.
├── API                                 # Contains code for the API described in the architecture
│   ├── server.py                       # The python flask API server
│   ├── requestprocessor.py             # Contains implementations to process each of the requests received by the server
│   ├── configurations.py               # Contains the configurations (or the values that will change based on the environment)
│   |── constants.py                    # The constants used in the API
│   |── mongodbprovider.py              # Contains methods to interact with Mongo DB
│   |── redisprovider.py                # Contains methods to interact with Redis
│   |── chess_board_segmentation.py     # Contains methods to segment the chess board image into 64 sub images
│   |── chess_piece_recognition.py      # Contains methods to infer the chess piece based on the trained deep learning model
│   |── mock_server.py                  # Implement a mock server to help initial integration with client app
│   |── utils.py                        # Contains utility methods used across files
│   |── tests.py                        # Contains tests to check different scenarios
├── chess_board_segmentation            # Contains notebooks to evaluate different algorithms to segment chess board
|── chess_piece_detection               # Contains code for different deep learning models trained to detect chess pieces
│   ├── app
|       ├── appconfigs.py               # Different configurations that are required for training the models (like location of training  images etc.)
│       ├── modelconfigs.py             # Contains the configurations for the different models used during training the model
│       |── constants.py                # The constants used across the training app
│       ├── models.py                   # Contains the different models that are trained
│       ├── data_generator.py           # Contains the data generator for generating the training data in batches. [Refer]                 (https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly)      
│       ├── utils.py                    # Contains the utility methods for obtaining the data, printing confusion matrix etc.
│       └── app.py                      # Tie all codes together and train or evaluate the required model based on the arguments passed
|── training_data_analysis              # Contains code to analyze the training data
└── training_data_generation            # Contains code to generate and crawl for training chess piece images