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# DOGS VS CATS DATASET PREDICTION
## LOADING MODULES
# Commented out IPython magic to ensure Python compatibility.
# %%capture
# !pip install tensorflow-addons
# !pip install gradio
import tensorflow_addons as tfa
import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model
#from google_drive_downloader import GoogleDriveDownloader as gdd
# from tensorflow.keras import *
# import tensorflow_datasets as tfds
# import matplotlib.pyplot as plt
# import time
"""##LOADING SAVED MODEL"""
model1='1TNF6uZBvcIfEUwzIR8t4L1kuImxb6PES'
model2='1cK1cIYdczAoEPkiNZUqx2r1UqF2idcay'
model3='1ldVcjryLk-YFfLRyNYdut5WeLLNxJ8ab'
model = model1 #@param ["model1", "model2","model3"] {type:"raw"}
PATH='best_model.h5'
#getData(flid=model,path=PATH)
# For example images
# gdd.download_file_from_google_drive(file_id='1LdB6ZE9vxPi4HNN2emqJSoP0ig9DiG10',
# dest_path='/content/examples.zip',
# unzip=True)
model=load_model(PATH)
# model=load_model("/content/saved_model/content/saved/saved_model")
labels=['Cat','Dog']
NUM_CLASSES=2
IMG_SIZE=224
ex=[['cat2.jpg'],
['dog2.jpeg'],
['cat3.jpg'],
['dog.jpeg']]
"""
## RUNNING WEB UI"""
def classify_image(inp):
inp = inp.reshape((-1, IMG_SIZE, IMG_SIZE, 3))
inp = tf.keras.applications.vgg16.preprocess_input(inp)
prediction = model.predict(inp).flatten()
return {labels[i]: float(prediction[i]) for i in range(NUM_CLASSES)}
image = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))
label = gr.outputs.Label(num_top_classes=2)
gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Cats Vs Dogs',height=600, width=1200,examples=ex,theme='peach').launch(debug=True)
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