app.py

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


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)
    #turn the image into a numpy array
    
    
    data[0] = np.asarray(image)
    # run the inference
    prediction = model.predict(data)
    gr.outputs.Label = open(labels.txt)
    return prediction
    
    
iface = gr.Interface(fn=predict, inputs=gr.inputs.Image(), outputs="text")
iface.launch()