Clean up repo, update READEME with environment setup and instructions…

… for downloading data from azure.

README.md

@@ -2,21 +2,25 @@
 
 Code repository for 2024 Data Science for the Common Good project with Doctors Without Borders.
 
-These are just guidelines, not strict rules, so feel free to alter them to meet your needs. Just keep in mind the goal is that others can understand and run your code, even if you aren't around to ask questions to!
+Doctors Without Borders collects data from their clinics and field locations using tally sheets. These tally sheets are standardized forms containing aggregate data, ensuring no individual can be identified and no protected health information (PHI) is included. Currently, this data is manually entered into their health system by over 100 employees, a time-consuming and tedious process. Our OCR pipeline is designed to automate this data entry process, improving efficiency and accuracy.
 
-This template draws a lot of inspiration from [Cookiecutter Data Science](https://drivendata.github.io/cookiecutter-data-science/). Please read their awesome explanations!
 
 # Getting Started
 ## Installing Dependencies and Packages
 Use these steps for setting up a development environment to install and work with code in this template:
 1) Set up a Python 3 virtual environment using [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#) or [Virtualenv](https://virtualenv.pypa.io/en/latest/index.html). Read [Python Virtual Environments: A Primer](https://realpython.com/python-virtual-environments-a-primer/#the-virtualenv-project) for details on how to get started with virtual environments and why you need them. For a _really detailed_ explanation, see [An unbiased evaluation of environment management and packaging tools](https://alpopkes.com/posts/python/packaging_tools/). 
 2) Activate your virtual environment.
-
 3) Install the package.
 	- If you want to just use the scripts and package features, install the project by running `pip install .` from the root directory.
-	- If you will be changing the code and running tests, you can install it by running `pip install -e .[test,dev]`. The `-e/--editable` flag means local changes to the project code will always be available with the package is imported. You wouldn't use this in production, but it's useful for development.
+	- If you will be changing the code and running tests, you can install it by running `pip install -e '.[test,dev]'`. The `-e/--editable` flag means local changes to the project code will always be available with the package is imported. You wouldn't use this in production, but it's useful for development.
 
+For example, if you use the 'venv' module, you would run the following to create an environment named `venv` with Python version 3.10, then activate it and install the package in developer mode.
+Make sure you have Python 3.10 or later installed on your system. You can check your Python version by running `python3 --version`.
+Navigate to your project directory in the terminal and run the following command to create a virtual environment named `venv` - `python3 -m venv venv`.
+To activate the virtual environment, use `venv\Scripts\activate` on Windows or `source venv/bin/activate` on Unix or MacOS.
+To install the package, run `pip install .` for regular use or `pip install -e .[test,dev]` for development. Ps use `pip install -e '.[test,dev]'` for zsh.
 
+
 For example, if you use Conda, you would run the following to create an environment named `template` with python version 3.10, then activate it and install the package in developer mode:
 ```
 $ conda create -n template python=3.10 -y
@@ -49,6 +53,55 @@ Collecting numpy
 ...
 ```
 
+## Instructions for Downloading data from azure
+This part demonstrates how to interact with Azure services to download blobs from Azure Blob Storage.
+
+First, launch Jupyter Notebook:
+```bash
+jupyter notebook
+```
+In the Jupyter Notebook interface, navigate to the folder 'notebooks', open the 'ocr_azure_functions' file. Then you can run the cells.
+Here are the explanations for the various parameters used in the script.
+- **`keyvault_url`**:
+  - **Usage**: Connects to the Key Vault.
+
+- **`secret_name`**:
+  - **Usage**: Retrieves the secret from the Key Vault.
+
+- **`storage_account_name`**:
+  - **Usage**: Constructs the connection string for Blob Storage.
+
+- **`container_name`**:
+  - **Usage**: Specifies which container to list or download blobs from.
+
+- **`storage_account_key = get_keyvault_secret(keyvault_url, secret_name, credential)`**:
+  - **Usage**: Retrieves the storage account key from the Key Vault using the specified URL, secret name, and credential.
+
+### Example Usage
+Please specify where to store downloaded files on your computer using the local_path variable.
+```bash
+keyvault_url = '<keyvault-url>'
+secret_name = '<secret-name>'
+storage_account_name = "<storage-account-name>"
+container_name = "<container-name>"
+
+# Obtain Azure credentials
+credential = get_az_credential()
+
+# Retrieve the storage account key from Azure Key Vault
+storage_account_key = get_keyvault_secret(keyvault_url, secret_name, credential)
+
+# List blobs in the container
+list_blobs_in_container(storage_account_name, storage_account_key, container_name)
+
+# Download blobs in the container
+download_blobs_in_container(storage_account_name, storage_account_key, container_name)
+```
+
+
+
+
+
 ## Specifying Requirements
 In order for users to install your package and all the libraries it depends on by running `pip install`, you need to provide a `pyproject.toml` file. This has two important sections:
 - `project`: List project metadata and version information and all library requirements/dependencies, including for testing or development environments. This is the main file you will work with and add requirements to. Some dependencies 

pyproject.toml

@@ -37,6 +37,7 @@ dependencies = [
     "python-Levenshtein",
     "torchvision",
     "pytest-datadir",
+    'notebook'
 ]
 
 [project.optional-dependencies]