plant-disease-classification-using-cnn.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Plant Disease Classification using Convolutional Neural Network (CNN)"]},{"cell_type":"markdown","metadata":{},"source":["## **Dataset**\n","We will use the [New Plant Diseases Dataset (Augmented)](https://www.kaggle.com/vipoooool/new-plant-diseases-dataset) from Kaggle. This dataset includes images of various plant diseases and healthy plants."]},{"cell_type":"markdown","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":69},"executionInfo":{"elapsed":44561,"status":"ok","timestamp":1710178320377,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"3b220b02","outputId":"9f6ce951-16d3-48f3-d94d-f7240a781e9c"},"source":["### **Libraries Used**\n","\n","- **Pandas:** Data manipulation and analysis.\n","- **NumPy:** Numerical computing and array operations.\n","- **Matplotlib:** Data visualization library.\n","- **TensorFlow:** Open-source machine learning framework.\n","- **Keras (from TensorFlow):** High-level neural networks API.\n","- **OS:** Operating system-related functions.\n","- **PIL (Python Imaging Library):** Image processing library.\n","- **Seaborn:** Data visualization library based on Matplotlib.\n","- **OpenCV (cv2):** Computer vision library.\n"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2024-03-11T19:26:47.616555Z","iopub.status.busy":"2024-03-11T19:26:47.616175Z","iopub.status.idle":"2024-03-11T19:26:47.623673Z","shell.execute_reply":"2024-03-11T19:26:47.622568Z","shell.execute_reply.started":"2024-03-11T19:26:47.616526Z"},"executionInfo":{"elapsed":4635,"status":"ok","timestamp":1710178395492,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"e81c97ee","trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\sonit\\AppData\\Local\\Temp\\ipykernel_7772\\773570136.py:1: DeprecationWarning: \n","Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n","(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n","but was not found to be installed on your system.\n","If this would cause problems for you,\n","please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n","        \n","  import pandas as pd\n"]}],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import os\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array\n","from tensorflow.keras import models, layers\n","import os\n","from PIL import Image\n","import seaborn as sns\n","import cv2\n"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Set Up Data Directories"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-03-11T19:27:26.752437Z","iopub.status.busy":"2024-03-11T19:27:26.751916Z","iopub.status.idle":"2024-03-11T19:27:27.181334Z","shell.execute_reply":"2024-03-11T19:27:27.180244Z","shell.execute_reply.started":"2024-03-11T19:27:26.752403Z"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1710178395493,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"3e44b377","trusted":true},"outputs":[],"source":["BASE_DIR = r'C:/Users/sonit/OneDrive/Desktop/Work/Coding/FarmWise/jn/archive/New Plant Diseases Dataset/New Plant Diseases Dataset'\n","\n","test_dir = os.path.join(BASE_DIR, 'valid')\n","train_dir = os.path.join(BASE_DIR, 'train')\n","\n","train_class_dir = [os.path.join(train_dir, d) for d in os.listdir(train_dir) if os.path.isdir(os.path.join(train_dir, d))]\n","test_class_dir = [os.path.join(test_dir, d) for d in os.listdir(test_dir) if os.path.isdir(os.path.join(test_dir, d))]\n","\n","train_image_paths = []\n","test_image_paths = []\n","\n","for class_dir in train_class_dir:\n","    for file_name in os.listdir(class_dir):\n","        if file_name.endswith(('.JPG','.jpg','jpeg')):\n","            image_path = os.path.join(class_dir, file_name)\n","            train_image_paths.append(image_path)\n","\n","for class_dir in test_class_dir:\n","    for file_name in os.listdir(class_dir):\n","        if file_name.endswith(('.JPG','.jpg','jpeg')):\n","            image_path = os.path.join(class_dir, file_name)\n","            train_image_paths.append(image_path)\n","\n","\n","train_labels = [os.path.basename(class_dir) for class_dir in train_class_dir]\n","test_labels = [os.path.basename(class_dir) for class_dir in test_class_dir]"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":35,"status":"ok","timestamp":1710178395494,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"dfc968db","outputId":"cfab49c6-7cea-494c-f1da-b1d9a6289714"},"outputs":[{"data":{"text/plain":["['Apple___Apple_scab',\n"," 'Apple___Black_rot',\n"," 'Apple___Cedar_apple_rust',\n"," 'Apple___healthy',\n"," 'Blueberry___healthy',\n"," 'Cherry_(including_sour)___healthy',\n"," 'Cherry_(including_sour)___Powdery_mildew',\n"," 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n"," 'Corn_(maize)___Common_rust_',\n"," 'Corn_(maize)___healthy',\n"," 'Corn_(maize)___Northern_Leaf_Blight',\n"," 'Grape___Black_rot',\n"," 'Grape___Esca_(Black_Measles)',\n"," 'Grape___healthy',\n"," 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n"," 'Orange___Haunglongbing_(Citrus_greening)',\n"," 'Peach___Bacterial_spot',\n"," 'Peach___healthy',\n"," 'Pepper,_bell___Bacterial_spot',\n"," 'Pepper,_bell___healthy',\n"," 'Potato___Early_blight',\n"," 'Potato___healthy',\n"," 'Potato___Late_blight',\n"," 'Raspberry___healthy',\n"," 'Soybean___healthy',\n"," 'Squash___Powdery_mildew',\n"," 'Strawberry___healthy',\n"," 'Strawberry___Leaf_scorch',\n"," 'Tomato___Bacterial_spot',\n"," 'Tomato___Early_blight',\n"," 'Tomato___healthy',\n"," 'Tomato___Late_blight',\n"," 'Tomato___Leaf_Mold',\n"," 'Tomato___Septoria_leaf_spot',\n"," 'Tomato___Spider_mites Two-spotted_spider_mite',\n"," 'Tomato___Target_Spot',\n"," 'Tomato___Tomato_mosaic_virus',\n"," 'Tomato___Tomato_Yellow_Leaf_Curl_Virus']"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["classes_train = os.listdir(train_dir)\n","\n","classes_train\n"]},{"cell_type":"markdown","metadata":{},"source":["### 3. Data Preprocessing"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1543,"status":"ok","timestamp":1710179429026,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"d5abbda4","outputId":"76149462-e8ad-408f-9759-30cc15fc2bba"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 140590 images belonging to 38 classes.\n","Found 35144 images belonging to 38 classes.\n"]}],"source":["target_size = (100,100)\n","\n","batch_size = 64\n","\n","\n","train_datagen = ImageDataGenerator(\n","    rescale=1./255,\n","    rotation_range=20,\n","    width_shift_range=0.2,\n","    height_shift_range=0.2,\n","    horizontal_flip=True\n",")\n","\n","train_generator = train_datagen.flow_from_directory(\n","    train_dir,\n","    target_size,\n","    batch_size=batch_size,\n","    class_mode='categorical',\n","    shuffle=True\n",")\n","\n","val_datagen = ImageDataGenerator(\n","    rescale=1./255,\n","    rotation_range=20,\n","    width_shift_range=0.2,\n","    height_shift_range=0.2,\n","    horizontal_flip=True\n",")\n","\n","\n","validation_generator = val_datagen.flow_from_directory(\n","    test_dir,\n","    target_size,\n","    batch_size=batch_size,\n","    class_mode='categorical',\n","    shuffle=False\n",")\n"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1710179429026,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"393b8745","outputId":"c3b0e35d-5e69-4160-dfe1-7f9db8f341cf"},"outputs":[{"data":{"text/plain":["{'Apple___Apple_scab': 0,\n"," 'Apple___Black_rot': 1,\n"," 'Apple___Cedar_apple_rust': 2,\n"," 'Apple___healthy': 3,\n"," 'Blueberry___healthy': 4,\n"," 'Cherry_(including_sour)___Powdery_mildew': 5,\n"," 'Cherry_(including_sour)___healthy': 6,\n"," 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot': 7,\n"," 'Corn_(maize)___Common_rust_': 8,\n"," 'Corn_(maize)___Northern_Leaf_Blight': 9,\n"," 'Corn_(maize)___healthy': 10,\n"," 'Grape___Black_rot': 11,\n"," 'Grape___Esca_(Black_Measles)': 12,\n"," 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)': 13,\n"," 'Grape___healthy': 14,\n"," 'Orange___Haunglongbing_(Citrus_greening)': 15,\n"," 'Peach___Bacterial_spot': 16,\n"," 'Peach___healthy': 17,\n"," 'Pepper,_bell___Bacterial_spot': 18,\n"," 'Pepper,_bell___healthy': 19,\n"," 'Potato___Early_blight': 20,\n"," 'Potato___Late_blight': 21,\n"," 'Potato___healthy': 22,\n"," 'Raspberry___healthy': 23,\n"," 'Soybean___healthy': 24,\n"," 'Squash___Powdery_mildew': 25,\n"," 'Strawberry___Leaf_scorch': 26,\n"," 'Strawberry___healthy': 27,\n"," 'Tomato___Bacterial_spot': 28,\n"," 'Tomato___Early_blight': 29,\n"," 'Tomato___Late_blight': 30,\n"," 'Tomato___Leaf_Mold': 31,\n"," 'Tomato___Septoria_leaf_spot': 32,\n"," 'Tomato___Spider_mites Two-spotted_spider_mite': 33,\n"," 'Tomato___Target_Spot': 34,\n"," 'Tomato___Tomato_Yellow_Leaf_Curl_Virus': 35,\n"," 'Tomato___Tomato_mosaic_virus': 36,\n"," 'Tomato___healthy': 37}"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["train_generator.class_indices"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1710179429026,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"5b1e94e4","outputId":"58f80343-fa50-48ed-9f8e-d71fa582ae2f"},"outputs":[{"data":{"text/plain":["38"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["class_directories = [d for d in os.listdir(train_dir) if os.path.isdir(os.path.join(train_dir, d))]\n","num_classes = len(class_directories)\n","num_classes"]},{"cell_type":"markdown","metadata":{},"source":["### 4. Build CNN Model\n"]},{"cell_type":"code","execution_count":7,"metadata":{"executionInfo":{"elapsed":751,"status":"ok","timestamp":1710179465322,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"f02951a3"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\sonit\\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"]}],"source":["# Define the CNN model\n","model = models.Sequential()\n","\n","model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))\n","model.add(layers.MaxPooling2D((2, 2)))\n","\n","model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))\n","model.add(layers.MaxPooling2D((2, 2)))\n","\n","model.add(layers.Conv2D(128, (3, 3), activation='relu', padding='same'))\n","model.add(layers.MaxPooling2D((2, 2)))\n","\n","model.add(layers.Flatten())\n","\n","model.add(layers.Dense(128, activation='relu'))\n","\n","model.add(layers.Dropout(0.5))  # Optional dropout layer for regularization\n","\n","model.add(layers.Dense(num_classes, activation='softmax'))  # Adjust num_classes based on your data\n","\n","# Compile the model (excluded for brevity)\n","model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1038,"status":"ok","timestamp":1710179474503,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"a9d5a00a","outputId":"afd9e948-9a2d-44ec-d8da-7800bd40a13d"},"outputs":[{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n","</pre>\n"],"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18432</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,424</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">38</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,902</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"],"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m49\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18432\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m2,359,424\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m38\u001b[0m)             │         \u001b[38;5;34m4,902\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,457,574</span> (9.37 MB)\n","</pre>\n"],"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,457,574\u001b[0m (9.37 MB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,457,574</span> (9.37 MB)\n","</pre>\n"],"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,457,574\u001b[0m (9.37 MB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"],"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"]},"metadata":{},"output_type":"display_data"}],"source":["model.summary()"]},{"cell_type":"markdown","metadata":{},"source":["### 5. Train the Model"]},{"cell_type":"code","execution_count":9,"id":"74623060","metadata":{},"outputs":[],"source":["import pickle\n"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4657639,"status":"ok","timestamp":1710184137095,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"b9df177e","outputId":"4610082a-aa7b-4855-a7a4-b42d10935dc2"},"outputs":[],"source":["# num_epochs = 10\n","# steps_per_epoch = len(train_generator)\n","# validation_steps = len(validation_generator)\n","\n","# history = model.fit(\n","#     train_generator,\n","#     steps_per_epoch=steps_per_epoch,\n","#     epochs=num_epochs,\n","#     validation_data=validation_generator,\n","#     validation_steps=validation_steps,\n","#     verbose=1  \n","# )\n","\n","# model.save(\"model.h5\")\n","\n","# # Save the training history to a pickle file\n","# with open('history.pickle', 'wb') as file:\n","#     pickle.dump(history.history, file)"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"elapsed":1529,"status":"ok","timestamp":1710184731119,"user":{"displayName":"Chirag Sanadhya","userId":"06671951063446170947"},"user_tz":-330},"id":"9c7f3476","outputId":"7e39231a-650e-49a8-ed98-a5c925b44072"},"outputs":[],"source":["# import matplotlib.pyplot as plt\n","\n","\n","# plt.figure(figsize=(12, 4))\n","# plt.subplot(1, 3, 1)\n","# plt.plot(history.history['loss'], label='Training Loss')\n","# plt.plot(history.history['val_loss'], label='Validation Loss')\n","# plt.title('Training and Validation Loss')\n","# plt.xlabel('Epoch')\n","# plt.ylabel('Loss')\n","# plt.legend()\n","\n","\n","# plt.subplot(1, 3, 2)\n","# plt.plot(history.history['accuracy'], label='Training Accuracy')\n","# plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n","# plt.title('Training and Validation Accuracy')\n","# plt.xlabel('Epoch')\n","# plt.ylabel('Accuracy')\n","# plt.legend()\n","\n","\n","\n","\n","# plt.tight_layout()\n","# plt.show()\n"]},{"cell_type":"code","execution_count":12,"id":"6cf57c37","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["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"]},{"name":"stdout","output_type":"stream","text":["Found 35144 images belonging to 38 classes.\n","\u001b[1m  2/550\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m27s\u001b[0m 51ms/step - accuracy: 0.8047 - loss: 0.3954  "]},{"name":"stderr","output_type":"stream","text":["c:\\Users\\sonit\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n","  self._warn_if_super_not_called()\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[1m550/550\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 70ms/step - accuracy: 0.8850 - loss: 0.3664\n","Test Loss: 0.4216172993183136\n","Test Accuracy: 0.8642727136611938\n"]}],"source":["from tensorflow.keras.models import load_model\n","\n","# Load the trained model\n","loaded_model = load_model(\"model.h5\")\n","\n","# Create a generator for the test data\n","test_datagen = ImageDataGenerator(rescale=1./255)  # No need for augmentation for test data\n","\n","test_generator = test_datagen.flow_from_directory(\n","    test_dir,\n","    target_size=target_size,\n","    batch_size=batch_size,\n","    class_mode='categorical',\n","    shuffle=False  # No need to shuffle test data\n",")\n","\n","# Evaluate the model on the test data\n","test_loss, test_accuracy = loaded_model.evaluate(test_generator)\n","\n","print(\"Test Loss:\", test_loss)\n","print(\"Test Accuracy:\", test_accuracy)\n"]},{"cell_type":"code","execution_count":null,"id":"44f2a943","metadata":{},"outputs":[],"source":["from tensorflow.keras.preprocessing import image\n","\n","# Function to preprocess user image\n","def preprocess_image(image_path, target_size):\n","    img = image.load_img(image_path, target_size=target_size)\n","    img_array = image.img_to_array(img)\n","    img_array = img_array / 255.0  # Normalize pixel values\n","    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n","    return img_array\n","\n","# Function to make predictions on user image\n","def predict_disease(image_path, model, target_size):\n","    preprocessed_img = preprocess_image(image_path, target_size)\n","    prediction = model.predict(preprocessed_img)\n","    predicted_class_index = np.argmax(prediction)  # Get the index of the class with highest probability\n","    return predicted_class_index\n","\n","# Path to user's image\n","user_image_path = \"path/to/your/image.jpg\"  # Change this to the path of your image\n","\n","# Make predictions on user's image\n","predicted_class_index = predict_disease(user_image_path, loaded_model, target_size)\n","\n","# Get the class label corresponding to the predicted index\n","class_directories = [d for d in os.listdir(train_dir) if os.path.isdir(os.path.join(train_dir, d))]\n","class_label = class_directories[predicted_class_index]\n","\n","print(\"Predicted Disease:\", class_label)\n"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":78313,"sourceId":182633,"sourceType":"datasetVersion"}],"dockerImageVersionId":30664,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","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":"3.12.1"},"papermill":{"default_parameters":{},"duration":66.901736,"end_time":"2024-03-10T13:31:03.387760","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-03-10T13:29:56.486024","version":"2.5.0"}},"nbformat":4,"nbformat_minor":5}