This is a demo project to elaborate how Deep Learning Models are deployed on production using Flask API

You must have keras, tensorflow, sklearn, seaborn, matplotlib, numpy and Flask (for API) installed.

This project has three major parts :

1. flowers_image_classification.ipynb - This contains the code implemented to the train the deep learning model from scratch on the categories of flowers considered. This file also contains the model_plot, confusion matrix plots for in-depth analysis.
2. app.py - This contains Flask APIs that receives images through GUI or API calls, computes the labels based on our model and returns it.
3. images - these are the images that were used as input to train the classifier.

1. Ensure that you are in the project home directory. Open the deep learning model notebook 'flowers_image_classification.ipynb' for more information

2. Run app.py using below command to start Flask API

python app.py

By default, flask will run on port 5000.

3. Navigate to URL http://127.0.0.1:5000/

Upload the image and check for the output value returned by the model.