Create predict.py

predict.py

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+import numpy as np
+import tensorflow as tf
+import tensorflow_hub as hub
+from PIL import Image
+
+def pre_process(image: Image.Image):
+    # load the image and convert into
+    # numpy array
+    img = image.resize((180,120))
+    # asarray() class is used to convert
+    # PIL images into NumPy arrays
+    numpydata = np.array(img)
+    image = np.expand_dims(numpydata, axis=0)
+    #image = image//255.0
+    return image
+
+def predict(image: Image.Image):
+    #save_option = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost', )
+    model = tf.keras.models.load_model('leaf_classify.h5',custom_objects={'KerasLayer':hub.KerasLayer})#, options=save_option)
+    pre = model.predict(image,batch_size = None)
+    #result = np.argmax(pre)
+    pred = tf.nn.sigmoid(pre)
+    classes = ['Alstonia Scholaris','Arjun','Bael','Basil','Chinar','Gauva','Jamun','Jatropa','Lemon','Mango','Pomegranate',
+'Pongamia Pinnata']
+    return {classes[i]: float(pred[0][i]) for i in range(len(classes))}