xcurvnubaim
commited on
Commit
·
feeed5b
1
Parent(s):
772a5bb
feat: gradio
Browse files- app.py +33 -0
- labels.txt +90 -0
app.py
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from tensorflow.keras.models import load_model
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import numpy as np
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import gradio as gr
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import tensorflow as tf
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from io import StringIO
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from PIL import Image
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import requests
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url = "https://drive.usercontent.google.com/download?id=1T5HGnk9Mxlb5G6FTxp26BWSrTpzbjtP2&export=download&authuser=0"
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open("models.h5", "wb").write(requests.get(url).content)
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labels = []
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model = load_model('/content/models.h5')
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with open("/content/name of the animals.txt") as f:
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for line in f:
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labels.append(line.replace('\n', ''))
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def classify_image(inp):
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# Create a copy of the input array to avoid reference issues
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inp_copy = np.copy(inp)
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# Resize the input image to the expected shape (224, 224)
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inp_copy = Image.fromarray(inp_copy)
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inp_copy = inp_copy.resize((224, 224))
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inp_copy = np.array(inp_copy)
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inp_copy = inp_copy.reshape((-1, 224, 224, 3))
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inp_copy = tf.keras.applications.efficientnet.preprocess_input(inp_copy)
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prediction = model.predict(inp_copy).flatten()
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confidences = {labels[i]: float(prediction[i]) for i in range(90)}
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return confidences
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demo = gr.Interface(classify_image, gr.Image(), gr.Label(num_top_classes=3))
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if __name__ == "__main__":
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demo.launch()
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labels.txt
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antelope
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badger
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bat
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bear
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bee
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beetle
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bison
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boar
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butterfly
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cat
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caterpillar
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chimpanzee
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cockroach
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cow
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coyote
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crab
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crow
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deer
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dog
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dolphin
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donkey
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dragonfly
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duck
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eagle
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elephant
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flamingo
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fly
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fox
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goat
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goldfish
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goose
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gorilla
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grasshopper
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hamster
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hare
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hedgehog
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hippopotamus
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hornbill
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horse
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hummingbird
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hyena
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jellyfish
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kangaroo
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koala
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ladybugs
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leopard
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lion
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lizard
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lobster
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mosquito
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moth
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mouse
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octopus
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okapi
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orangutan
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otter
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owl
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ox
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oyster
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panda
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parrot
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pelecaniformes
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penguin
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pig
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pigeon
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porcupine
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possum
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raccoon
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rat
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reindeer
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rhinoceros
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sandpiper
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seahorse
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seal
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shark
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sheep
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snake
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sparrow
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squid
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squirrel
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starfish
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swan
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tiger
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turkey
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turtle
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whale
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wolf
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wombat
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woodpecker
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zebra
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