https://github.com/adityaexp/comicfinder
🤖 AI-based manhwa recommender using OpenAI embeddings + Streamlit UI.
https://github.com/adityaexp/comicfinder
ai comic-recommender cosine-similarity embeddings manga manhua manhwa nlp openai python recommender-system semantic-search streamlit webtoon
Last synced: 11 months ago
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🤖 AI-based manhwa recommender using OpenAI embeddings + Streamlit UI.
- Host: GitHub
- URL: https://github.com/adityaexp/comicfinder
- Owner: AdityaEXP
- Created: 2025-07-12T05:11:28.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-12T09:45:56.000Z (12 months ago)
- Last Synced: 2025-07-12T10:27:38.614Z (12 months ago)
- Topics: ai, comic-recommender, cosine-similarity, embeddings, manga, manhua, manhwa, nlp, openai, python, recommender-system, semantic-search, streamlit, webtoon
- Language: Python
- Homepage: https://comicfinder.streamlit.app
- Size: 15.8 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🧠 ComicFinder
ComicFinder is an AI-powered content-based recommendation system built using Python and OpenAI Embeddings.
It helps users discover semantically similar manga, manhwa, manhua, and webtoons based on natural language descriptions, genres, or titles — ideal for fans seeking personalized recommendations beyond keyword search.
---
## 💻 Live Demo Of Comic Finder
**https://comicfinder.streamlit.app/**
---
## 🚀 Features Of Comic Finder
- 🔍 Recommends similar manga/manhwa/manhua/webtoon based on descriptions or titles
- 📦 Utilizes precomputed `clean_embeddings.npy` for fast results
- 🧠 Embedding generation using OPENAI embeddings api
- ⚡ Fast cosine similarity search for real-time recommendation
- 🖥️ Clean Streamlit-based frontend
- 📁 Organized data and scripts for easy retraining or extension
---
## 📁 Project Structure
```
comic-recommender/
├── app.py # Main application script
├── data/
│ ├── data.csv # Original manhwa dataset
│ ├── clean_data.csv # Cleaned and preprocessed data
│ └── clean_embeddings.npy # (Ignored from Git, must be downloaded separately)
├── scripts/
│ ├── clean_dataset.py # Data cleaning script
│ ├── generate_embeddings.py # Embedding generation
│ └── recommend.py # Similarity-based recommendations but CLI version
├── .env # Store API keys
├── requirements.txt # Python dependencies
└── README.md # You're here!
```
---
# 🔧 How to Install and Run ComicFinder Locally
```
git clone https://github.com/AdityaEXP/ComicFinder.git
cd ComicFinder
# Optional: Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
streamlit run .\app.py
```
---
## 📌 Example Use Cases
- Find romance manhwa similar to *What's Wrong with Secretary Kim?*
- Get fantasy webtoon recommendations with strong male leads
- Discover hidden manga gems with character development arcs
- Replace genre filters with AI-powered natural language queries
---
# 📥 Download Embedding File
Since clean_embeddings.npy is large, it’s not included in this repo.
[📦 Download clean_embeddings.npy](https://drive.google.com/file/d/1toZRablb8yCVhFrICdU1jQtmYh20mZ9P/view?usp=sharing)
Or you can also generate the clean_embeddings.npy using your own openai api key it will cost around $0.02 per generation
---
# 🔐 Environment Variables
Create a .env file for your OpenAI API Key
```
OPENAIKEY=sk-xxxxxx
```
---
# 📜 License
MIT — free to use, modify, and distribute.
---
# 🤝 Author
Aditya
🛠️ AI + Python + Web3 Enthusiast
---
## 📚 Dataset Source and Preprocessing
This project uses data inspired by or adapted from the following Kaggle dataset:
**📊 [Kaggle - Manhwa and Webtoon Dataset](https://www.kaggle.com/datasets/victorsoeiro/manga-manhwa-and-manhua-dataset/data)**
Credit to **Victor Soeiro** for compiling and sharing this dataset.