text-similarity-scorer

This repo contains files to code that deploys an API endpoint for a user to detect text similarity between 2 texts without the use of any libraries.

Directory Structure

The directory structure is provided below.

.
├── Dockerfile
├── DocumentSimilarity.py
├── LICENSE
├── README.md
├── constants.py
├── requirements.txt
├── server.py
├── similarity_scorer.py
├── templates
    ├── base.html
    ├── index.html
    └── output.html

Build steps:

Listed below are different ways to build and run the system. There are a total of 3 ways to build and run the system. They are:

1. Build from the git repo.
2. Build using docker image.

---

1. Build from the git repo.

• Clone the project Git repo.

  foo@bar:~$ git clone https://github.com/likith11/text-similarity-scorer.git

• Move into the project directory.

  foo@bar:~$ cd text-similarity-scorer

• Create a virtual environment and activate it.

  foo@bar:~$ python3 -m venv venv 
  foo@bar:~$ source venv/bin/activate

• Install requirements from requirements.txt

  (venv) foo@bar:~$ pip install -r requirements.txt

   

---

(1.1) - To RUN the command line text similarity scorer. Run the following command:

(venv) foo@bar:~$ python similarity_scorer.py "Text 1 goes here" "Text 2 goes here"

NOTE: Here "Text 1 goes here" and "Text 2 goes here" are 2 strings that are passed as the arguments. Replace text within double quotes to pass custom input. For more help pass -h after the executable file. A similarity score is displayed as output.  

(1.2) - To RUN the web server:

(venv) foo@bar:~$ python server.py

GUI Steps:

• Navigate to http://0.0.0.0:5000/ or http://localhost:5000/ (Replace port number if its a different port on your machine. The default port above is 5000).
• Two text areas with label Text Area 1 and Text Area 2 along with a blue Process button is displayed.
• Input text to be compared to the text boxes.
• Click on Process_
• A new screen with Translucent alert box is shown with the corresponding Similarity Score calculated
• Click on Retry? button to go back to the main screen and repeat the process.

API Call:

• API end point is deployed at http://0.0.0.0:5000/api/similarity where POST request is accepted.
• The API accepts text inputs in the JSON format of

  {
       "text_one" : "Text 1 goes here",
       "text_two" : "Text 2 goes here"

   }

• An example curl command is

curl -d '{"text_one" : "Text 1 goes here", "text_two" : "Text 2 goes here"}' -H 'Content-Type: application/json' 
http://0.0.0.0:5000/api/similarity 

• An API response json is returned with the similarity score.

 

---

 

2. Pull from Docker Hub / Build Docker Image Locally.

• Pull image from docker hub.

  foo@bar:~$ docker pull likithponnanna/python-similarity:submission

• Run the image that was pulled. Below port 5000 is mapped between the external and docker container.

  foo@bar:~$ docker run -p 5000:5000 python-similarity 

• NOTE : To run the server follow the steps mentioned in section 1.2 API Call.

   

• [OPTIONAL] To build the docker image using the dockerfile locally run the following command before initiating docker run.

  foo@bar:~$ git clone https://github.com/likith11/text-similarity-scorer.git
  foo@bar:~$ cd text-similarity-scorer
  foo@bar:~$ docker build -t python-similarity .