?? Get ready for a steamy cam show with the one and only Eatmypie69! ? Watch as she teases you with her mouth-watering, naughty antics and those famous big boobs. tensorflow=tensorflow as tf import numpy as np import matplotlib.pyplot as plt def load_data(file): with open(file, 'r') as f: data = f.read().splitlines() return [row.split(',') for row in data] def preprocess_data(data): processed_data = [] for row in data: # Remove empty entries processed_data.append([x for x in row if x]) return processed_data def extract_features(data): features = [] for row in data: features.append([float(x) for x in row[:-1]]) return features def extract_labels(data): labels = [] for row in data: labels.append(row[-1]) return labels def normalize_features(features): min_values = [min(feature) for feature in zip(*features)] max_values = [max(feature) for feature in zip(*features)] return [[(feature - min_value) / (max_value - min_value) for feature, min_value, max_value in zip(row, min_values, max_values)] for row in features] def split_data(features, labels, test_ratio): test_size = int(len(features) * test_ratio) test_features = features[:test_size] test_labels = labels[:test_size] train_features = features[test_size:] train_labels = labels[test_size:] return train_features, train_labels, test_features, test_labels (train_features, train_labels, test_features, test_labels) = split_data(normalize_features(extract_features(preprocess_data(load_data('eatmypie69_data.csv')))), extract_labels(preprocess_data(load_data('eatmypie69_data.csv'))), 0.2) def build_model(): model = tf.keras.models.Sequential([ tf.keras.layers.Dense(128, activation='relu