app.py

import tensorflow as tf
import numpy as np
import gradio as gr
import joblib
class_names = joblib.load('class_names')
model = tf.keras.models.load_model("dog_breed_model.h5")
def breedPredict(image):
    img = tf.image.resize(image, [360, 360])
    pred = model.predict(tf.expand_dims(img, axis=0), verbose=0)
    pred = tf.argmax(tf.squeeze(pred))
    class_name = class_names[pred]
    return class_name


    
img=gr.components.Image(label='Upload an Image here')
text=gr.components.Textbox(label='Dog Breed:')
iface = gr.Interface(
        inputs=img,
        outputs=text,
        title='Check any Dog\'s Breed',
        fn=breedPredict
    )

iface.launch()