updates

README.md

@@ -11,7 +11,7 @@ short_description: "Netflix title recommendations using cosine similarity."
 tags: ["recommendation", "netflix", "flask", "cosine-similarity", "docker"]
 ---
 
-# Netflix Content-Based Recommender
+# Netflix Recommender App
 
 A Flask-based web application that provides personalized recommendations for Netflix content using a content-based filtering approach.
 
@@ -27,19 +27,24 @@ Ensure you have the following installed:
 - **Node.js and npm** (Check with `node -v` and `npm -v`)
 - **pip** (Python package manager)
 
-## Installation
+
+## View application online (easiest method)
+https://huggingface.co/spaces/erichier/finalcapstone
+
+
+## Manual Installation
 
 ### 1. Clone the Repository
 ```sh
-git clone https://github.com/yourusername/flick_picker.git
-cd flick_picker
+git clone https://huggingface.co/spaces/erichier/finalcapstone
+cd finalcapstone
 ```
 
 ### 2. Set Up a Virtual Environment
 
 #### macOS & Linux
 ```sh
-python3 -m venv venv
+python3.12 -m venv venv
 source venv/bin/activate
 ```
 
@@ -63,27 +68,21 @@ npm install
 ```
 
 ### 4. Download Dataset
-Ensure `netflix_titles.csv` is placed in the project root directory.
+Verify `netflix_titles.csv` is placed present from git clone.
+Available here if needed: https://www.kaggle.com/datasets/shivamb/netflix-shows
 
-### 5. Build or Load Model
+### 5. Build or Load Model & Run Application
 Run the following command to preprocess the dataset and generate a similarity model:
 
 ```sh
-python recommend_app.py
+gunicorn -w 2 -b 0.0.0.0:7860 recommend_app:app
 ```
 
 This will load the Netflix dataset, process it, and save a cached similarity model (`cosine_sim_cache.pkl`).
 
-## Running the Application
-
-Once setup is complete, start the Flask server:
-
-```sh
-python recommend_app.py
-```
-
 The app will be available at:  
-[http://127.0.0.1:5020](http://127.0.0.1:5020)
+Access app at [http://0.0.0.0:7860] or [http://localhost:7860]
+
 
 ## Frontend Development
 If making changes to the frontend, ensure Node.js dependencies are installed. Run:
@@ -129,12 +128,11 @@ docker build -t my-recommend-app .
 docker run -p 7860:7860 my-recommend-app
 ```
 
-access app at http://0.0.0.0:7860
+access app at http://0.0.0.0:7860 or http://localhost:7860
 
-#### Run with gunicorn
+<!-- #### Run with gunicorn
 ```sh
 gunicorn -w 2 -b 0.0.0.0:7860 recommend_app:app
-```
+``` -->
+
 
-#### Just view online (easiest)
-https://huggingface.co/spaces/erichier/finalcapstone