import tensorflow as tf
import numpy as np
import os,glob,cv2
import sys,argparse


def predict(imgPath):
    # First, pass the path of the image
    dir_path = os.path.dirname(os.path.realpath(__file__))
    image_path=  imgPath 
    filename = dir_path +'/' +image_path
    image_size=32
    num_channels=3
    images = []
    # Reading the image using OpenCV
    image = cv2.imread(filename)
    # Resizing the image to our desired size and preprocessing will be done exactly as done during training
    image = cv2.resize(image, (image_size, image_size),0,0, cv2.INTER_LINEAR)
    images.append(image)
    images = np.array(images, dtype=np.uint8)
    images = images.astype('float32')
    images = np.multiply(images, 1.0/255.0) 
    #The input to the network is of shape [None image_size image_size num_channels]. Hence we reshape.
    x_batch = images.reshape(1, image_size,image_size,num_channels)
    
    ## Let us restore the saved model 
    sess = tf.Session()
    # Step-1: Recreate the network graph. At this step only graph is created.
    saver = tf.train.import_meta_graph('cifar-100-model.meta')
    # Step-2: Now let's load the weights saved using the restore method.
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    
    # Accessing the default graph which we have restored
    graph = tf.get_default_graph()
    
    # Now, let's get hold of the op that we can be processed to get the output.
    # In the original network y_pred is the tensor that is the prediction of the network
    y_pred = graph.get_tensor_by_name("y_pred:0")
    
    ## Let's feed the images to the input placeholders
    x= graph.get_tensor_by_name("x:0") 
    y_true = graph.get_tensor_by_name("y_true:0") 
    y_test_images = np.zeros((1, 10)) 
    
    classes = ["frog", "truck", "deer", "automobile", "bird", "horse", "ship", "cat", "dog", "airplane"]
    
    ### Creating the feed_dict that is required to be fed to calculate y_pred 
    feed_dict_testing = {x: x_batch, y_true: y_test_images}
    result=sess.run(y_pred, feed_dict=feed_dict_testing)
    # result is of this format [probabiliy_of_rose probability_of_sunflower]
    soln = {}
    i = 0;
    for res in result[0]:
        soln[classes[i]] = res
        i +=1
    for s in soln:
        print(s, soln[s])
        
    maximum = max(soln, key=soln.get) 
    print(maximum)
    

predict("images/areo1.jfif")
