Create app.p

app.p

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+from keras.models import load_model
+import numpy as np
+
+# Load the saved model
+model = load_model('model.h5')
+
+predicted_image = model.predict(np.expand_dims(input_image, axis=0))
+
+import tensorflow as tf
+from tensorflow.keras.datasets import cifar10
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from tensorflow.keras.utils import to_categorical
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load CIFAR-10 dataset
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+
+# Normalize pixel values to be between 0 and 1
+x_train = x_train.astype('float32') / 255.0
+x_test = x_test.astype('float32') / 255.0
+
+# One-hot encode the labels
+y_train = to_categorical(y_train, num_classes=10)
+y_test = to_categorical(y_test, num_classes=10)
+
+predicted = model.predict(x_test)
+
+np.argmax(predicted, axis = 1)
+
+from PIL import Image
+import numpy as np
+
+classes = {
+    0 : 'Airplane',
+    1 : 'Automobile',
+    2 : 'Bird',
+    3 : 'Cat',
+    4 : 'Deer',
+    5 : 'Dog',
+    6 : 'Frog',
+    7 : 'Horse',
+    8 : 'Ship',
+    9 : 'Truck'
+}
+
+def prediction(input_img):
+    # Define the transformation
+    transform = transforms.Compose([
+        transforms.Resize(32),
+        transforms.CenterCrop(32),
+        transforms.ToTensor(),
+        # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    pil_image = Image.fromarray(input_img.astype('uint8'))
+    # Apply the transformation
+    transformed_image = np.array(transform(pil_image).T)
+    input_image = np.expand_dims(transformed_image, axis=0)
+    output = model.predict(input_image)
+    # print(transformed_image.shape)
+    # print(transformed_image)
+    # plt.imshow(transformed_image)
+    # plt.show()
+    return classes[np.argmax(output)]
+demo = gr.Interface(prediction, gr.Image(), "text")
+demo.launch()
+