diff --git a/website/test.py b/website/test.py
deleted file mode 100644
index bd0d73df..00000000
--- a/website/test.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import pandas as pd
-import tensorflow as tf
-import numpy as np
-
-PARAMS = ['ax', 'ay', 'az', 'gx', 'gy', 'gz', 'class']
-# data = request.POST.get('data')
-# parsed_csv = list(csv.reader(data.split(';')))
-# df = pd.DataFrame(parsed_csv, columns=PARAMS)
-
-df = pd.read_csv('data.csv')
-off_target = pd.DataFrame(df[df['class']==0].values.tolist(), columns=PARAMS)
-on_target = pd.DataFrame(df[df['class']==1].values.tolist(), columns=PARAMS)
-
-
-HOTSPOT = [off_target, on_target] # known location / class
-NUM_HOTSPOT = len(HOTSPOT)
-SAMPLES_PER_HOTSPOT = 1
-ONE_HOT_ENCODED_HOTSPOT = np.eye(NUM_HOTSPOT)
-
-inputs = []
-outputs = []
-
-for hotspot_index in range(NUM_HOTSPOT):
-
-    target = HOTSPOT[hotspot_index]
-
-    num_recordings = int(target.shape[0] / SAMPLES_PER_HOTSPOT)
-
-    output = ONE_HOT_ENCODED_HOTSPOT[hotspot_index]
-
-    print(f"\tThere are {num_recordings} recordings.")
-
-    for i in range(num_recordings):
-        tensor = []
-        for j in range(SAMPLES_PER_HOTSPOT):
-            index = i * SAMPLES_PER_HOTSPOT + j
-            # normalize the input data, between 0 to 1:
-            # - acceleration is between: -4 to +4
-            # - gyroscope is between: -2000 to +2000
-            tensor += [
-                (target['ax'][index] + 4) / 8,
-                (target['ay'][index] + 4) / 8,
-                (target['az'][index] + 4) / 8,
-                (target['gx'][index] + 2000) / 4000,
-                (target['gy'][index] + 2000) / 4000,
-                (target['gz'][index] + 2000) / 4000
-            ]
-        inputs.append(tensor)
-        outputs.append(output)
-
-# convert the list to numpy array
-inputs = np.array(inputs)
-outputs = np.array(outputs)
-
-        
-# Randomize the order of the inputs, so they can be evenly distributed for training, testing, and validation
-# https://stackoverflow.com/a/37710486/2020087
-num_inputs = len(inputs)
-randomize = np.arange(num_inputs)
-np.random.shuffle(randomize)
-
-# Swap the consecutive indexes (0, 1, 2, etc) with the randomized indexes
-inputs = inputs[randomize]
-outputs = outputs[randomize]
-
-# Split the recordings (group of samples) into three sets: training, testing and validation
-TRAIN_SPLIT = int(0.6 * num_inputs)
-TEST_SPLIT = int(0.2 * num_inputs + TRAIN_SPLIT)
-
-inputs_train, inputs_test, inputs_validate = np.split(inputs, [TRAIN_SPLIT, TEST_SPLIT])
-outputs_train, outputs_test, outputs_validate = np.split(outputs, [TRAIN_SPLIT, TEST_SPLIT])
-
-# print(inputs_train, outputs_train)
-
-
-# build the model and train it
-model = tf.keras.Sequential()
-model.add(tf.keras.layers.Dense(50, activation='relu')) # relu is used for performance
-model.add(tf.keras.layers.Dense(15, activation='relu'))
-model.add(tf.keras.layers.Dense(NUM_HOTSPOT, activation='softmax')) # softmax is used, because we only expect one hotspot to occur per input
-model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
-history = model.fit(inputs_train, outputs_train, epochs=600, batch_size=1, 
-                    validation_data=(inputs_validate, outputs_validate))
-
-
-# print("Evaluate on test data")
-# results = model.evaluate(inputs_test, outputs_test)
-# print("test loss, test acc:", results)
-
-
-# predictions = model.predict(inputs_test)
-# # print the predictions and the expected ouputs
-# print("predictions =\n", np.round(predictions, decimals=3))
-# print("actual =\n", outputs_test)
-
-# # use the model to predict the test inputs
-# feed = [[1,1,1,1,1,1]]
-# predictions = model.predict(feed)
-
-# # print the predictions and the expected ouputs
-# print("predictions =\n", np.round(predictions, decimals=3))
-
-
-# Convert the model to the TensorFlow Lite format without quantization
-converter = tf.lite.TFLiteConverter.from_keras_model(model)
-converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
-tflite_model = converter.convert()
-
-def hex_to_c_array(hex_data, var_name):
-    c_str = ''
-
-    # Create header guard
-    c_str += '#ifdef ' + var_name.upper() + '_H\n'
-    c_str += '#define ' + var_name.upper() + '_H\n\n'
-
-    # Add array length at top of file
-    c_str += '\nunsigned int ' + var_name + '_len = ' + str(len(hex_data)) + ';\n'
-
-    # Declare C variable
-    c_str += 'unsigned char ' + var_name + '[] = {'
-    hex_array = []
-
-    for i, val in enumerate(hex_data):
-        # Construct string from hex
-        hex_str = format(val, '#04x')
-
-        # Add formatting so each line stays within 80 characters
-        if (i + 1) < len(hex_data):
-            hex_str += ','
-        if (i + 1) % 12 == 0:
-            hex_str += '\n'
-        hex_array.append(hex_str)
-
-    # Add closing brace
-    c_str += '\n ' + format(' '.join(hex_array)) + '\n};\n\n'
-
-    # Close header guard
-    c_str += '#endif // ' + var_name.upper() + '_H'
-
-    return c_str
-
-
-with open('hotspot_model.h', 'w') as file:
-    file.write(hex_to_c_array(tflite_model, 'hotspot_model'))
\ No newline at end of file