train.py

import os
import tensorflow as tf
from huggingface_hub import push_to_hub_keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Flatten, Dense


# Environment variable for Hugging Face token
sac = os.getenv('accesstoken')


# Define data paths (modify as needed)
train_data_dir = 'tt'
validation_data_dir = 'valid'
test_data_dir = 'valid'

# Set image dimensions (adjust if necessary)
img_width, img_height = 224, 224  # VGG16 expects these dimensions

# Data augmentation for improved generalization (optional)
train_datagen = ImageDataGenerator(
    rescale=1./255,  # Normalize pixel values
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

validation_datagen = ImageDataGenerator(rescale=1./255)  # Only rescale for validation

# Load training and validation data
train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=32,  # Adjust batch size based on GPU memory
    class_mode='binary'  # Two classes: cat or dog
)

validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=32,
    class_mode='binary'
)

# Load pre-trained VGG16 model (without the top layers)
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))

# Freeze the base model layers (optional - experiment with unfreezing for fine-tuning)
base_model.trainable = False

# Add custom layers for classification on top of the pre-trained model
x = base_model.output
x = Flatten()(x)
predictions = Dense(1, activation='sigmoid')(x)  # Sigmoid for binary classification

# Create the final model
model = tf.keras.Model(inputs=base_model.input, outputs=predictions)

# Compile the model
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# Train the model
history = model.fit(
    train_generator,
    epochs=1,  # Adjust number of epochs based on dataset size and validation performance
    validation_data=validation_generator
)

# Evaluate the model on test data (optional)
test_generator = validation_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=32,
    class_mode='binary'
)

test_loss, test_acc = model.evaluate(test_generator)
print('Test accuracy:', test_acc)

# Save the model for future use (optional)
# Not recommended for Hugging Face Hub upload (use tf.saved_model.save())

# Export the model for Hugging Face Hub using tf.saved_model.save()
# export_dir = 'saved_model'  # Create a directory for the SavedModel
# tf.saved_model.save(model, export_dir,)
# Save model weights to local directory
# model.save_pretrained("kerascatanddog")
# model.savesave_format='tf'
# model.save('cat_dog_classifier2.h5',save_format='h5')
# new_model = tf.keras.models.load_model('cat_dog_classifier2.h5')

# Show the model architecture
model.summary()

# # Upload the model to your Hugging Face space repository
# push_to_hub_keras(
#     model, # model,  # Point to the SavedModel directory
#     repo_id="okeowo1014/kerascatanddog.keras",
#     commit_message="cats and dog image classifier with transfer learning",
#     tags=["image-classifier", "data-augmentation", "class-weights"],
#     include_optimizer=True,
#     token=sac,
# )
# Save the model with a valid extension (recommended: .h5)
model.save('cat_dog_classifierss.keras','',save_format='tf',)
# tf.keras.models.save_model(model, 'cat_dog_classifierss.keras')
# Authenticate with Hugging Face (replace YOUR_TOKEN with your actual token)
# from huggingface_hub import hf_hub_auth
# hf_hub_auth(token=sac)
# Recreate the exact same model purely from the file
new_model = keras.models.load_model('cat_dog_classifierss.keras')
# Push the model to Hugging Face Hub with a descriptive name
model_repo = push_to_hub_keras(new_model, repo_id="okeowo1014/kerascatanddog",token=sac)  # Optional organization

print(f"Model pushed to Hugging Face Hub: {model_repo}")