtering:** Filters by Budget, Diet, Company, Occasion, and Noise Level.
+* **One-Click Quick Starters:** Pre-configured scenarios for instant recommendations.
+* **Dark Mode UI:** A sleek, modern user interface built with Gradio.
+
+## 🛠️ Tech Stack
+
+* **Python 3.10+**
+* **Gradio:** For the web interface.
+* **Sentence-Transformers (`all-mpnet-base-v2`):** For generating high-quality text embeddings.
+* **Scikit-Learn:** For calculating Cosine Similarity between user input and reviewer profiles.
+* **Pandas & NumPy:** For data manipulation and vector operations.
+
+## 📂 Project Structure
+
+* `app.py`: The main application logic (UI, Search Engine, Logic).
+* `cleaned_dataset_10k.csv`: A processed dataset of 10,000 restaurant reviews from Tel Aviv.
+* `final_embeddings_10k.pkl`: Pre-computed vector embeddings for the reviews (saves processing time).
+* `requirements.txt`: List of dependencies required to run the app.
+
+## 🧠 How It Works
+
+1.  **Input:** The user selects their preferences (Budget, Diet, Company, Occasion, Atmosphere).
+2.  **Vectorization:** The system creates a textual description of the user's desire and converts it into a mathematical vector using the `all-mpnet-base-v2` model.
+3.  **Persona Clustering:** The system compares the user's vector against pre-calculated "Taste Profiles" (Personas) derived from the dataset.
+4.  **Similarity Search:** It calculates the **Cosine Similarity** to find the closest match.
+5.  **Ranking:** The system ranks restaurants within that Persona cluster and returns the highest-rated match with a relevant review.
+
+## 👥 Authors
+
+* **Matan** - Data Scientist & Developer
+* **Gal** - Data Scientist & Developer
+
+---
+*Created as a final project for the Data Analysis Course.*