program.py

# Import necessary libraries
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img,img_to_array
import numpy as np

# Build the CNN model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))

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

# Prepare the image data
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)

train_set = train_datagen.flow_from_directory(
    'dataset/training_set',
    target_size=(64, 64),
    batch_size=32,
    class_mode='binary')

test_set = test_datagen.flow_from_directory(
    'dataset/test_set',
    target_size=(64, 64),
    batch_size=32,
    class_mode='binary')

# Train the model
model.fit(
    train_set,
    steps_per_epoch=100,  # Adjust based on your dataset
    epochs=5,  # Can be changed as needed
    validation_data=test_set,
    validation_steps=2000)  # Adjust based on your dataset

# Save the model
model.save('cat_dog_model.h5')

# Function to make a prediction
def predict_image(image_path):
    test_image = load_img(image_path, target_size=(64, 64))
    test_image = img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis=0)
    result = model.predict(test_image)
    if result[0][0] == 1:
        return 'dog'
    else:
        return 'cat'

# Example of predicting a new image
print(predict_image('input.jpg'))  # Replace with your image path