import gradio as gr
import numpy
import matplotlib.pyplot as plt

#!pip install tslearn
from tslearn.clustering import KernelKMeans
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
from tslearn.datasets import UCR_UEA_datasets

numpy.random.seed(42)

X_train, y_train, X_test, y_test = UCR_UEA_datasets().load_dataset("PickupGestureWiimoteZ")

X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train)
sz = X_train.shape[1]

gak_km = KernelKMeans(n_clusters=10,
                      kernel="gak",
                      kernel_params={"sigma": "auto"},
                      n_init=10,
                      verbose=True,
                      random_state=42)
y_pred = gak_km.fit_predict(X_train)

#def process_file(file):
 #plt.figure()
 #for yi in range(10):
 # plt.subplot(10, 1, 1 + yi)
 # for xx in X_train[y_pred == yi]:
 #     plt.plot(xx.ravel(), "k-", alpha=.2)
 # plt.xlim(0, sz)
 # plt.ylim(-4, 4)
 # plt.title("Cluster %d" % (yi + 1))

 #plt.tight_layout()
 #plt.show()
   
    #df = pd.read_csv(file.name, index_col=False)   
    #network = dtwsom.DtwSom(2, 2, type_conn.honeycomb)
    #network.train(df.values.tolist(), 100)
 #return plt

#iface = gr.Interface(fn=sketch_recognition, inputs="sketchpad", outputs="label").launch()

#iface = gr.Interface(
#    process_file,
#    inputs="file",
#    outputs=["plot"],
#    examples=["golf.csv"]
#)

#iface.launch(debug=False)
def sentence_builder(quantity, animal, place, activity_list, morning):
    return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


iface = gr.Interface(
    sentence_builder,
    [
        gr.inputs.Slider(2, 20),
        gr.inputs.Dropdown(["cat", "dog", "bird"]),
        gr.inputs.Radio(["park", "zoo", "road"]),
        gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
        gr.inputs.Checkbox(label="Is it the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", "park", ["ran", "swam"], True],
        [4, "dog", "zoo", ["ate", "swam"], False],
        [10, "bird", "road", ["ran"], False],
        [8, "cat", "zoo", ["ate"], True],
    ],
)

iface.launch()