app.py

import tensorflow as tf
import gradio as gr

model = tf.keras.models.load_model('50epoch.48-0.06.h5')
labels = ['Diseased', 'Healthy']

def classify_images(inp):
    inp = inp[None, ...]
    inp = tf.keras.applications.resnet.preprocess_input(inp)
    prediction = model.predict(inp).flatten()
    return {labels[i]: float(prediction[i]) for i in range(len(labels))}

image = gr.Image(shape=(224, 224))
label = gr.Label(num_top_classes=3)

examples = [
    ["aug_Durian diseased__0_9801.jpg"],
    ["aug_Durian healthy__0_163.jpg"],
    ["aug_Guava___diseased__0_596.jpg"],
    ["aug_Guava___healthy__0_1324.jpg"],
    ["aug_Mango___diseased__0_980.jpg"],
    ["aug_Mango___healthy__0_3867.jpg"],
    ["aug_Rambutan diseased__0_388.jpg"],
    ["aug_Rambutan healthy__0_3427.jpg"],
]

gr.Interface(fn=classify_images, inputs=image, outputs=label, interpretation="default",examples=examples).launch(share=False)