README.md

---

title: Anime Recommendation System
emoji: 📚
colorFrom: purple
colorTo: yellow
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false
license: apache-2.0
---


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# Anime Recommendation system  

This is an **Anime Recommendation System** that combines multiple recommendation techniques such as **Collaborative Filtering**, **Content-Based Filtering**, and **Popularity-Based Filtering**. We used the **AnimeList 2023 dataset**, but to optimize computational cost and storage, we included only animes with an average rating above **6.0**. The system is designed for **continuous data ingestion and transformation** and is fully **dockerized** for seamless deployment.

The system is hosted on **Hugging Face Spaces** and fetches datasets and pre-trained models from **Hugging Face Hub** to generate **personalized anime recommendations** based on user preferences and anime features.

![assets/animes.jpg](assets/animes.jpg)

## Live Demo 🤗
[Anime Recommendation System App](https://huggingface.co/spaces/krishnaveni76/Anime-Recommendation-System) 

## Tech Stacks 🛠️

- **Python**: Main programming language used for building recommendation algorithms and Streamlit app.  
- **Docker**: Containerizes the application to ensure a consistent environment across different platforms.  
- **Streamlit**: For building and deploying the web app that serves the recommendations.  
- **Hugging Face Spaces**: Hosts the Streamlit-based recommendation system.  
- **Hugging Face Datasets**: Stores and retrieves anime datasets for processing.  
- **Hugging Face Models**: Stores the pre-trained recommendation models for inference.  

## Pipeline 🚀 

![assets/project_architecture.gif](assets/project_architecture.gif) 

The pipeline follows a structured sequence of steps to build an **Anime Recommendation System**, including data ingestion, transformation, and multiple recommendation models.

### 1. Data Ingestion 📥  
- Initiates the **data ingestion process**, where anime data is loaded from Hugging Face datasets.  
- The ingested data is saved as artifacts in a local folder for further processing.  

### 2. Data Transformation 🔄  
- Cleans, transforms, and processes the raw data into a structured format.  
- Extracts important features required for **Content-Based Filtering** and prepares data for **Collaborative Filtering**.  

### 3. Collaborative Filtering 🤝  
- Implements **three collaborative filtering models** to recommend anime based on user preferences:  
  - **Singular Value Decomposition (SVD)**: Factorizes the user-item interaction matrix to make personalized recommendations.  
  - **Item-Based K-Nearest Neighbors (Item-KNN)**: Recommends anime similar to a given anime based on user ratings.  
  - **User-Based K-Nearest Neighbors (User-KNN)**: Suggests anime that users with similar preferences have liked.  
- The chosen model is trained using **transformed data**, and the final trained model is stored as an artifact.  
- Once trained, it can generate recommendations for users or anime titles.  

![assets/collaborative and contentbased filtering.png](assets/collaborative_and_contentbased_filtering.png)

### 4. Content-Based Filtering 🎭  
- Uses extracted anime features like genres to train a **Content-Based Recommendation Model**.  
- This model recommends anime similar to those a user has watched or liked.  

### 5. Popularity-Based Filtering ⭐  
This recommendation system ranks anime based on various **popularity metrics**, making it ideal for users who want to discover trending or highly-rated shows **without needing personalized preferences**.  

The system applies different filters to sort anime based on:  

- **Most Popular** 🎭: Anime ranked by **popularity score**, highlighting the most widely recognized titles.  
- **Top Ranked** 🏆: Highest-rated anime, based on **official ranking metrics**.  
- **Overall Top Rated** ⭐: Best-rated anime, sorted by **average user ratings**.  
- **Most Favorited** ❤️: Anime with the highest number of **favorites**, indicating strong fan appreciation.  
- **Highest Member Count** 👥: Anime with the largest **viewer base**, showing widespread appeal.  
- **Popular Among Members** 🔥: Anime with a **high number of members and strong ratings**, making them community favorites.  
- **Highest Average Rating** 🎖️: Shows that have the **best average rating** after handling missing values.  

## Artifacts Storage 📂  

All intermediate and final outputs, including processed datasets and trained models, are first saved locally in the **Artifacts** folder. These artifacts are then uploaded to **Hugging Face** for efficient storage and easy access.  

When building the **Streamlit** app, these datasets and trained models are retrieved directly from **Hugging Face**, ensuring seamless integration and scalability.  

![assets/Artifacts.png](assets/artifacts.png)

- The datasets used in this project are available at:  
    - [Anime and User Ratings](https://www.kaggle.com/datasets/krishnaveniponna/anime-and-ratings-list-dataset-2023)  
      

- You can find the Artifacts of trained models here:  

    - [Pre-trained Models](https://huggingface.co/krishnaveni76/anime-recommendation-models)  


## Deployment on Hugging Face Spaces 🚀 

This project is deployed using **Hugging Face Spaces**, which provides a seamless way to host **Streamlit applications**. The application pulls the datasets and trained models from Hugging Face and runs inside a **Docker container**.

### Pre-requisites  
- Docker  
- Hugging Face (for datasets and trained models)  
- Python 3.8+  
- Hugging Face Spaces account
 
### Local step 🔧
1. **Clone the repository**
```bash

   git clone https://huggingface.co/spaces/krishnaveni76/Anime-Recommendation-System

   cd Anime-Recommendation-System

``` 
2. **Set Up a Virtual Environment**:
```bash

# For macOS and Linux:

python3 -m venv venv 

# For Windows:

python -m venv venv

``` 
3. **Activate the Virtual Environment**:
```bash

# For macOS and Linux:

source venv/bin/activate 

# For Windows:

.\venv\Scripts\activate

``` 
4. **Install Required Dependencies**:
```bash

pip install -r requirements.txt

```

### Running the Pipeline 🔄
To process the data and train the recommendation models, run the following command:

```bash 

python run_pipeline.py

```
This will execute the pipeline, ingest and transform data, and train the models before making recommendations.

### Running with Docker 🚀
To run the application inside a Docker container, follow these steps:

1. Build the Docker Image 
```bash

docker build -t anime-recommendation-system .

```
2. Run the Docker Container 
```bash

docker run -p 8501:8501 anime-recommendation-system

``` 
This will start the Streamlit application, which can be accessed at `http://localhost:8501`.

### Contact 📫
For any questions, suggestions, or collaboration opportunities, feel free to reach out:

📧Email: [email protected] 

🌐 LinkedIn: [Krishnaveni Ponna](https://www.linkedin.com/in/krishnaveni-ponna-28ab93239)

🐦 Twitter: [@Krishnaveni076](https://x.com/Krishnaveni076)