app.py

from tensorflow import keras
from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np
import gradio as gr
import rasterio
from rasterio.enums import Resampling


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



def greet(name):
    return "Hello " + prediction + "!!"

def predict(image):  

# 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
    
#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 = gr.inputs.Image()
    image = ImageOps.fit(image, size, Resampling.LANCZOS)
    
#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)
    gr.print(prediction)
    return prediction
    
    
iface = gr.Interface(fn=predict, inputs="image", outputs="text")
iface.launch()