app.py

import numpy as np
import gradio as gr
import tensorflow as tf

models = [    {"name": "my_model_2.h5", "size": 512},    {"name": "my_model.h5", "size": 224},]

def classify_image(image, model_name):
    model_config = next(m for m in models if m["name"] == model_name)
    model = tf.keras.models.load_model(model_name)
    input_image = np.expand_dims(image, axis=0)
    prediction = model.predict(input_image).flatten()
    if len(prediction) > 1:
        probability = 100 * np.exp(prediction[0]) / (np.exp(prediction[0]) + np.exp(prediction[1]))
    else:
        probability = round(100. / (1 + np.exp(-prediction[0])), 2)
    if probability > 45:
        label = "Glaucoma"
    elif probability > 25:
        label = "Unclear"
    else:
        label = "Not glaucoma"
    return label, probability

inputs = [
    gr.inputs.Image(shape=(224, 224), label="Eye image"),
    gr.inputs.Dropdown(choices=[m["name"] for m in models], label="Model"),
]
outputs = [
    gr.outputs.Textbox(label="Predicted label"),
    gr.outputs.Textbox(label="Probability of glaucoma (0-100)"),
]

gr.Interface(classify_image, inputs, outputs, examples=["001.jpg", "002.jpg", "225.jpg"]).launch()