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quickdraw.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6cfa034f67b78907cbc9f741996318e91b5198cecb5b855b3ef8d0f447740c6
+size 30093304

requirements.txt

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+gradio
+tensorflow
+pillow

run.py

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+labels = ['aircraft carrier',
+ 'airplane',
+ 'alarm clock',
+ 'ambulance',
+ 'angel',
+ 'animal migration',
+ 'ant',
+ 'anvil',
+ 'apple',
+ 'arm',
+ 'asparagus',
+ 'axe',
+ 'backpack',
+ 'banana',
+ 'bandage',
+ 'barn',
+ 'baseball bat',
+ 'baseball',
+ 'basket',
+ 'basketball',
+ 'bat',
+ 'bathtub',
+ 'beach',
+ 'bear',
+ 'beard',
+ 'bed',
+ 'bee',
+ 'belt',
+ 'bench',
+ 'bicycle',
+ 'binoculars',
+ 'bird',
+ 'birthday cake',
+ 'blackberry',
+ 'blueberry',
+ 'book',
+ 'boomerang',
+ 'bottlecap',
+ 'bowtie',
+ 'bracelet',
+ 'brain',
+ 'bread',
+ 'bridge',
+ 'broccoli',
+ 'broom',
+ 'bucket',
+ 'bulldozer',
+ 'bus',
+ 'bush',
+ 'butterfly',
+ 'cactus',
+ 'cake',
+ 'calculator',
+ 'calendar',
+ 'camel',
+ 'camera',
+ 'camouflage',
+ 'campfire',
+ 'candle',
+ 'cannon',
+ 'canoe',
+ 'car',
+ 'carrot',
+ 'castle',
+ 'cat',
+ 'ceiling fan',
+ 'cell phone',
+ 'cello',
+ 'chair',
+ 'chandelier',
+ 'church',
+ 'circle',
+ 'clarinet',
+ 'clock',
+ 'cloud',
+ 'coffee cup',
+ 'compass',
+ 'computer',
+ 'cookie',
+ 'cooler',
+ 'couch',
+ 'cow',
+ 'crab',
+ 'crayon',
+ 'crocodile',
+ 'crown',
+ 'cruise ship',
+ 'cup',
+ 'diamond',
+ 'dishwasher',
+ 'diving board',
+ 'dog',
+ 'dolphin',
+ 'donut',
+ 'door',
+ 'dragon',
+ 'dresser',
+ 'drill',
+ 'drums',
+ 'duck',
+ 'dumbbell',
+ 'ear',
+ 'elbow',
+ 'elephant',
+ 'envelope',
+ 'eraser',
+ 'eye',
+ 'eyeglasses',
+ 'face',
+ 'fan',
+ 'feather',
+ 'fence',
+ 'finger',
+ 'fire hydrant',
+ 'fireplace',
+ 'firetruck',
+ 'fish',
+ 'flamingo',
+ 'flashlight',
+ 'flip flops',
+ 'floor lamp',
+ 'flower',
+ 'flying saucer',
+ 'foot',
+ 'fork',
+ 'frog',
+ 'frying pan',
+ 'garden hose',
+ 'garden',
+ 'giraffe',
+ 'goatee',
+ 'golf club',
+ 'grapes',
+ 'grass',
+ 'guitar',
+ 'hamburger',
+ 'hammer',
+ 'hand',
+ 'harp',
+ 'hat',
+ 'headphones',
+ 'hedgehog',
+ 'helicopter',
+ 'helmet',
+ 'hexagon',
+ 'hockey puck',
+ 'hockey stick',
+ 'horse',
+ 'hospital',
+ 'hot air balloon',
+ 'hot dog',
+ 'hot tub',
+ 'hourglass',
+ 'house plant',
+ 'house',
+ 'hurricane',
+ 'ice cream',
+ 'jacket',
+ 'jail',
+ 'kangaroo',
+ 'key',
+ 'keyboard',
+ 'knee',
+ 'knife',
+ 'ladder',
+ 'lantern',
+ 'laptop',
+ 'leaf',
+ 'leg',
+ 'light bulb',
+ 'lighter',
+ 'lighthouse',
+ 'lightning',
+ 'line',
+ 'lion',
+ 'lipstick',
+ 'lobster',
+ 'lollipop',
+ 'mailbox',
+ 'map',
+ 'marker',
+ 'matches',
+ 'megaphone',
+ 'mermaid',
+ 'microphone',
+ 'microwave',
+ 'monkey',
+ 'moon',
+ 'mosquito',
+ 'motorbike',
+ 'mountain',
+ 'mouse',
+ 'moustache',
+ 'mouth',
+ 'mug',
+ 'mushroom',
+ 'nail',
+ 'necklace',
+ 'nose',
+ 'ocean',
+ 'octagon',
+ 'octopus',
+ 'onion',
+ 'oven',
+ 'owl',
+ 'paint can',
+ 'paintbrush',
+ 'palm tree',
+ 'panda',
+ 'pants',
+ 'paper clip',
+ 'parachute',
+ 'parrot',
+ 'passport',
+ 'peanut',
+ 'pear',
+ 'peas',
+ 'pencil',
+ 'penguin',
+ 'piano',
+ 'pickup truck',
+ 'picture frame',
+ 'pig',
+ 'pillow',
+ 'pineapple',
+ 'pizza',
+ 'pliers',
+ 'police car',
+ 'pond',
+ 'pool',
+ 'popsicle',
+ 'postcard',
+ 'potato',
+ 'power outlet',
+ 'purse',
+ 'rabbit',
+ 'raccoon',
+ 'radio',
+ 'rain',
+ 'rainbow',
+ 'rake',
+ 'remote control',
+ 'rhinoceros',
+ 'rifle',
+ 'river',
+ 'roller coaster',
+ 'rollerskates',
+ 'sailboat',
+ 'sandwich',
+ 'saw',
+ 'saxophone',
+ 'school bus',
+ 'scissors',
+ 'scorpion',
+ 'screwdriver',
+ 'sea turtle',
+ 'see saw',
+ 'shark',
+ 'sheep',
+ 'shoe',
+ 'shorts',
+ 'shovel',
+ 'sink',
+ 'skateboard',
+ 'skull',
+ 'skyscraper',
+ 'sleeping bag',
+ 'smiley face',
+ 'snail',
+ 'snake',
+ 'snorkel',
+ 'snowflake',
+ 'snowman',
+ 'soccer ball',
+ 'sock',
+ 'speedboat',
+ 'spider',
+ 'spoon',
+ 'spreadsheet',
+ 'square',
+ 'squiggle',
+ 'squirrel',
+ 'stairs',
+ 'star',
+ 'steak',
+ 'stereo',
+ 'stethoscope',
+ 'stitches',
+ 'stop sign',
+ 'stove',
+ 'strawberry',
+ 'streetlight',
+ 'string bean',
+ 'submarine',
+ 'suitcase',
+ 'sun',
+ 'swan',
+ 'sweater',
+ 'swing set',
+ 'sword',
+ 'syringe',
+ 't-shirt',
+ 'table',
+ 'teapot',
+ 'teddy-bear',
+ 'telephone',
+ 'television',
+ 'tennis racquet',
+ 'tent',
+ 'The Eiffel Tower',
+ 'The Great Wall of China',
+ 'The Mona Lisa',
+ 'tiger',
+ 'toaster',
+ 'toe',
+ 'toilet',
+ 'tooth',
+ 'toothbrush',
+ 'toothpaste',
+ 'tornado',
+ 'tractor',
+ 'traffic light',
+ 'train',
+ 'tree',
+ 'triangle',
+ 'trombone',
+ 'truck',
+ 'trumpet',
+ 'umbrella',
+ 'underwear',
+ 'van',
+ 'vase',
+ 'violin',
+ 'washing machine',
+ 'watermelon',
+ 'waterslide',
+ 'whale',
+ 'wheel',
+ 'windmill',
+ 'wine bottle',
+ 'wine glass',
+ 'wristwatch',
+ 'yoga',
+ 'zebra',
+ 'zigzag']
+
+from PIL import Image 
+import numpy as np
+import tensorflow as tf
+
+imgs = []
+def predict(img):
+    img = img['layers'][0][:,:,3]
+    imgs.append(img)
+    image_pil = Image.fromarray(img)
+
+    # Resize the image using PIL
+    new_size = (28, 28)  # Set the new size (e.g., 100x100)
+    resized_image_pil = image_pil.resize(new_size)
+
+    # Convert the resized PIL image back to a NumPy array
+    resized_image_np = np.array(resized_image_pil)
+    # imgs.append(img)
+    img = np.array(resized_image_np).reshape(1, 28, 28,1)
+    out = model.predict(img);
+    top5 = np.argsort(out[0])[::-1][:5]
+    label = labels
+
+    # Create a dictionary with label-probability pairs for the top 5 predictions
+    probabilities = {label[i]: float(out[0][i]) for i in top5}
+
+    return probabilities
+
+model = tf.keras.models.load_model('quickdraw.h5', compile=False)
+
+import gradio as gr
+
+gr.Interface(fn=predict,
+             inputs=gr.Sketchpad(),
+             outputs="label",
+             live=True).launch()