import cv2
import numpy as np
import urllib.request
import matplotlib.pyplot as plt
from tensorflow.keras.optimizers import Adam

from huggingface_hub import from_pretrained_keras

reloaded_model = from_pretrained_keras('ShaharAdar/best-model-try')

reloaded_model.compile(optimizer=Adam(0.00001),
                       loss='categorical_crossentropy',
                       metrics=['accuracy']
)

class_names = ['Clams', 'Corals', 'Crabs', 'Dolphin', 'Eel', 'Fish', 
              'Jelly Fish', 'Lobster', 'Nudibranchs', 'Octopus', 'Otter',
              'Penguin', 'Puffers', 'Sea Rays', 'Sea Urchins', 'Seahorse',
              'Seal', 'Sharks', 'Shrimp', 'Squid', 'Starfish',
              'Turtle_Tortoise', 'Whale']

def fetch_image(filepath):
  try:
      # Directly read the image from the provided file path
      image = cv2.imread(filepath, cv2.IMREAD_UNCHANGED)

      # Convert the image from BGR to RGB (if necessary)
      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

      return image

  except Exception as e:
      print("Error reading image:", e)
      return None  # Return None to indicate an error

def fetch_image_2(filepath):
    resp = urllib.request.urlopen(url)
    image = np.asarray(bytearray(resp.read()), dtype="uint8")
    image = cv2.imdecode(image, cv2.IMREAD_UNCHANGED)

    # Convert the image from BGR to RGB
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    return image

def disp_img(image):
    # Display the image
    plt.imshow(image)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.show()

def resize_2(img):
    img = cv2.resize(img,(224,224))     # resize image to match model's expected sizing
    img = img.reshape(1,224,224,3) # return the image with shaping that TF wants.
    return img

def make_prediction(image):
    prediction = reloaded_model.predict(image)
    predicted_class = prediction.argmax()

    print('Predicted class: ', class_names[predicted_class])

def predict_class(url):
    image = fetch_image_2(url)
    disp_img(image)
    image = resize_2(image)
    make_prediction(image)
    print("\n")

img = input("Enter url of images you want to predict it's class:")
print(predict_class(img))