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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('eee4.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_old = np.argmax(yhat) | |
| display = str(display_old) | |
| #display = str(yhat) | |
| #display = str(display) | |
| if display == "0": | |
| message = "Cloudy" # | |
| if display == "1": | |
| message = "Snowy" # Morning_fog_-_Flickr_-_tmoravec.jpg | |
| if display == "2": | |
| message = "Foggy" # Stuyvesant_Fish_House_25_E78_St_cloudy_jeh.jpg | |
| if display == "3": | |
| message = "Rainy" #Snow_on_Branches,_Beechview,_2020-12-17,_01.jpg | |
| 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=["640px-Hopetoun_house_sunny_day.jpg","Panoramic_of_water_reflection_of_the_mountains_of_Vang_Vieng_with_cloudy_sky_in_paddy_fields.jpg","Snow_Scene_at_Shipka_Pass_1.JPG","Jida,_Zhuhai,_rainy_day.jpg","The_lift_bridge_on_Cherry_Street_over_the_ship_channel_to_the_Turning_Basin_on_a_foggy_day,_2012-03-17_-a.jpg"]).launch() |