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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('eee2.keras')
def image_mod(image):
raw_image = image
temp_image = "temporary_image.jpg"
cv2.imwrite(temp_image, raw_image)
img = cv2.imread("temporary_image.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(yhat)
display = str(display)
#if display == "0":
# message = "Rainy" # Jida,_Zhuhai,_rainy_day.jpg
#if display == "1":
# message = "Sunny" # Morning_fog_-_Flickr_-_tmoravec.jpg
#if display == "2":
# message = "Foggy" # Stuyvesant_Fish_House_25_E78_St_cloudy_jeh.jpg
#if display == "3":
# message = "Cloudy" #Snow_on_Branches,_Beechview,_2020-12-17,_01.jpg
#if display == "4":
# message = "Snowy" # Daedalus_000355_171913_516869_4578_(36155269413).jpg
return display
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","Stuyvesant_Fish_House_25_E78_St_cloudy_jeh.jpg","Morning_fog_-_Flickr_-_tmoravec.jpg","Jida,_Zhuhai,_rainy_day.jpg","Snowy_Nashua.jpg"]).launch() |