himanshugoswamiii/Userfacet-OA

Task of the project

Task : https://github.com/himanshugoswamiii/Userfacet-OA/blob/main/assets/Backend-Develoment-Assessment.pdf

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This project is made using FastAPI Currently this is not using any external database for data storage. Everything is done by using list and dictionaries to replicate the behavior of the database

Configuration of the Project

Go to your project folder:

Creating a virtual environment:

python -m venv venv

this command may differ based on your OS

Starting the Virtual environment:

source venv/bin/activate

Installing dependencies

pip install requirements.txt

Running the project

Go to the root of your project then run this command to open a live server on your localhost : http://127.0.0.1:8000/

uvicorn main:app --reload

Documentation for the project

To see the interactive documentation for your api endpoints you can go to : http://127.0.0.1:8000/docs

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SAMPLE USAGE

• You can create multiple surveys of 20 questions
• You can get the similarity for particular survey

Use interactive documentation

1. Create a survey (We need atleast 1 survey to work with)

Example input :

{
  "name": "survey-1",
  "questions" : [
      "question 1",
      "question 2",
      "question 3",
      "question 4",
      "question 5",
      "question 6",
      "question 7",
      "question 8",
      "question 9",
      "question 10",
      "question 11",
      "question 12",
      "question 13",
      "question 14",
      "question 15",
      "question 16",
      "question 17",
      "question 18",
      "question 10",
      "question 20"
    ]
}

2. Add responses for the available surveys

Here you'll add the response user by user

{
  "candidate_name": "Himanshu",
  "response": [
    0,
    null,
    1,
    2,
    3,
    4,
    1,
    2,
    3,
    4,
    1,
    2,
    3,
    4,
    1,
    2,
    3,
    4,
    5,
    6
  ]
}

null represents no response for that question

Add more response to see the similarity

3. Calculate the similarities

You've 3 methods for calculating similarities

Note: I've not covered everything in this sample usage. Please see the interactive documentation for all the options