import tensorflow as tf
from PIL import Image
import tensorflow_hub as hub
from utils import process_image, get_class_names, load_model

import argparse
import numpy as np
import json

def predict(image_path, model, top_k,class_names):
    image = Image.open(image_path)
    image_array = np.asarray(image)
    processed_image = process_image(image_array)
    
    
    prediction = model.predict(np.expand_dims(processed_image, 0))
    
    values, indices= tf.math.top_k(prediction[0].tolist(), k=top_k)
    probability = values.numpy().tolist()
    labels = [class_names[str(i)] for i in indices.numpy().tolist()]
    
    print("Number of Top values to show(K): ",top_k)
    print("Predicted Flower: ",labels[0])
    print("Predicted Probabilities:",probability)
    print("Predicted Classes:",indices.numpy().tolist())
    print('Predicted Labels:',labels)
    
    


if __name__ == "__main__":

    parser = argparse.ArgumentParser(description = "Image Classsifier parser")
    parser.add_argument("image_path",help="Path of the image to be predicted")
    parser.add_argument("saved_model",help="Path of the saved_model")
    parser.add_argument("--top_k", help="Show top k prediction", required = False, default = 5, type = int)
    parser.add_argument("--category_names", help="Json File path for saved labels ", required = False, default = "label_map.json")
    args = parser.parse_args()
    
    model = load_model(args.saved_model)
    class_names = get_class_names(args.category_names)
    predict(args.image_path, model, args.top_k, class_names)

3d4425420b236047386132b7f5299d37b09d3d19
<mask token>

<mask token>


def predict(image_path, model, top_k, class_names):
    image = Image.open(image_path)
    image_array = np.asarray(image)
    processed_image = process_image(image_array)
    prediction = model.predict(np.expand_dims(processed_image, 0))
    values, indices = tf.math.top_k(prediction[0].tolist(), k=top_k)
    probability = values.numpy().tolist()
    labels = [class_names[str(i)] for i in indices.numpy().tolist()]
    print('Number of Top values to show(K): ', top_k)
    print('Predicted Flower: ', labels[0])
    print('Predicted Probabilities:', probability)
    print('Predicted Classes:', indices.numpy().tolist())
    print('Predicted Labels:', labels)


<mask token>

<mask token>


def predict(image_path, model, top_k, class_names):
    image = Image.open(image_path)
    image_array = np.asarray(image)
    processed_image = process_image(image_array)
    prediction = model.predict(np.expand_dims(processed_image, 0))
    values, indices = tf.math.top_k(prediction[0].tolist(), k=top_k)
    probability = values.numpy().tolist()
    labels = [class_names[str(i)] for i in indices.numpy().tolist()]
    print('Number of Top values to show(K): ', top_k)
    print('Predicted Flower: ', labels[0])
    print('Predicted Probabilities:', probability)
    print('Predicted Classes:', indices.numpy().tolist())
    print('Predicted Labels:', labels)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Image Classsifier parser')
    parser.add_argument('image_path', help='Path of the image to be predicted')
    parser.add_argument('saved_model', help='Path of the saved_model')
    parser.add_argument('--top_k', help='Show top k prediction', required=
        False, default=5, type=int)
    parser.add_argument('--category_names', help=
        'Json File path for saved labels ', required=False, default=
        'label_map.json')
    args = parser.parse_args()
    model = load_model(args.saved_model)
    class_names = get_class_names(args.category_names)
    predict(args.image_path, model, args.top_k, class_names)

import tensorflow as tf
from PIL import Image
import tensorflow_hub as hub
from utils import process_image, get_class_names, load_model
import argparse
import numpy as np
import json


def predict(image_path, model, top_k, class_names):
    image = Image.open(image_path)
    image_array = np.asarray(image)
    processed_image = process_image(image_array)
    prediction = model.predict(np.expand_dims(processed_image, 0))
    values, indices = tf.math.top_k(prediction[0].tolist(), k=top_k)
    probability = values.numpy().tolist()
    labels = [class_names[str(i)] for i in indices.numpy().tolist()]
    print('Number of Top values to show(K): ', top_k)
    print('Predicted Flower: ', labels[0])
    print('Predicted Probabilities:', probability)
    print('Predicted Classes:', indices.numpy().tolist())
    print('Predicted Labels:', labels)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Image Classsifier parser')
    parser.add_argument('image_path', help='Path of the image to be predicted')
    parser.add_argument('saved_model', help='Path of the saved_model')
    parser.add_argument('--top_k', help='Show top k prediction', required=
        False, default=5, type=int)
    parser.add_argument('--category_names', help=
        'Json File path for saved labels ', required=False, default=
        'label_map.json')
    args = parser.parse_args()
    model = load_model(args.saved_model)
    class_names = get_class_names(args.category_names)
    predict(args.image_path, model, args.top_k, class_names)

import tensorflow as tf
from PIL import Image
import tensorflow_hub as hub
from utils import process_image, get_class_names, load_model

import argparse
import numpy as np
import json

def predict(image_path, model, top_k,class_names):
    image = Image.open(image_path)
    image_array = np.asarray(image)
    processed_image = process_image(image_array)
    
    
    prediction = model.predict(np.expand_dims(processed_image, 0))
    
    values, indices= tf.math.top_k(prediction[0].tolist(), k=top_k)
    probability = values.numpy().tolist()
    labels = [class_names[str(i)] for i in indices.numpy().tolist()]
    
    print("Number of Top values to show(K): ",top_k)
    print("Predicted Flower: ",labels[0])
    print("Predicted Probabilities:",probability)
    print("Predicted Classes:",indices.numpy().tolist())
    print('Predicted Labels:',labels)
    
    


if __name__ == "__main__":

    parser = argparse.ArgumentParser(description = "Image Classsifier parser")
    parser.add_argument("image_path",help="Path of the image to be predicted")
    parser.add_argument("saved_model",help="Path of the saved_model")
    parser.add_argument("--top_k", help="Show top k prediction", required = False, default = 5, type = int)
    parser.add_argument("--category_names", help="Json File path for saved labels ", required = False, default = "label_map.json")
    args = parser.parse_args()
    
    model = load_model(args.saved_model)
    class_names = get_class_names(args.category_names)
    predict(args.image_path, model, args.top_k, class_names)
