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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', title = 'Breed Classification detection ', description = 'It will classify 7 different species: You can drage the images from google. 1. Terrier 2. Toy 3. Working 4. Sporting 5. Haund 6. Herding 7. Non sporting Group ').launch() |