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import tensorflow as tf | |
import gradio as gr | |
import cv2 | |
import numpy as np | |
new_model = tf.keras.models.load_model('breedclassification.h5') | |
def predict_classes(link): | |
img = cv2.resize(link,(224,224)) | |
img = img/255 | |
img = img.reshape(-1,224,224,3) | |
pred = np.round(new_model.predict(img)).argmax(axis = 1) | |
dic = {0: 'Herding breed', 1: 'Hound breed', 2: 'Non sporting breed', 3: 'Terrior breed', 4:'working breed', 5: 'sporting breed', 6: 'toy breed'} | |
print(dic.get(int(pred))) | |
a = dic.get(int(pred)) | |
return a | |
label = gr.outputs.Label(num_top_classes=7) | |
gr.Interface(fn=predict_classes, inputs='image', outputs=label,interpretation='default').launch() |