app.py

import gradio as gr
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
from PIL import Image
import numpy as np

# Load your model
model = load_model('cats_and_dogs_classifier.h5')

# Define prediction function
def predict(image):
    # Ensure the image is a PIL Image object
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    
    # Resize image to match model's expected sizing (150x150 pixels)
    img = image.resize((150, 150))
    # Convert image to array and expand dimensions to fit model input requirements
    img_array = img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    # Predict using loaded model
    prediction = model.predict(img_array)
    # Convert prediction to label
    class_label = 'Cat' if prediction[0][0] < 0.5 else 'Dog'
    return class_label

# Create a Gradio interface
interface = gr.Interface(fn=predict, inputs="image", outputs="text")
interface.launch()