README update

README.md

@@ -9,4 +9,56 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Shows Recs: TV Series Recommendation System
+
+![Streamlit](https://img.shields.io/badge/Streamlit-1.37.1-brightgreen.svg) ![Docker](https://img.shields.io/badge/Docker-20.10.7-blue.svg) ![Python](https://img.shields.io/badge/Python-3.9-blue.svg)
+
+## Overview
+
+**Shows Recs** is a web application that provides TV series recommendations based on user input. It uses Sentence Transformers and FAISS to search and recommend series by embedding text queries and comparing them to a precomputed index of embeddings.
+
+## Features
+
+- **Metric Selection**: Choose from `L2`, `Dot Product`, or `Cosine Similarity`.
+- **Customizable Recommendations**: Specify the number of recommendations to display.
+- **Fast and Accurate**: Leverages FAISS for efficient similarity search.
+
+## Installation & Usage
+
+### 1. Clone the Repository
+
+```bash
+git clone https://huggingface.co/skudinov/shows_recs.git
+cd shows_recs
+```
+
+### 2. Install Dependencies
+Create a virtual environment and install the required packages:
+```bash
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+### 3. Run the Application
+Run the Streamlit app locally:
+```bash
+streamlit run app.py
+```
+The app will be accessible at http://localhost:8501.
+
+### 4. Run with Docker
+- Build the Docker image:
+```bash
+docker build -t your_docker_account/your_docker_repo .
+```
+- Run the Docker container:
+```bash
+docker run -p 8501:8501 your_docker_account/your_docker_repo
+```
+
+## How to Use
+- Enter a query in the text input field.
+- Select a similarity metric (L2, Dot Product, Cosine Similarity).
+- Choose the number of recommendations.
+- Click "Search" to get a list of TV series recommendations.