diabetic_retinopathy_detection(model_code).ipynb

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      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mThe kernel failed to start as the Python Environment 'Python 3.10.12' is no longer available. Consider selecting another kernel or refreshing the list of Python Environments."
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      "source": [
        "import os\n",
        "import random\n",
        "import shutil\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "\n",
        "\n",
        "import os\n",
        "import random\n",
        "import shutil\n",
        "\n",
        "main_folder = 'cleaned_dataset_diabetic_retinopathy\\cleaned_dataset_diabetic_retinopathy\\cleaned_dataset'\n",
        "train_ratio = 0.8\n",
        "\n",
        "train_dir = 'train'\n",
        "test_dir = 'test'\n",
        "\n",
        "os.makedirs(train_dir, exist_ok=True)\n",
        "os.makedirs(test_dir, exist_ok=True)\n",
        "\n",
        "\n",
        "subfolders = [f for f in os.listdir(main_folder) if os.path.isdir(os.path.join(main_folder, f))]\n",
        "\n",
        "\n",
        "for subfolder in subfolders:\n",
        "    subfolder_path = os.path.join(main_folder, subfolder)\n",
        "    all_files = os.listdir(subfolder_path)\n",
        "    random.shuffle(all_files)\n",
        "    \n",
        "    split_index = int(len(all_files) * train_ratio)\n",
        "    train_files = all_files[:split_index]\n",
        "    test_files = all_files[split_index:]\n",
        "    \n",
        "\n",
        "    train_subdir = os.path.join(train_dir, subfolder)\n",
        "    test_subdir = os.path.join(test_dir, subfolder)\n",
        "    os.makedirs(train_subdir, exist_ok=True)\n",
        "    os.makedirs(test_subdir, exist_ok=True)\n",
        "    \n",
        "\n",
        "    for file in train_files:\n",
        "        src = os.path.join(subfolder_path, file)\n",
        "        dst = os.path.join(train_subdir, file)\n",
        "        try:\n",
        "            shutil.copy(src, dst)\n",
        "            print(f\"Copied {src} to {dst}\")\n",
        "        except Exception as e:\n",
        "            print(f\"Error copying {src} to {dst}: {e}\")\n",
        "    \n",
        "\n",
        "    for file in test_files:\n",
        "        src = os.path.join(subfolder_path, file)\n",
        "        dst = os.path.join(test_subdir, file)\n",
        "        try:\n",
        "            shutil.copy(src, dst)\n",
        "            print(f\"Copied {src} to {dst}\")\n",
        "        except Exception as e:\n",
        "            print(f\"Error copying {src} to {dst}: {e}\")\n",
        "\n",
        "batch_size = 32\n",
        "image_size = (150, 150)\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "\n",
        "model = Sequential([\n",
        "    Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Conv2D(128, (3, 3), activation='relu'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(len(train_generator.class_indices), activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.samples // batch_size,\n",
        "    epochs=10,\n",
        "    validation_data=test_generator,\n",
        "    validation_steps=test_generator.samples // batch_size\n",
        ")\n",
        "\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Confusion Matrix\n",
        "test_steps = len(test_generator)\n",
        "predictions = model.predict(test_generator, steps=test_steps)\n",
        "predicted_classes = np.argmax(predictions, axis=1)\n",
        "true_classes = test_generator.classes\n",
        "\n",
        "class_names = list(train_generator.class_indices.keys())\n",
        "\n",
        "conf_matrix = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.show()\n"
      ]
    }
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