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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) | |