Upload Deep Learning Workshop 3.ipynb

Deep Learning Workshop 3.ipynb

@@ -0,0 +1,597 @@
+{
+ "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
+}