import tensorflow as tf
from keras.models import load_model
import gradio as gr
from matplotlib import pyplot as plt
import cv2
import numpy as np
model = load_model('eee.keras')

def image_mod(image_mod):
    img = cv2.imread("640px-Snow_cars_2012_G1.jpg")
    resize = tf.image.resize(img, (256, 256))
    plt.imshow(resize.numpy().astype(int))
    yhat = model.predict(np.expand_dims(resize,0))
    display = np.argmax(yhat)
    display = str(display)
    if display == "0":
        message = "Rainy" # Jida,_Zhuhai,_rainy_day.jpg
    if display == "1":
        message = "Foggy"
    if display == "2":
        message = "Cloudy"
    if display == "3":
        message = "Snowy"
    if display == "4":
        message = "Sunny" # Daedalus_000355_171913_516869_4578_(36155269413).jpg
    return message

gr.Interface(fn=image_mod,
             inputs=gr.Image(shape=(256, 256)),
             outputs=gr.Label(num_top_classes=3),
             examples=["Daedalus_000355_171913_516869_4578_(36155269413).jpg","Utah_solar;_a_photovoltaic_power_station_(36293687776).jpg","Foggy_day_of_Riga.jpg","Jida,_Zhuhai,_rainy_day.jpg","640px-Snow_cars_2012_G1.jpg"]).launch()