app.py

import numpy as np
import tensorflow as tf
from tensorflow import keras
from PIL import Image
import numpy as np
from skimage import transform
import numpy as np
import tensorflow as tf
from tensorflow import keras
import gradio as gr

savedModel = keras.models.load_model("my_h5_model.h5")
def predict(image):
    classes=['fresh apple','fresh banana','fresh orange','rotten apple','rotten banana','rotten orange']
    np_image = np.array(image).astype('float32')/255
    np_image = transform.resize(np_image, (300, 300, 3))
    np_image = np.expand_dims(np_image, axis=0)
    softmax=savedModel.predict(np_image).flatten().tolist()
    confidence = dict(zip(classes, softmax))

    return confidence
print("this model is trained on 3 fruits only.Apples, Oranges, and Bananas")
gr.Interface(fn=predict, 
             inputs="image",
             outputs=gr.Label(num_top_classes=3),
             examples=['apple.jpg','orange.jpg','banana.jpg']).launch()