First Commit

20220804-16551659632113-all-images-Adam.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da173ad036b3c0d5358aa6729626c00435d383c7b9ba02798cc3dd5909fcebaf
+size 23432380

app.py

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+import gradio as gr
+import requests
+import tensorflow as tf
+import tensorflow_hub as hub
+
+path = '20220804-16551659632113-all-images-Adam.h5'
+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):
+
+    
+    # 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.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,
+   enable_queue=enable_queue).launch()