catvsdog.py

import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential, load_model
from keras.layers import Conv2D, Flatten, Dense, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
from tensorflow.keras import optimizers
import os

train_dir = 'C:/Users/Lenovo/OneDrive/Desktop/model/dogvscat/train'
test_dir = 'C:/Users/Lenovo/OneDrive/Desktop/model/dogvscat/test'
model_path = 'model/catvsdog.h5'

if os.path.exists(model_path):
    model = load_model(model_path)
    print("Model loaded successfully.")
else:
    train_datagen = ImageDataGenerator(
        rescale=1./255,
        rotation_range=20,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True
    )
    test_datagen = ImageDataGenerator(rescale=1./255)
    train_generator = train_datagen.flow_from_directory(train_dir, target_size=(224, 224), batch_size=20, class_mode='binary')
    test_generator = test_datagen.flow_from_directory(test_dir, target_size=(224, 224), batch_size=20, class_mode='binary')

    model = Sequential([
        Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
        MaxPooling2D(2, 2),
        Conv2D(64, (3, 3), activation='relu'), MaxPooling2D(2, 2),
        Conv2D(128, (3, 3), activation='relu'), MaxPooling2D(2, 2),
        Conv2D(128, (3, 3), activation='relu'), MaxPooling2D(2, 2),
        Flatten(), Dense(512, activation='relu'), Dense(1, activation='sigmoid')   
    ])
    model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(learning_rate=1e-3), metrics=['accuracy'])
    model.fit(train_generator, steps_per_epoch=train_generator.samples // 20, epochs=10,
              validation_data=test_generator, validation_steps=test_generator.samples // 20)
    model.save(model_path)
   
def predict_image(img_path):
    img = load_img(img_path, target_size=(224, 224))
    img_array = img_to_array(img).reshape(1, 224, 224, 3) / 255.0
    prediction = model.predict(img_array)
    plt.imshow(img)
    plt.axis('off')
    plt.show()
    print("Prediction:", "cat" if prediction < 0.5 else "dog")

while True:
    img_path = input("Enter the path to an image (or type 'exit' to quit): ")
    if img_path.lower() == 'exit':
        print("Exiting the program.")
        break
    elif os.path.exists(img_path):
        predict_image(img_path)
    else:
        print("Invalid path. Please try again.")