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{
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"id": "02edd069-0381-4537-902e-03ffd273349c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(\n",
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
"WARNING:absl:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
]
}
],
"source": [
"from keras.models import load_model\n",
"import numpy as np\n",
"\n",
"# Load the saved model\n",
"model = load_model('model.h5')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "78c9d169-adbe-4588-9a78-fb02b90e3781",
"metadata": {},
"outputs": [],
"source": [
"from torchvision import transforms"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f57b1e4e-c171-4233-addf-a5bbdd91896f",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'input_image' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[16], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Perform inference on the input image\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# Make sure your input shape matches the input shape of the model\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m predicted_image \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(np\u001b[38;5;241m.\u001b[39mexpand_dims(\u001b[43minput_image\u001b[49m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m))\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# The output 'predicted_image' will be the deblurred image generated by the model\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# You can further process or save the output image as needed\u001b[39;00m\n",
"\u001b[1;31mNameError\u001b[0m: name 'input_image' is not defined"
]
}
],
"source": [
"# Perform inference on the input image\n",
"# Make sure your input shape matches the input shape of the model\n",
"predicted_image = model.predict(np.expand_dims(input_image, axis=0))\n",
"\n",
"# The output 'predicted_image' will be the deblurred image generated by the model\n",
"# You can further process or save the output image as needed\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "87814748-7c0b-41e2-998b-d3a3eb6d7bbd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2.16.1'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
"tf.__version__"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9fafecb4-54e9-43a5-ac19-143205069848",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import cifar10\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
"from tensorflow.keras.utils import to_categorical\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"\n",
"# Load CIFAR-10 dataset\n",
"(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
"\n",
"# Normalize pixel values to be between 0 and 1\n",
"x_train = x_train.astype('float32') / 255.0\n",
"x_test = x_test.astype('float32') / 255.0\n",
"\n",
"# One-hot encode the labels\n",
"y_train = to_categorical(y_train, num_classes=10)\n",
"y_test = to_categorical(y_test, num_classes=10)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "b8e744fc-d509-49a9-a1d1-9be7f37a6c21",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 1.],\n",
" [0., 0., 0., ..., 0., 0., 1.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 1.],\n",
" [0., 1., 0., ..., 0., 0., 0.],\n",
" [0., 1., 0., ..., 0., 0., 0.]])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0ae22ae2-c40d-4a4a-9c25-00d2c423b53c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 72ms/step\n"
]
}
],
"source": [
"predicted = model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e80370e5-6753-4b04-afde-ddcfcdb3c148",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 32, 32, 3)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9bd01caf-8ce5-465a-b734-a5480b96521d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 8, 8, ..., 5, 1, 7], dtype=int64)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.argmax(predicted, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "070f8ef8-6522-4fe7-9d35-67f1861de531",
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"# Define AlexNet architecture\n",
"model = Sequential([\n",
" # First convolutional layer\n",
" Conv2D(96, (11, 11), strides=(1, 1), activation='relu', input_shape=(32, 32, 3)),\n",
" MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),\n",
" # Second convolutional layer\n",
" Conv2D(256, (5, 5), padding='same', activation='relu'),\n",
" MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),\n",
" # Third convolutional layer\n",
" Conv2D(384, (3, 3), padding='same', activation='relu'),\n",
" # Fourth convolutional layer\n",
" Conv2D(384, (3, 3), padding='same', activation='relu'),\n",
" # Fifth convolutional layer\n",
" Conv2D(256, (3, 3), padding='same', activation='relu'),\n",
" MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),\n",
" # Flatten the convolutional layers output for fully connected layers\n",
" Flatten(),\n",
" # First fully connected layer\n",
" Dense(4096, activation='relu'),\n",
" Dropout(0.5),\n",
" # Second fully connected layer\n",
" Dense(4096, activation='relu'),\n",
" Dropout(0.5),\n",
" # Output layer\n",
" Dense(10, activation='softmax')\n",
"])\n",
"\n",
"# Compile the model with a lower learning rate\n",
"optimizer = Adam(learning_rate=0.0001)\n",
"model.compile(optimizer=optimizer,\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"# Data augmentation\n",
"datagen = ImageDataGenerator(\n",
" rotation_range=15,\n",
" width_shift_range=0.1,\n",
" height_shift_range=0.1,\n",
" horizontal_flip=True,\n",
")\n",
"\n",
"datagen.fit(x_train)\n",
"\n",
"# Train the model with data augmentation\n",
"model.fit(datagen.flow(x_train, y_train, batch_size=128), epochs=25, validation_data=(x_test, y_test))\n",
"\n",
"# Evaluate the model on the test set\n",
"test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=2)\n",
"\n",
"print(\"\\nTest Accuracy:\", test_accuracy)\n",
"print(\"Test Loss:\", test_loss)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "82b6c768-2b9d-4633-8e17-96d26d814421",
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "692a13a1-3483-4dd9-9364-c27e909b89d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7896\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7896/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1280, 717, 3)\n",
"(1, 32, 32, 3)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\gradio\\queueing.py\", line 501, in call_prediction\n",
" output = await route_utils.call_process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\gradio\\route_utils.py\", line 253, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\gradio\\blocks.py\", line 1695, in process_api\n",
" result = await self.call_function(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\gradio\\blocks.py\", line 1235, in call_function\n",
" prediction = await anyio.to_thread.run_sync(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\anyio\\to_thread.py\", line 33, in run_sync\n",
" return await get_asynclib().run_sync_in_worker_thread(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 877, in run_sync_in_worker_thread\n",
" return await future\n",
" ^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 807, in run\n",
" result = context.run(func, *args)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\gradio\\utils.py\", line 692, in wrapper\n",
" response = f(*args, **kwargs)\n",
" ^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Temp\\ipykernel_29808\\1451871443.py\", line 16, in prediction\n",
" output = model.predict(transformed_image)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 122, in error_handler\n",
" raise e.with_traceback(filtered_tb) from None\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\models\\functional.py\", line 280, in _adjust_input_rank\n",
" raise ValueError(\n",
"ValueError: Exception encountered when calling Sequential.call().\n",
"\n",
"\u001b[1mInvalid input shape for input Tensor(\"data:0\", shape=(32, 32, 3), dtype=float32). Expected shape (None, 32, 32, 3), but input has incompatible shape (32, 32, 3)\u001b[0m\n",
"\n",
"Arguments received by Sequential.call():\n",
" • inputs=tf.Tensor(shape=(32, 32, 3), dtype=float32)\n",
" • training=False\n",
" • mask=None\n",
"Exception in thread Thread-81 (_do_normal_analytics_request):\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_exceptions.py\", line 10, in map_exceptions\n",
" yield\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_backends\\sync.py\", line 168, in start_tls\n",
" raise exc\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_backends\\sync.py\", line 163, in start_tls\n",
" sock = ssl_context.wrap_socket(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\ssl.py\", line 455, in wrap_socket\n",
" return self.sslsocket_class._create(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\ssl.py\", line 1046, in _create\n",
" self.do_handshake()\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\ssl.py\", line 1317, in do_handshake\n",
" self._sslobj.do_handshake()\n",
"TimeoutError: _ssl.c:983: The handshake operation timed out\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 69, in map_httpcore_exceptions\n",
" yield\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 233, in handle_request\n",
" resp = self._pool.handle_request(req)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_sync\\connection_pool.py\", line 268, in handle_request\n",
" raise exc\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_sync\\connection_pool.py\", line 251, in handle_request\n",
" response = connection.handle_request(request)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_sync\\connection.py\", line 99, in handle_request\n",
" raise exc\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_sync\\connection.py\", line 76, in handle_request\n",
" stream = self._connect(request)\n",
" ^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_sync\\connection.py\", line 156, in _connect\n",
" stream = stream.start_tls(**kwargs)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_backends\\sync.py\", line 152, in start_tls\n",
" with map_exceptions(exc_map):\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 155, in __exit__\n",
" self.gen.throw(value)\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpcore\\_exceptions.py\", line 14, in map_exceptions\n",
" raise to_exc(exc) from exc\n",
"httpcore.ConnectTimeout: _ssl.c:983: The handshake operation timed out\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\threading.py\", line 1052, in _bootstrap_inner\n",
" self.run()\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\threading.py\", line 989, in run\n",
" self._target(*self._args, **self._kwargs)\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\gradio\\analytics.py\", line 63, in _do_normal_analytics_request\n",
" httpx.post(url, data=data, timeout=5)\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_api.py\", line 319, in post\n",
" return request(\n",
" ^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_api.py\", line 106, in request\n",
" return client.request(\n",
" ^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_client.py\", line 827, in request\n",
" return self.send(request, auth=auth, follow_redirects=follow_redirects)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_client.py\", line 914, in send\n",
" response = self._send_handling_auth(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_client.py\", line 942, in _send_handling_auth\n",
" response = self._send_handling_redirects(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_client.py\", line 979, in _send_handling_redirects\n",
" response = self._send_single_request(request)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_client.py\", line 1015, in _send_single_request\n",
" response = transport.handle_request(request)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 232, in handle_request\n",
" with map_httpcore_exceptions():\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 155, in __exit__\n",
" self.gen.throw(value)\n",
" File \"C:\\Users\\Haider Ali\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\httpx\\_transports\\default.py\", line 86, in map_httpcore_exceptions\n",
" raise mapped_exc(message) from exc\n",
"httpx.ConnectTimeout: _ssl.c:983: The handshake operation timed out\n"
]
}
],
"source": [
"def prediction(input_img):\n",
" # image = Image.open(\"img1.jpg\")\n",
" print(input_img.shape)\n",
" # Define the transformation\n",
" transform = transforms.Compose([\n",
" transforms.Resize(32),\n",
" transforms.CenterCrop(32),\n",
" transforms.ToTensor(),\n",
" # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
" ])\n",
" pil_image = Image.fromarray(input_img.astype('uint8'))\n",
" # Apply the transformation\n",
" transformed_image = np.array(transform(pil_image).T)\n",
" input_image = np.expand_dims(transformed_image, axis=0)\n",
" print(input_image.shape)\n",
" output = model.predict(transformed_image)\n",
" print(output)\n",
" # print(transformed_image.shape)\n",
" # print(transformed_image)\n",
" # plt.imshow(transformed_image)\n",
" # plt.show()\n",
" # return transformed_image\n",
"demo = gr.Interface(prediction, gr.Image(), \"image\")\n",
"demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "b2b8aaaf-ce9c-4c25-875e-fce01fbf3832",
"metadata": {},
"outputs": [],
"source": [
"classes = {\n",
" 0 : 'Airplane',\n",
" 1 : 'Automobile',\n",
" 2 : 'Bird',\n",
" 3 : 'Cat',\n",
" 4 : 'Deer',\n",
" 5 : 'Dog',\n",
" 6 : 'Frog',\n",
" 7 : 'Horse',\n",
" 8 : 'Ship',\n",
" 9 : 'Truck'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "d3da2b58-6b86-4e6d-9b7f-9f8ce1fe2339",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7904\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7904/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1600, 1204, 3)\n",
"(1, 32, 32, 3)\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
"[[0.00640055 0.17760815 0.04763744 0.09621317 0.05900569 0.09116109\n",
" 0.02236336 0.09745935 0.01388952 0.38826168]]\n"
]
}
],
"source": [
"def prediction(input_img):\n",
" # Define the transformation\n",
" transform = transforms.Compose([\n",
" transforms.Resize(32),\n",
" transforms.CenterCrop(32),\n",
" transforms.ToTensor(),\n",
" # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
" ])\n",
" pil_image = Image.fromarray(input_img.astype('uint8'))\n",
" # Apply the transformation\n",
" transformed_image = np.array(transform(pil_image).T)\n",
" input_image = np.expand_dims(transformed_image, axis=0)\n",
" output = model.predict(input_image)\n",
" # print(transformed_image.shape)\n",
" # print(transformed_image)\n",
" # plt.imshow(transformed_image)\n",
" # plt.show()\n",
" return classes[np.argmax(output)]\n",
"demo = gr.Interface(prediction, gr.Image(), \"text\")\n",
"demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2070d29e-b593-490e-b867-6a10ab1b02ff",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|