Create app.py

Added all the code for the app.py

app.py

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+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import cv2
+import matplotlib.pyplot as plt
+
+
+model = from_pretrained_keras("harsha163/CutMix_data_augmentation_for_image_classification")
+
+# functions for inference                   
+IMG_SIZE = 32
+
+class_names = [
+    "Airplane",
+    "Automobile",
+    "Bird",
+    "Cat",
+    "Deer",
+    "Dog",
+    "Frog",
+    "Horse",
+    "Ship",
+    "Truck",
+]
+
+# resize the image and it to a float between 0,1
+def preprocess_image(image, label):
+    image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
+    image = tf.image.convert_image_dtype(image, tf.float32) / 255.0
+    return image, label
+
+
+def read_image(image):
+    image = tf.convert_to_tensor(image)
+    image.set_shape([None, None, 3])
+    print('$$$$$$$$$$$$$$$$$$$$$ in read image $$$$$$$$$$$$$$$$$$$$$$')
+    print(image.shape)
+    plt.imshow(image)
+    plt.show()
+    # image = tf.image.resize(images=image, size=[IMG_SIZE, IMG_SIZE])
+    # image = image / 127.5 - 1
+    image, _ = preprocess_image(image, 1) #  1 here is a temporary label
+    return image
+
+def infer(input_image):
+    print('#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$')
+    image_tensor = read_image(input_image)
+    print(image_tensor.shape)
+    predictions = model.predict(np.expand_dims((image_tensor), axis=0))
+    predictions = np.squeeze(predictions)
+    predictions = np.argmax(predictions) # , axis=2
+    predicted_label = class_names[predictions.item()]
+    return str(predicted_label)
+    
+    
+# get the inputs
+input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))
+# the app outputs two segmented images
+output = [gr.outputs.Label()]
+# it's good practice to pass examples, description and a title to guide users
+examples = [["./content/examples/Frog.jpeg"], ["./content/examples/Truck.jpeg"]] 
+title = "Image classification"
+description = "Upload an image or select from examples to classify it"
+
+gr_interface = gr.Interface(infer, input, output, examples=examples, allow_flagging=False, analytics_enabled=False, title=title, description=description).launch(enable_queue=True, debug=True)
+gr_interface.launch()