app.py

from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import gradio as gr

gr.Interface(fn=predict, 
    inputs=gr.inputs.Image(type="pil"
             ),
             outputs=gr.outputs.Label(num_top_classes=2),
def greet(name):
    return "Hello " + name + "!!"

def predict(img): 

# Load the model
    model = load_model('keras_model.h5')

# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1.
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
# Replace this with the path to your image
    image = img
# image = Image.open('<IMAGE_PATH>')
#resize the image to a 224x224 with the same strategy as in TM2:
#resizing the image to be at least 224x224 and then cropping from the center
    size = (224, 224)
    image = ImageOps.fit(image, size, Image.ANTIALIAS)

#turn the image into a numpy array
    image_array = np.asarray(image)
# Normalize the image
    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
# Load the image into the array
    data[0] = normalized_image_array

# run the inference
    prediction = model.predict(data)
    return prediction
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()