app.py

import gradio as gr
from tensorflow import keras
from skimage.transform import resize

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

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()

resnet50_model = keras.models.load_model('model.h5')
labels = ['Glaucoma_Pos', 'Glaucoma_Neg']

def classify_image(inp):

  inp =resize(inp, (300, 300, 3))
  inp = inp.reshape((-1, 300, 300, 3))
  # inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
  prediction = resnet50_model.predict(inp).flatten()
  confidences = {labels[i]: float(prediction[i]) for i in range(2)}
  return confidences

gr.Interface(fn=classify_image, 
             inputs=gr.Image(shape=(300, 300)),
             outputs=gr.Label(num_top_classes=2),
             ).launch()