app.py

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

model = tf.keras.models.load_model('ev2m')

classes = np.loadtxt('classes.txt', dtype=str)
classes.sort()
classes = list(classes[:20])
classes.append('other')

def pred(image):
    image = np.array(image) / 255.0
    image = tf.image.resize(image, (480, 480))
    image = tf.keras.preprocessing.image.img_to_array(image)
    pred = model.predict(np.expand_dims(image, axis=0))
    return {classes[i]: float(pred[0][i]) for i in range(len(classes))}

demo = gr.Interface(fn=pred, inputs="image", outputs="label")
demo.launch()