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Flask deployment of deep learning model performing segmentation task on aerial imagery building footprints

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FastMap.ai

Flask deployment of Image segmentation deep learning model

Directories and files to be aware of:

Flask-env2 conda environment

This project relies on you using the environment.yml file to recreate the Flask-env2 conda environment. To do so, please run the following commands:

# create the zipcode conda environment
conda env create -f environment.yml

# activate the zipcode conda environment
conda activate Flask-env2

.src source code:

This project contains several .py modules in the src/ directory. Please use the following bash command to install the .src module:

#install the .src modules
pip install -e .

• A static/' directory that contains the static files for the web deployment

• 'app.py'

The flask app script that utilizes the 'src/' modules to process images and make inferences

• templates directory

Contains the results.html page displaying model outputs

Running the Flask Application

To run in a development environment (on your local computer)

export FLASK_ENV=development
env FLASK_APP=app.py flask run

To run in a production environment (used for deployment, but test it out locally first):

export FLASK_ENV=production
python app.py

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Flask deployment of deep learning model performing segmentation task on aerial imagery building footprints

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