Uploaded project structure

Butterfly classification with CNN.ipynb

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+{
+ "cells": [
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+   "id": "9b10cf68",
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+   },
+   "source": [
+    "# Import Libraries and Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "82a4c58c",
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+   "outputs": [],
+   "source": [
+    "## Remove Warnings ## \n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "## Data ## \n",
+    "import numpy as np\n",
+    "import pandas as pd \n",
+    "import os \n",
+    "\n",
+    "## Visualization ## \n",
+    "import matplotlib.pyplot as plt \n",
+    "import plotly.express as px\n",
+    "import seaborn as sns\n",
+    "import plotly.graph_objects as go \n",
+    "\n",
+    "## Image ## \n",
+    "import cv2\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator \n",
+    "\n",
+    "## Tensorflow ## \n",
+    "from tensorflow.keras.models import Sequential, Model\n",
+    "from tensorflow.keras.layers import Input, Dense , Conv2D , Dropout , Flatten , Activation, MaxPooling2D , GlobalAveragePooling2D\n",
+    "from tensorflow.keras.optimizers import Adam , RMSprop \n",
+    "from tensorflow.keras.layers import BatchNormalization\n",
+    "from tensorflow.keras.callbacks import ReduceLROnPlateau , EarlyStopping , ModelCheckpoint , LearningRateScheduler\n",
+    "from tensorflow.keras.applications import ResNet50V2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1906bacd",
+   "metadata": {
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+     "start_time": "2023-09-03T09:54:38.450401",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>class id</th>\n",
+       "      <th>filepaths</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>data set</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>C:/Users/kamel/Documents/Image Classification/...</td>\n",
+       "      <td>ADONIS</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>C:/Users/kamel/Documents/Image Classification/...</td>\n",
+       "      <td>ADONIS</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>C:/Users/kamel/Documents/Image Classification/...</td>\n",
+       "      <td>ADONIS</td>\n",
+       "      <td>train</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>C:/Users/kamel/Documents/Image Classification/...</td>\n",
+       "      <td>ADONIS</td>\n",
+       "      <td>train</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>C:/Users/kamel/Documents/Image Classification/...</td>\n",
+       "      <td>ADONIS</td>\n",
+       "      <td>train</td>\n",
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+       "  </tbody>\n",
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+      "text/plain": [
+       "   class id                                          filepaths  labels  \\\n",
+       "0         0  C:/Users/kamel/Documents/Image Classification/...  ADONIS   \n",
+       "1         0  C:/Users/kamel/Documents/Image Classification/...  ADONIS   \n",
+       "2         0  C:/Users/kamel/Documents/Image Classification/...  ADONIS   \n",
+       "3         0  C:/Users/kamel/Documents/Image Classification/...  ADONIS   \n",
+       "4         0  C:/Users/kamel/Documents/Image Classification/...  ADONIS   \n",
+       "\n",
+       "  data set  \n",
+       "0    train  \n",
+       "1    train  \n",
+       "2    train  \n",
+       "3    train  \n",
+       "4    train  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
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+   ],
+   "source": [
+    "df = pd.read_csv('C:/Users/kamel/Documents/Image Classification/butterfly-dataset/butterflies and moths.csv')  \n",
+    "IMAGE_DIR = 'C:/Users/kamel/Documents/Image Classification/butterfly-dataset'\n",
+    "df['filepaths'] = IMAGE_DIR + '/' + df['filepaths']\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
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+   "id": "1b2dd2d3",
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+    "tags": []
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+   "outputs": [],
+   "source": [
+    "train_df = df.loc[df['data set'] == 'train']\n",
+    "val_df = df.loc[df['data set'] == 'valid']\n",
+    "test_df = df.loc[df['data set'] == 'test']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
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+    "tags": []
+   },
+   "source": [
+    "# Exploratory Data Analysis"
+   ]
+  },
+  {
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+   "execution_count": 13,
+   "id": "01cf1f03",
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+   "source": [
+    "label_counts = df['labels'].value_counts()[:10]\n",
+    "\n",
+    "fig = px.bar(x=label_counts.index, \n",
+    "             y=label_counts.values,\n",
+    "             color=label_counts.values,\n",
+    "             text=label_counts.values,\n",
+    "             color_continuous_scale='Blues')\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title_text='Labels Distribution',\n",
+    "    template='plotly_white',\n",
+    "    xaxis=dict(\n",
+    "        title='Label',\n",
+    "    ),\n",
+    "    yaxis=dict(\n",
+    "        title='Count',\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "fig.update_traces(marker_line_color='black', \n",
+    "                  marker_line_width=1.5, \n",
+    "                  opacity=0.8)\n",
+    " \n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40cae06a",
+   "metadata": {
+    "papermill": {
+     "duration": 0.045333,
+     "end_time": "2023-09-03T09:54:44.387581",
+     "exception": false,
+     "start_time": "2023-09-03T09:54:44.342248",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Generate Image using ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9c49b50f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-09-03T09:54:44.571283Z",
+     "iopub.status.busy": "2023-09-03T09:54:44.570890Z",
+     "iopub.status.idle": "2023-09-03T09:54:47.843050Z",
+     "shell.execute_reply": "2023-09-03T09:54:47.842111Z"
+    },
+    "papermill": {
+     "duration": 3.322283,
+     "end_time": "2023-09-03T09:54:47.845125",
+     "exception": false,
+     "start_time": "2023-09-03T09:54:44.522842",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1256 images belonging to 10 classes.\n",
+      "Found 50 images belonging to 10 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# only train data needs to be augmented \n",
+    "train_gen = ImageDataGenerator(horizontal_flip=True, vertical_flip=True, rescale=1/255.)\n",
+    "val_gen = ImageDataGenerator(rescale=1/255.)\n",
+    "\n",
+    "train_dir = 'C:/Users/kamel/Documents/Image Classification/butterfly-dataset/train'\n",
+    "val_dir = 'C:/Users/kamel/Documents/Image Classification/butterfly-dataset/valid'\n",
+    "\n",
+    "BATCH_SIZE = 16\n",
+    "SEED = 56\n",
+    "IMAGE_SIZE = (244, 244)\n",
+    "\n",
+    "train_flow_gen = train_gen.flow_from_directory(directory=train_dir,\n",
+    "                                              class_mode='sparse',\n",
+    "                                              batch_size=BATCH_SIZE,\n",
+    "                                              target_size=IMAGE_SIZE,\n",
+    "                                              seed=SEED)\n",
+    "\n",
+    "val_flow_gen = val_gen.flow_from_directory(directory=val_dir,\n",
+    "                                            class_mode='sparse',\n",
+    "                                            batch_size=BATCH_SIZE,\n",
+    "                                            target_size=IMAGE_SIZE,\n",
+    "                                            seed=SEED)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0398ba07",
+   "metadata": {
+    "papermill": {
+     "duration": 0.045878,
+     "end_time": "2023-09-03T09:54:47.938297",
+     "exception": false,
+     "start_time": "2023-09-03T09:54:47.892419",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Create Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2b80bd86",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-09-03T09:54:48.123906Z",
+     "iopub.status.busy": "2023-09-03T09:54:48.122767Z",
+     "iopub.status.idle": "2023-09-03T09:54:48.130732Z",
+     "shell.execute_reply": "2023-09-03T09:54:48.129884Z"
+    },
+    "papermill": {
+     "duration": 0.058368,
+     "end_time": "2023-09-03T09:54:48.132785",
+     "exception": false,
+     "start_time": "2023-09-03T09:54:48.074417",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "verbose=False\n",
+    "    \n",
+    "input_tensor = Input(shape=(224, 224, 3))\n",
+    "    \n",
+    "base_model = ResNet50V2(input_tensor=input_tensor, include_top=False, weights='imagenet')\n",
+    "    \n",
+    "bm_output = base_model.output\n",
+    "\n",
+    "x = GlobalAveragePooling2D()(bm_output)\n",
+    "x = Dense(1024, activation='relu')(x)\n",
+    "x = Dropout(rate=0.5)(x)\n",
+    "output = Dense(100, activation='softmax')(x)\n",
+    "model = Model(inputs=input_tensor, outputs=output)\n",
+    "    \n",
+    "if verbose:\n",
+    "    model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c28b3bd4",
+   "metadata": {
+    "papermill": {
+     "duration": 0.327423,
+     "end_time": "2023-09-03T10:55:37.594519",
+     "exception": false,
+     "start_time": "2023-09-03T10:55:37.267096",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# ResNet Modelling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e1087e22",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-09-03T10:55:38.752875Z",
+     "iopub.status.busy": "2023-09-03T10:55:38.752385Z",
+     "iopub.status.idle": "2023-09-03T10:55:41.001254Z",
+     "shell.execute_reply": "2023-09-03T10:55:41.000298Z"
+    },
+    "papermill": {
+     "duration": 2.573106,
+     "end_time": "2023-09-03T10:55:41.003664",
+     "exception": false,
+     "start_time": "2023-09-03T10:55:38.430558",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "rlr_cb = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, mode='min', verbose=0)\n",
+    "early_cb = EarlyStopping(monitor='val_loss', patience=5, mode='min', verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "19196570",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-09-03T10:55:41.562460Z",
+     "iopub.status.busy": "2023-09-03T10:55:41.562088Z",
+     "iopub.status.idle": "2023-09-03T11:28:47.817367Z",
+     "shell.execute_reply": "2023-09-03T11:28:47.816362Z"
+    },
+    "papermill": {
+     "duration": 1986.53774,
+     "end_time": "2023-09-03T11:28:47.819746",
+     "exception": false,
+     "start_time": "2023-09-03T10:55:41.282006",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "79/79 [==============================] - 661s 8s/step - loss: 1.2424 - accuracy: 0.6815 - val_loss: 76.2388 - val_accuracy: 0.1400 - lr: 0.0010\n",
+      "Epoch 2/5\n",
+      "79/79 [==============================] - 686s 9s/step - loss: 0.6616 - accuracy: 0.8169 - val_loss: 3.6352 - val_accuracy: 0.6000 - lr: 0.0010\n",
+      "Epoch 3/5\n",
+      "79/79 [==============================] - 692s 9s/step - loss: 0.4898 - accuracy: 0.8583 - val_loss: 6.5402 - val_accuracy: 0.3800 - lr: 0.0010\n",
+      "Epoch 4/5\n",
+      "79/79 [==============================] - 699s 9s/step - loss: 0.4228 - accuracy: 0.8933 - val_loss: 0.5610 - val_accuracy: 0.8200 - lr: 0.0010\n",
+      "Epoch 5/5\n",
+      "79/79 [==============================] - 694s 9s/step - loss: 0.2828 - accuracy: 0.9132 - val_loss: 0.0705 - val_accuracy: 0.9800 - lr: 0.0010\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x2569b583a00>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(train_flow_gen, epochs=5,\n",
+    "         steps_per_epoch=int(np.ceil(train_df.shape[0]/BATCH_SIZE)),\n",
+    "         validation_data=val_flow_gen,\n",
+    "         validation_steps=int(np.ceil(val_df.shape[0]/BATCH_SIZE)),\n",
+    "         callbacks=[rlr_cb, early_cb])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a7fdf171",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 50 images belonging to 10 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_dir = 'C:/Users/kamel/Documents/Image Classification/butterfly-dataset/test'\n",
+    "test_gen = ImageDataGenerator(rescale=1/255.)\n",
+    "test_flow_gen = test_gen.flow_from_directory(directory=test_dir,\n",
+    "                                             class_mode='sparse',\n",
+    "                                             batch_size=BATCH_SIZE,\n",
+    "                                             target_size=IMAGE_SIZE,\n",
+    "                                             seed=SEED)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f5ac91bb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-09-03T11:28:48.864295Z",
+     "iopub.status.busy": "2023-09-03T11:28:48.863901Z",
+     "iopub.status.idle": "2023-09-03T11:28:54.311623Z",
+     "shell.execute_reply": "2023-09-03T11:28:54.310452Z"
+    },
+    "papermill": {
+     "duration": 5.996544,
+     "end_time": "2023-09-03T11:28:54.313850",
+     "exception": false,
+     "start_time": "2023-09-03T11:28:48.317306",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4/4 [==============================] - 5s 1s/step - loss: 0.1499 - accuracy: 0.9400\n",
+      "ResNet Test Data Accuracy: 0.9399999976158142\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('ResNet Test Data Accuracy: {0}'.format(model.evaluate(test_flow_gen)[1:][0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "78b5d06a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the current weights manually\n",
+    "model.save('C:/Users/kamel/Documents/Image Classification/model_checkpoint_manual_effnet.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a9e58e9",
+   "metadata": {},
+   "source": [
+    "# Deployment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "72ab47ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7861/\" 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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 1s 1s/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\queueing.py\", line 495, in call_prediction\n",
+      "    output = await route_utils.call_process_api(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\route_utils.py\", line 232, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1561, in process_api\n",
+      "    result = await self.call_function(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1179, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\anyio\\to_thread.py\", line 28, in run_sync\n",
+      "    return await get_asynclib().run_sync_in_worker_thread(func, *args, cancellable=cancellable,\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 818, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 754, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\utils.py\", line 678, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "  File \"C:\\Users\\kamel\\AppData\\Local\\Temp\\ipykernel_9500\\3770787755.py\", line 35, in classify_image\n",
+      "    img = preprocess_image(img)\n",
+      "  File \"C:\\Users\\kamel\\AppData\\Local\\Temp\\ipykernel_9500\\3770787755.py\", line 28, in preprocess_image\n",
+      "    raise ValueError(\"Unsupported input type. Please provide a file path or a NumPy array.\")\n",
+      "ValueError: Unsupported input type. Please provide a file path or a NumPy array.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 131ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\queueing.py\", line 495, in call_prediction\n",
+      "    output = await route_utils.call_process_api(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\route_utils.py\", line 232, in call_process_api\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1561, in process_api\n",
+      "    result = await self.call_function(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\blocks.py\", line 1179, in call_function\n",
+      "    prediction = await anyio.to_thread.run_sync(\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\anyio\\to_thread.py\", line 28, in run_sync\n",
+      "    return await get_asynclib().run_sync_in_worker_thread(func, *args, cancellable=cancellable,\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 818, in run_sync_in_worker_thread\n",
+      "    return await future\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 754, in run\n",
+      "    result = context.run(func, *args)\n",
+      "  File \"D:\\Software\\anaconda3\\lib\\site-packages\\gradio\\utils.py\", line 678, in wrapper\n",
+      "    response = f(*args, **kwargs)\n",
+      "  File \"C:\\Users\\kamel\\AppData\\Local\\Temp\\ipykernel_9500\\3770787755.py\", line 35, in classify_image\n",
+      "    img = preprocess_image(img)\n",
+      "  File \"C:\\Users\\kamel\\AppData\\Local\\Temp\\ipykernel_9500\\3770787755.py\", line 28, in preprocess_image\n",
+      "    raise ValueError(\"Unsupported input type. Please provide a file path or a NumPy array.\")\n",
+      "ValueError: Unsupported input type. Please provide a file path or a NumPy array.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import load_model\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "\n",
+    "# Load the trained model\n",
+    "model_path = 'C:/Users/kamel/Documents/Image Classification/model_checkpoint_manual_effnet.h5'\n",
+    "model = load_model(model_path)\n",
+    "\n",
+    "class_names = ['ADONIS', 'AFRICAN GIANT SWALLOWTAIL', 'AMERICAN SNOOT', 'AN 88', 'APPOLLO', 'ARCIGERA FLOWER MOTH', 'ATALA', 'ATLAS MOTH', 'BANDED ORANGE HELICONIAN', 'BANDED PEACOCK']\n",
+    "\n",
+    "# Define a function to preprocess the input image\n",
+    "def preprocess_image(img):\n",
+    "    # Check if img is a file path or an image object\n",
+    "    if isinstance(img, str):\n",
+    "        # Load and preprocess the image\n",
+    "        img = cv2.imread(img)\n",
+    "        img = cv2.resize(img, (224, 224))\n",
+    "        img = img / 255.0  # Normalize pixel values\n",
+    "        img = np.expand_dims(img, axis=0)  # Add batch dimension\n",
+    "    elif isinstance(img, np.ndarray):\n",
+    "        # If img is already an image array, resize it\n",
+    "        img = cv2.resize(img, (224, 224))\n",
+    "        img = img / 255.0  # Normalize pixel values\n",
+    "        img = np.expand_dims(img, axis=0)  # Add batch dimension\n",
+    "    else:\n",
+    "        raise ValueError(\"Unsupported input type. Please provide a file path or a NumPy array.\")\n",
+    "\n",
+    "    return img\n",
+    "\n",
+    "# Define the classification function\n",
+    "def classify_image(img):\n",
+    "    # Preprocess the image\n",
+    "    img = preprocess_image(img)\n",
+    "    \n",
+    "    # Make predictions\n",
+    "    predictions = model.predict(img)\n",
+    "    \n",
+    "    # Get the predicted class label\n",
+    "    predicted_class = np.argmax(predictions)\n",
+    "    \n",
+    "    # Get the predicted class name\n",
+    "    predicted_class_name = class_names[predicted_class]\n",
+    "    \n",
+    "    return f\"Predicted Class: {predicted_class_name}\"\n",
+    "\n",
+    "# Create a Gradio interface\n",
+    "iface = gr.Interface(fn=classify_image, \n",
+    "                     inputs=\"image\",\n",
+    "                     outputs=\"text\",\n",
+    "                     live=True)\n",
+    "\n",
+    "# Launch the Gradio app\n",
+    "iface.launch()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b97686a4",
+   "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": "3.9.18"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 5680.107554,
+   "end_time": "2023-09-03T11:29:02.521595",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2023-09-03T09:54:22.414041",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}