Survey App

About

This application was created to allow businesses to make surveys for customers. It was created with a particular focus on hospitals and clinics, in order to get better patient feedback. It was created as part of Elmhurst College's Master's in Computer Information Technology's capstone project.

Getting setup

Web Server

Installation

Development was written in Python 3.7. So it is encouraged that users who wish to run the web server use Python 3.7. You can download a compatible Python interpreter here.

Downloading the requirements

Once you have Python 3.7 installed, you can download the requirements from the root directory's requirements.txt file by running the following in your terminal:

pip install -r requirements.txt

This will get you all set up with Flask, SQLAlchemy, and everything else.

Running the Flask web server

The Flask server is located under src/python/server. The server makes use of Flask application factories. What this means is that the server instance is created in server/__init__.py with the create_app function. This might sound a little weird, but Flask has it's own special way of handling these factories. In order to run the server, make sure you're in the root directory (survey_app/) and type the following in your terminal:

flask run

However, it you want to avoid possible issues with required libraries for the ODBC database connector, you should run the web server via the instructions in the Docker section below.

Database

Getting started

This project makes use of Flask SQLAlchemy for all interaction with the backend database. The database migrations are located under the /migrations directory. Database tables are located in models.py under src/python/server/. In order to configure the project to use a local database, you need to set the SQLALCHEMY_DATABASE_URL environment variable. The Flask server gets it's configuration from src/python/server/config.py. You'll see that in that file, there's a line which reads:

ODBC_connection_string = os.environ.get('ODBC_CONNECTION_STRING')
params = urllib.parse.quote_plus(ODBC_connection_string)
# and
SQLALCHEMY_DATABASE_URI = "mssql+pyodbc:///?odbc_connect=%s" % params

This is how the database's URL is passed to the web server, and thus what the web server uses to connect to the database. This project is build around using a Microsoft SQL Server DB. This is because my teammates accompanying website is build with .NET, which integrates with Microsoft SQL Server easily. Because of this, you will have to set the ODBC database connection string as an environment variable in the root level .env file. This will look something like:

export ODBC_CONNECTION_STRING="Driver={ODBC Driver 17 for SQL Server};Server=tcp:sql-server-for-capstone.database.windows.net,1433;Database=MastersCapstone;Uid=root-admin;Pwd={your password here};Encrypt=yes;TrustServerCertificate=no;Connection Timeout=30;"

This project uses python-dotenv. So add your database URL to the /.env file and it will be found automatically when Flask runs via the load_dotenv() call in config.py. By default, this project uses Microsoft SQL Server 2008 and the pymssql database connector. You can find more information about SQLAlchemy's database URL here.

Generating your database with flask db

Once you've created your database and set your SQLALCHEMY_DATABASE_URI, you can have flask db create the database tables and relations for you. Simply run the following in your terminal:

flask db upgrade

This command will find the database migrations in the migrations/versions directory and apply them.

Populating your database with dummy data

If your database is empty, and you'd like to add some dummy data, there is the populate_db_dummy_data.py script. Once you've properly set the SQLALCHEMY_DATABASE_URI and run flask db upgrade, you can add dummy data to your app by running:

python populate_db_dummy_data.py

Running the web server with Docker

Installing Docker

If you do not have docker, then follow the instructions for downloading Docker here.

Building and running the Docker container

The Dockerfile is located in the root level directory. From there, run the following to build, tag, and run the web server. Make sure your computer's port 5000 is available.

docker build -t web_server . && docker run -p 5000:5000 web_server 

Using the API

Currently, the API has two different endpoints. These endpoints are responsible for getting questions, and receiving responses.

/api/hospitals/<int:hospital_id>/questions

Use this endpoint to GET questions for hospital with given id hospital_id

method: GET
example url: /api/hospitals/23/questions
example response:
{
  "hospital_name": "Loyola Medical Center",
  "questions": [
    {
      "free_text_field": false,
      "multiple_choice": true,
      "options": [
        {
          "option_id": 25,
          "text": "0-30 minutes"
        },
        {
          "option_id": 26,
          "text": "30-60 minutes"
        },
        {
          "option_id": 27,
          "text": "60+ minutes"
        }
      ],
      "question": "How long did you wait to see a physician?",
      "question_id": 7
    },
    {
      "free_text_field": false,
      "multiple_choice": true,
      "options": [
        {
          "option_id": 32,
          "text": "Yes"
        },
        {
          "option_id": 33,
          "text": "No"
        }
      ],
      "question": "Are you satisfied with your visit today?",
      "question_id": 9
    },
    ...
    ...
    ...
  ],
  "success": true,
  "total_questions": 6
}

/api/responses

Use this endpoint to POST survey responses.

method: POST
sample request:
{
  "hospital_id": 1,
  "responses" : [
    {
      "question_id": 1,
      "option_selected": 2,
      "response_text": null
    },
    {
      "question_id": 2,
      "option_selected": null,
      "response_text": "I really liked my visit with my doctor. He took a lot of time to listen to me."
    }
  ]
}