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Update app.py
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app.py
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@@ -1,6 +1,6 @@
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import os
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import numpy as np
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import gradio as gr
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import matplotlib.pyplot as plt
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import tensorflow as tf
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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@@ -9,10 +9,15 @@ from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, Batc
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from tensorflow.keras.models import Model
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from tensorflow.keras.optimizers import Adam
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#
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# Function to find the greatest batch size that fully divides the total samples
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def find_batch_size(total_samples, initial_batch_size):
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@@ -21,10 +26,14 @@ def find_batch_size(total_samples, initial_batch_size):
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return bs
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return 1 # Fallback to 1 if no perfect divisor is found
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#
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# Define the Pokémon classes to be classified
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classes = ['Doduo', 'Geodude', 'Zubat']
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import os
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import zipfile
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import numpy as np
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import matplotlib.pyplot as plt
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import tensorflow as tf
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.keras.models import Model
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from tensorflow.keras.optimizers import Adam
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# Function to download and extract the dataset
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def download_and_extract_dataset(dataset_name):
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# Make sure the kaggle.json file is in the proper location
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os.environ['KAGGLE_CONFIG_DIR'] = os.path.expanduser("~/.kaggle/")
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# Download the dataset
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os.system(f'kaggle datasets download -d mikoajkolman/pokemon-images-first-generation17000-files')
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# Extract the dataset
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with zipfile.ZipFile(f'{dataset_name}.zip', 'r') as zip_ref:
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zip_ref.extractall('.')
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# Function to find the greatest batch size that fully divides the total samples
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def find_batch_size(total_samples, initial_batch_size):
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return bs
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return 1 # Fallback to 1 if no perfect divisor is found
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# Suppress TensorFlow logging and warnings
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1 = INFO, 2 = WARNING)
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tf.get_logger().setLevel('ERROR')
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tf.autograph.set_verbosity(2)
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# Download and extract the dataset
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dataset_name = 'mikoajkolman/pokemon-images-first
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# Define the Pokémon classes to be classified
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classes = ['Doduo', 'Geodude', 'Zubat']
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