modify readme

 # ML base Spacing Correcter
 # ML base Spacing Correcter
-This model is improved version of [TrainKoSpacing](https://github.com/haven-jeon/TrainKoSpacing "TrainKoSpacing"), using FastText instead of Word2Vec
+본 프로젝트는 (주) 미리내에서 진행한 산학 연계 프로젝트로 별도 사내의 버전 관리 서비스를 이용하였음으로, 히스토리 내역 없이 소스 코드 및 데모 코드만 첨부하였음.
+
+## Introduction
+This model is improved version of [TrainKoSpacing](https://github.com/haven-jeon/TrainKoSpacing "TrainKoSpacing"), using FastText instead of Word2Vec.
+
+If you want detail information you can watch our [presentation video](https://drive.google.com/file/d/1f-D3DC8cnrRniLvoAJ4WyreCjoo316Yc/view?usp=sharing "KCC Presentation Video "presentation video")
+
 
 
 ## Performances
 ## Performances
 | Model  | Test Accuracy(%)   | Encoding Time Cost |
 | Model  | Test Accuracy(%)   | Encoding Time Cost |
@@ -12,7 +18,9 @@ This model is improved version of [TrainKoSpacing](https://github.com/haven-jeon
 #### Corpus
 #### Corpus
 
 
 We mainly focus on the National Institute of Korean Language 모두의 말뭉치 corpus and National Information Society Agency AI-Hub data. However, due to the license issue, we are restricted to distribute this dataset. You should be able to get them throw the link below
 We mainly focus on the National Institute of Korean Language 모두의 말뭉치 corpus and National Information Society Agency AI-Hub data. However, due to the license issue, we are restricted to distribute this dataset. You should be able to get them throw the link below
+
 [National Institute of Korean Language 모두의 말뭉치](https://corpus.korean.go.kr/).
 [National Institute of Korean Language 모두의 말뭉치](https://corpus.korean.go.kr/).
+
 [National Information Society Agency AI-Hub](https://aihub.or.kr/aihub-data/natural-language/about "National Information Society Agency AI-Hub")
 [National Information Society Agency AI-Hub](https://aihub.or.kr/aihub-data/natural-language/about "National Information Society Agency AI-Hub")
 
 
 #### Data format
 #### Data format
@@ -34,8 +42,11 @@ Bziped file consisting of one sentence per line.
 ### Word Embedding
 ### Word Embedding
 #### 자모분해
 #### 자모분해
 To get similar shpae of Korean charector, use 자모분해 FastText word embedding.
 To get similar shpae of Korean charector, use 자모분해 FastText word embedding.
+
 ex)
 ex)
+
 자연어처리
 자연어처리
+
 ㅈ ㅏ – ㅇ ㅕ ㄴ ㅇ ㅓ – ㅊ ㅓ – ㄹ ㅣ –
 ㅈ ㅏ – ㅇ ㅕ ㄴ ㅇ ㅓ – ㅊ ㅓ – ㄹ ㅣ –
 
 
 #### 2 stage FastText
 #### 2 stage FastText
@@ -47,6 +58,7 @@ Because middle part of output distribution are evenly distributed.
 ![probability_distribution_of_output_vector](img/probability_distribution_of_output_vector.png)
 ![probability_distribution_of_output_vector](img/probability_distribution_of_output_vector.png)
 
 
 Use log transform and second derivative
 Use log transform and second derivative
+
 result:
 result:
 ![Thresholding_result](img/Thresholding_result.png)
 ![Thresholding_result](img/Thresholding_result.png)
 
 
@@ -110,7 +122,24 @@ Directory guide for embedding model files
 	- **kospacing_wv.np**
 	- **kospacing_wv.np**
 	- **w2idx.dic**
 	- **w2idx.dic**
 
 
-### Reference
+## Demo
+![demo_img](img/demo_screenshot.png)
+You can watch Demo video [here](https://drive.google.com/file/d/1fYKapmplTmVKVxypj0-bB2TFV_2IiBO_/view?usp=sharing "here")
+
+### How to Run Demo
+#### 1. run demo server
+```bash
+cd demo
+python server.py
+```
+#### 2. open demo client page
+open html file on path: demo/front-client/client_demo.html
+
+Input Korean sentence and click submit
+
+
+## Reference
 TrainKoSpacing: https://github.com/haven-jeon/TrainKoSpacing
 TrainKoSpacing: https://github.com/haven-jeon/TrainKoSpacing
+
 딥 러닝을 이용한 자연어 처리 입문: https://wikidocs.net/book/2155
 딥 러닝을 이용한 자연어 처리 입문: https://wikidocs.net/book/2155