Upload app.py

app.py

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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 cv2
+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
+
+
+def getData(flid,path,unzp=False):
+  return gdd.download_file_from_google_drive(file_id=flid,
+                                    dest_path=path,
+                                    unzip=unzp)
+
+
+"""##LOADING SAVED MODEL"""
+
+model1='1TNF6uZBvcIfEUwzIR8t4L1kuImxb6PES'
+model2='1cK1cIYdczAoEPkiNZUqx2r1UqF2idcay'
+model3='1ldVcjryLk-YFfLRyNYdut5WeLLNxJ8ab'
+model = model1 #@param ["model1", "model2","model3"] {type:"raw"}
+PATH='/saved_model/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('./saved_model/best_model.h5')
+# model=load_model("/content/saved_model/content/saved/saved_model")
+
+labels=['Cat','Dog']
+NUM_CLASSES=2
+IMG_SIZE=224
+# ex=[['/content/dogs-cat-examples/cat2.jpg'],  
+#     ['/content/dogs-cat-examples/cat3.jpg'],
+#     ['/content/dogs-cat-examples/dog2.jpeg'],
+#     ['/content/dogs-cat-examples/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).launch()
+
+
+
+