Create application.py

application.py

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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. Hound 6. Herding 7. Non sporting Group ').launch()