from app import App
from g2p.plot import plot_fp
model_path = '../Interface/model/model.pth'
device = 'cuda'
train_path = '../Interface/static/Data/data_train_converted.pkl'
tf_path = '../Interface/retrieval/tf_train.npy'
centroid_path = '../Interface/retrieval/centroids_train.npy'
cluster_path = '../Interface/retrieval/clusters_train.npy'
dataset_path = '../Interface/static/Data/data_test_converted.pkl'
app = App(model_path, device, train_path, tf_path, centroid_path, cluster_path)
dataset = pickle.load(open(dataset_path, 'rb'))['data']
# retrieve-> transfer -> predict -> align -> decorate
data_boundary = dataset[0]
data_graph = app.retrieve(data_boundary)[0]
data = app.transfer(data_boundary, data_graph)
data = app.forward(data, network_data=False)
data = app.align(data)
data = app.decorate(data)
# or just:
# data = app.generate(data_boundary)
# visualize and save
ax = plot_fp(data.boundary, data.newBox[data.order], data.rType[data.order],
data.doors, data.windows)
fig = plt.gcf()
fig.canvas.draw()
fig.canvas.print_figure('test_interface_data.png')
