Training and Deploying Tensorflow Model as a Web App on Azure to predict diamond price

Project Stucture

|   app.py                          # flask app python script  
|   requirements.txt                # requirements for the web  app  
|   training.ipynb                  # Notebook for training and   deploying on Azure  
+---training                        # Training Folder  
|       model.py  
|                              
+---data                            # Data folder  
|       diamonds.csv          
+---images                          # media for README  
|         
+---static                          # CSS and java script styles for the web app  
|             
+---templates                       # template for the web app  
|       index.html  
+---tf_env                          # Environement for deployment  
|         
|   config.json                     # Aure ML workspace config  
|   inf_env.yml                     # Environement for Inference  
|   myenv.yml                       # Conda env for running Notebook  
|   README.md  

Data Information

Data used in the project can be found on kaggle

Attributes

Carat : Carat weight of the Diamond.
Cut : Describe cut quality of the diamond. Quality in increasing order Fair, Good, Very Good, Premium, Ideal .
Color : Color of the Diamond. from J (worst) to D (best)
Clarity : Diamond Clarity refers to the absence of the Inclusions and Blemishes. In order from worst to best :- I1,SI2, SI1, VS2, VS1, VVS2, VVS1, IF
Depth : The Height of a Diamond, measured from the Culet to the table, divided by its average Girdle Diameter.
Table : The Width of the Diamond's Table expressed as a Percentage of its Average Diameter.
X : Length of the Diamond in mm.
Y : Width of the Diamond in mm.
Z : Height of the Diamond in mm.
Price : the Price of the Diamond.
Qualitative Features (Categorical) : Cut, Color, Clarity.
Quantitative Features (Numerical) : Carat, Depth , Table , Price , X , Y, Z.

Requirements

• Install Python (Conda distribution)
• Install VScode
• Create an Azure account

Quickstart

1. Clone the repository
   git clone https://github.com/amine-akrout/diamond-price-prediction
2. Open and Run training.ipynb

first make sure to add your own config file for the Azure ML workspace Then Notebooke will:

• Connect to your working space
• Upload and Register the data
• Training and log a DNN model as an experiment
• Register and deploy the model

Once the model deployed, you will be able to use it as a web service, to do so, run the app.py to test the web app locally on localhost:5000

To deploy the web app on Azure, you need to create an App service and link it to github via a workflow

Finally, you can use the app via the link: https://diamond-price.azurewebsites.net/

Demo