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import gradio as gr
import requests
import tensorflow as tf
import tensorflow_hub as hub
import cv2
import tf_keras as keras
TF_USE_LEGACY_KERAS=1
path = '20220804-16551659632113-all-images-Adam.h5'
#aks----------------
# Register custom objects
custom_objects = {'KerasLayer': hub.KerasLayer}
# Load the model with custom objects registered
with tf.keras.utils.custom_object_scope(custom_objects):
model = tf.keras.models.load_model(path)
#aks end-----------
# model = tf.keras.models.load_model((path),custom_objects={"KerasLayer":hub.KerasLayer})
labels = ['affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale',
'american_staffordshire_terrier', 'appenzeller',
'australian_terrier', 'basenji', 'basset', 'beagle',
'bedlington_terrier', 'bernese_mountain_dog',
'black-and-tan_coonhound', 'blenheim_spaniel', 'bloodhound',
'bluetick', 'border_collie', 'border_terrier', 'borzoi',
'boston_bull', 'bouvier_des_flandres', 'boxer',
'brabancon_griffon', 'briard', 'brittany_spaniel', 'bull_mastiff',
'cairn', 'cardigan', 'chesapeake_bay_retriever', 'chihuahua',
'chow', 'clumber', 'cocker_spaniel', 'collie',
'curly-coated_retriever', 'dandie_dinmont', 'dhole', 'dingo',
'doberman', 'english_foxhound', 'english_setter',
'english_springer', 'entlebucher', 'eskimo_dog',
'flat-coated_retriever', 'french_bulldog', 'german_shepherd',
'german_short-haired_pointer', 'giant_schnauzer',
'golden_retriever', 'gordon_setter', 'great_dane',
'great_pyrenees', 'greater_swiss_mountain_dog', 'groenendael',
'ibizan_hound', 'irish_setter', 'irish_terrier',
'irish_water_spaniel', 'irish_wolfhound', 'italian_greyhound',
'japanese_spaniel', 'keeshond', 'kelpie', 'kerry_blue_terrier',
'komondor', 'kuvasz', 'labrador_retriever', 'lakeland_terrier',
'leonberg', 'lhasa', 'malamute', 'malinois', 'maltese_dog',
'mexican_hairless', 'miniature_pinscher', 'miniature_poodle',
'miniature_schnauzer', 'newfoundland', 'norfolk_terrier',
'norwegian_elkhound', 'norwich_terrier', 'old_english_sheepdog',
'otterhound', 'papillon', 'pekinese', 'pembroke', 'pomeranian',
'pug', 'redbone', 'rhodesian_ridgeback', 'rottweiler',
'saint_bernard', 'saluki', 'samoyed', 'schipperke',
'scotch_terrier', 'scottish_deerhound', 'sealyham_terrier',
'shetland_sheepdog', 'shih-tzu', 'siberian_husky', 'silky_terrier',
'soft-coated_wheaten_terrier', 'staffordshire_bullterrier',
'standard_poodle', 'standard_schnauzer', 'sussex_spaniel',
'tibetan_mastiff', 'tibetan_terrier', 'toy_poodle', 'toy_terrier',
'vizsla', 'walker_hound', 'weimaraner', 'welsh_springer_spaniel',
'west_highland_white_terrier', 'whippet',
'wire-haired_fox_terrier', 'yorkshire_terrier']
# load the model
def predict_breed(image):
##---aks
image = cv2.resize(image, (224, 224))
##aks end
# reshape the input
image = image.reshape((-1, 224, 224, 3))
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.constant(image)
# prediction = model_1000_images.predict(image).flatten()
prediction = model.predict(image).flatten()
# return prediction labels
return {labels[i]: float(prediction[i]) for i in range(120)}
title = "Dog Vision"
description = "A Dog Breed Classifier trained on the MobileNetV2 Deep Learning Model result."
examples = ['German.jpg']
enable_queue=True
gr.Interface(
fn=predict_breed,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=3),
# inputs=gr.inputs.Image(shape=(224, 224)),
# outputs=gr.outputs.Label(num_top_classes=3),
title=title,
description=description,
examples=examples,
cache_examples=True,
examples_per_page=2).launch(debug=True,enable_queue=enable_queue) |