import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np

# Load the trained model
model = tf.keras.models.load_model('cat_dog_classifier_vgg16.h5')

# Define a function to make predictions
def predict_image(img):
    # Preprocess the image
    img = img.resize((224, 224)) 
    img_array = image.img_to_array(img)  
    img_array = np.expand_dims(img_array, axis=0)  
    img_array = img_array / 255.0  

    # Make a prediction
    prediction = model.predict(img_array)
    if prediction[0] < 0.5:
        return "Cat"
    else:
        return "Dog"

# Create the Gradio interface
iface = gr.Interface(
    fn=predict_image, 
    inputs=gr.inputs.Image(type="pil"), 
    outputs="text",
    title="Cat and Dog Classifier",
    description="Upload an image of a cat or a dog and the model will classify it.",
    examples=["cat_example.jpg", "dog_example.jpg"]
)

# Launch the interface
iface.launch()