"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+ "\n",
+ "# Compute the confusion matrix\n",
+ "conf_matrix = confusion_matrix(y_true, y_pred)\n",
+ "\n",
+ "# Plot the confusion matrix\n",
+ "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=target_names)\n",
+ "disp.plot(cmap='Blues')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aeui1qutIGPa"
+ },
+ "source": [
+ "Recommendation System Preprocessing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 321
+ },
+ "id": "8pSkodRSIJE6",
+ "outputId": "40973340-5872-40d8-87ec-7e2523c4905a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_5\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+ "│ vgg16 (Functional) │ (None, 4, 4, 512) │ 14,714,688 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ global_average_pooling2d_5 │ (None, 512) │ 0 │\n",
+ "│ (GlobalAveragePooling2D) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_10 (Dense) │ (None, 128) │ 65,664 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_5 (Dropout) │ (None, 128) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_11 (Dense) │ (None, 1) │ 129 │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+ "
\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",
+ "│ vgg16 (\u001b[38;5;33mFunctional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m14,714,688\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ global_average_pooling2d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_10 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m65,664\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_5 (\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_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m129\u001b[0m │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 29,070,917 (110.90 MB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m29,070,917\u001b[0m (110.90 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 7,145,217 (27.26 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,145,217\u001b[0m (27.26 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 7,635,264 (29.13 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,635,264\u001b[0m (29.13 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Optimizer params: 14,290,436 (54.51 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m14,290,436\u001b[0m (54.51 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.applications import VGG16\n",
+ "from tensorflow.keras.models import load_model\n",
+ "\n",
+ "\n",
+ "# If you have your own fine-tuned model, load it like this:\n",
+ "vgg16_model = load_model('/content/drive/MyDrive/Deep Learning Project/vgg16_best_model.keras')\n",
+ "\n",
+ "vgg16_model.summary()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 353
+ },
+ "id": "zRPxHMM7-KxH",
+ "outputId": "533b324e-3008-406a-c447-8acf0a68e894"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential_1\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
+ "│ xception (Functional) │ (None, 7, 7, 2048) │ 20,861,480 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ global_average_pooling2d_1 │ (None, 2048) │ 0 │\n",
+ "│ (GlobalAveragePooling2D) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 2048) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (Dense) │ (None, 256) │ 524,544 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_3 (Dropout) │ (None, 256) │ 0 │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 3) │ 771 │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
+ "
\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",
+ "│ xception (\u001b[38;5;33mFunctional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m20,861,480\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ global_average_pooling2d_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m524,544\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m771\u001b[0m │\n",
+ "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 41,394,115 (157.91 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m41,394,115\u001b[0m (157.91 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 10,003,659 (38.16 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,003,659\u001b[0m (38.16 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 11,383,136 (43.42 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m11,383,136\u001b[0m (43.42 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Optimizer params: 20,007,320 (76.32 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m20,007,320\u001b[0m (76.32 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.applications import Xception\n",
+ "from tensorflow.keras.models import load_model\n",
+ "\n",
+ "xception_model = load_model('/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras')\n",
+ "\n",
+ "xception_model.summary() # Check the model architecture"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YqoYxUtXJVPQ"
+ },
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras.preprocessing import image\n",
+ "from tensorflow.keras.applications.vgg16 import preprocess_input as vgg16_preprocess\n",
+ "from tensorflow.keras.applications.xception import preprocess_input as xception_preprocess\n",
+ "import numpy as np\n",
+ "\n",
+ "# Load and preprocess the input image\n",
+ "def preprocess_image(img_path, model_type='vgg16'):\n",
+ " img = image.load_img(img_path, target_size=(224, 224)) # Resize the image\n",
+ " img_array = image.img_to_array(img) # Convert to array\n",
+ " img_array = np.expand_dims(img_array, axis=0) # Add batch dimension\n",
+ "\n",
+ " # Preprocess image for the respective model\n",
+ " if model_type == 'vgg16':\n",
+ " img_array = vgg16_preprocess(img_array) # Preprocess for VGG16\n",
+ " elif model_type == 'xception':\n",
+ " img_array = xception_preprocess(img_array) # Preprocess for Xception\n",
+ "\n",
+ " return img_array\n",
+ "\n",
+ "# Example usage\n",
+ "img_path = '/content/drive/MyDrive/Deep Learning Project/test1.png'\n",
+ "vgg16_img = preprocess_image(img_path, model_type='vgg16')\n",
+ "xception_img = preprocess_image(img_path, model_type='xception')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "KCvf5I9GJ4e7"
+ },
+ "outputs": [],
+ "source": [
+ "# Load and preprocess the input image for VGG16 (resize to 128x128)\n",
+ "def preprocess_image_vgg16(img_path):\n",
+ " img = image.load_img(img_path, target_size=(128, 128)) # Resize the image to 128x128\n",
+ " img_array = image.img_to_array(img) # Convert to array\n",
+ " img_array = np.expand_dims(img_array, axis=0) # Add batch dimension\n",
+ " img_array = vgg16_preprocess(img_array) # Preprocess for VGG16\n",
+ " return img_array\n",
+ "\n",
+ "# Example usage\n",
+ "vgg16_img = preprocess_image_vgg16(img_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WEN01gRMKfCn",
+ "outputId": "6fc457b8-bad6-4d32-c603-be4e521ad06e"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:5 out of the last 15 calls to .one_step_on_data_distributed at 0x7eafa83ae8e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
+ "Fire vs No Fire Prediction: 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict using VGG16\n",
+ "vgg16_pred = vgg16_model.predict(vgg16_img)\n",
+ "# Assuming the final layer is binary classification, we can apply sigmoid to the output\n",
+ "vgg16_pred_label = (vgg16_pred > 0.5).astype(int) # 0 = no fire, 1 = fire\n",
+ "print(f\"Fire vs No Fire Prediction: {vgg16_pred_label[0][0]}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nWwvnYw8Km9S"
+ },
+ "source": [
+ "Correct Prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "03Qrd1SQJ9g7",
+ "outputId": "1f464de8-c6a9-4371-e5de-087158122add"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step\n",
+ "Fire Severity Prediction: Mild\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict using Xception\n",
+ "xception_pred = xception_model.predict(xception_img)\n",
+ "# Since we have 3 classes (Mild, Moderate, Severe), use argmax to get the class index\n",
+ "xception_pred_label = np.argmax(xception_pred, axis=1)\n",
+ "severity_labels = ['Mild', 'Moderate', 'Severe']\n",
+ "print(f\"Fire Severity Prediction: {severity_labels[xception_pred_label[0]]}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cVduAeehKZpI"
+ },
+ "source": [
+ "Correct Prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "MhkDdlt3KWL0",
+ "outputId": "3e6d2317-5f13-4f62-d2cd-5cde4213139d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fire vs No Fire: Fire\n",
+ "Fire Severity: Mild\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"Fire vs No Fire: {'Fire' if vgg16_pred_label[0][0] == 1 else 'No Fire'}\")\n",
+ "print(f\"Fire Severity: {severity_labels[xception_pred_label[0]]}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "itJDUtI6T1mb"
+ },
+ "outputs": [],
+ "source": [
+ "def preprocess_image(img_path, model_type='vgg16'):\n",
+ " img = image.load_img(img_path, target_size=(224, 224) if model_type == 'vgg16' else (299, 299)) # Resize for model\n",
+ " img_array = image.img_to_array(img) # Convert to array\n",
+ " img_array = np.expand_dims(img_array, axis=0) # Add batch dimension\n",
+ "\n",
+ " # Preprocess image for the respective model\n",
+ " if model_type == 'vgg16':\n",
+ " img_array = vgg16_preprocess(img_array) # Preprocess for VGG16\n",
+ " elif model_type == 'xception':\n",
+ " img_array = xception_preprocess(img_array) # Preprocess for Xception\n",
+ "\n",
+ " return img_array"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "__________________________________________________________________________________________________________________________________"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Recomendation System:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1. BLIP: Bidirectional Language Image Prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6DvQJh-dKu5U",
+ "outputId": "7fa13dcc-d3e6-469c-dfc9-065e6d882f5d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
+ "Fire Status: Fire Detected\n",
+ "Fire Severity: Mild\n",
+ "Generated Recommendations: a wildfire was detected in the image. the fire severity is mild. based on this, provide a detailed recommendation for wildfire management strategies. focus on prevention, emergency response, containment efforts, and recovery strategies. in addition, suggest measures to minimize the environmental impact, protect human life, and restore affected areas. include guidelines for evacuation plans, resource allocation, and long - term environmental restoration efforts, especially considering the severity of the fire.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing import image\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "from transformers import BlipProcessor, BlipForConditionalGeneration\n",
+ "\n",
+ "# Load the BLIP model and processor\n",
+ "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
+ "blip_model = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
+ "\n",
+ "# Load pre-trained models\n",
+ "vgg16_model = load_model('/content/drive/MyDrive/Deep Learning Project/vgg16_best_model.keras')\n",
+ "xception_model = load_model('/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras')\n",
+ "\n",
+ "# Preprocessing functions\n",
+ "def preprocess_image(img_path, model_type='vgg16'):\n",
+ " if model_type == 'vgg16':\n",
+ " # Resize for VGG16 (128x128)\n",
+ " img = image.load_img(img_path, target_size=(128, 128))\n",
+ " elif model_type == 'xception':\n",
+ " # Resize for Xception (224x224)\n",
+ " img = image.load_img(img_path, target_size=(224, 224))\n",
+ "\n",
+ " img_array = image.img_to_array(img)\n",
+ " img_array = np.expand_dims(img_array, axis=0)\n",
+ "\n",
+ " # Preprocessing for the respective model\n",
+ " if model_type == 'vgg16':\n",
+ " from tensorflow.keras.applications.vgg16 import preprocess_input\n",
+ " img_array = preprocess_input(img_array)\n",
+ " elif model_type == 'xception':\n",
+ " from tensorflow.keras.applications.xception import preprocess_input\n",
+ " img_array = preprocess_input(img_array)\n",
+ "\n",
+ " return img_array\n",
+ "\n",
+ "# Fire detection function (VGG16)\n",
+ "def get_fire_status(image_path):\n",
+ " img = preprocess_image(image_path, model_type='vgg16')\n",
+ " predictions = vgg16_model.predict(img)\n",
+ " fire_status = 1 if predictions[0][0] > 0.5 else 0\n",
+ " fire_status_label = \"Fire Detected\" if fire_status == 1 else \"No Fire Detected\"\n",
+ " return fire_status, fire_status_label\n",
+ "\n",
+ "# Fire severity detection function (Xception)\n",
+ "def get_fire_severity(image_path):\n",
+ " img = preprocess_image(image_path, model_type='xception')\n",
+ " predictions = xception_model.predict(img)\n",
+ " severity_index = np.argmax(predictions[0])\n",
+ " severity_mapping = [\"Mild\", \"Moderate\", \"Severe\"]\n",
+ " severity = severity_mapping[severity_index]\n",
+ " return severity, severity_mapping[severity_index]\n",
+ "\n",
+ "# Generate detailed recommendations using BLIP\n",
+ "def generate_recommendations(image_path):\n",
+ " fire_status, fire_status_label = get_fire_status(image_path)\n",
+ " severity, severity_label = get_fire_severity(image_path)\n",
+ "\n",
+ " print(f\"Fire Status: {fire_status_label}\")\n",
+ " print(f\"Fire Severity: {severity_label}\")\n",
+ "\n",
+ " # More detailed input to BLIP to ensure longer, actionable recommendations\n",
+ " input_text = f\"A wildfire was detected in the image. The fire severity is {severity_label}. \" \\\n",
+ " f\"Based on this, provide a detailed recommendation for wildfire management strategies. \" \\\n",
+ " f\"Focus on prevention, emergency response, containment efforts, and recovery strategies. \" \\\n",
+ " f\"In addition, suggest measures to minimize the environmental impact, protect human life, \" \\\n",
+ " f\"and restore affected areas. Include guidelines for evacuation plans, resource allocation, \" \\\n",
+ " f\"and long-term environmental restoration efforts, especially considering the severity of the fire.\"\n",
+ "\n",
+ " # Load the image for BLIP\n",
+ " image = Image.open(image_path).convert(\"RGB\")\n",
+ " inputs = processor(images=image, text=input_text, return_tensors=\"pt\")\n",
+ "\n",
+ " out = blip_model.generate(**inputs)\n",
+ "\n",
+ " recommendations = processor.decode(out[0], skip_special_tokens=True)\n",
+ " return recommendations\n",
+ "\n",
+ "# Example usage\n",
+ "image_path = \"/content/drive/MyDrive/Deep Learning Project/test1.png\"\n",
+ "recommendations = generate_recommendations(image_path)\n",
+ "print(f\"Generated Recommendations: {recommendations}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vcL1kWBzbEWa",
+ "outputId": "eac74ec8-9a58-42c2-c14f-0870c9d5fa6f"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+ "\n",
+ "Starting analysis pipeline...\n",
+ "\n",
+ "Checking image path...\n",
+ "Image found at: /content/drive/MyDrive/Deep Learning Project/test1.png\n",
+ "Initializing models... This may take a few minutes.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Config of the encoder: is overwritten by shared encoder config: ViTConfig {\n",
+ " \"architectures\": [\n",
+ " \"ViTModel\"\n",
+ " ],\n",
+ " \"attention_probs_dropout_prob\": 0.0,\n",
+ " \"encoder_stride\": 16,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout_prob\": 0.0,\n",
+ " \"hidden_size\": 768,\n",
+ " \"image_size\": 224,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 3072,\n",
+ " \"layer_norm_eps\": 1e-12,\n",
+ " \"model_type\": \"vit\",\n",
+ " \"num_attention_heads\": 12,\n",
+ " \"num_channels\": 3,\n",
+ " \"num_hidden_layers\": 12,\n",
+ " \"patch_size\": 16,\n",
+ " \"qkv_bias\": true,\n",
+ " \"transformers_version\": \"4.48.0\"\n",
+ "}\n",
+ "\n",
+ "Config of the decoder: is overwritten by shared decoder config: GPT2Config {\n",
+ " \"activation_function\": \"gelu_new\",\n",
+ " \"add_cross_attention\": true,\n",
+ " \"architectures\": [\n",
+ " \"GPT2LMHeadModel\"\n",
+ " ],\n",
+ " \"attn_pdrop\": 0.1,\n",
+ " \"bos_token_id\": 50256,\n",
+ " \"decoder_start_token_id\": 50256,\n",
+ " \"embd_pdrop\": 0.1,\n",
+ " \"eos_token_id\": 50256,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"is_decoder\": true,\n",
+ " \"layer_norm_epsilon\": 1e-05,\n",
+ " \"model_type\": \"gpt2\",\n",
+ " \"n_ctx\": 1024,\n",
+ " \"n_embd\": 768,\n",
+ " \"n_head\": 12,\n",
+ " \"n_inner\": null,\n",
+ " \"n_layer\": 12,\n",
+ " \"n_positions\": 1024,\n",
+ " \"pad_token_id\": 50256,\n",
+ " \"reorder_and_upcast_attn\": false,\n",
+ " \"resid_pdrop\": 0.1,\n",
+ " \"scale_attn_by_inverse_layer_idx\": false,\n",
+ " \"scale_attn_weights\": true,\n",
+ " \"summary_activation\": null,\n",
+ " \"summary_first_dropout\": 0.1,\n",
+ " \"summary_proj_to_labels\": true,\n",
+ " \"summary_type\": \"cls_index\",\n",
+ " \"summary_use_proj\": true,\n",
+ " \"task_specific_params\": {\n",
+ " \"text-generation\": {\n",
+ " \"do_sample\": true,\n",
+ " \"max_length\": 50\n",
+ " }\n",
+ " },\n",
+ " \"transformers_version\": \"4.48.0\",\n",
+ " \"use_cache\": true,\n",
+ " \"vocab_size\": 50257\n",
+ "}\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model loaded successfully.\n",
+ "Using device: cpu\n",
+ "\n",
+ "Starting image analysis...\n",
+ "\n",
+ "Analysis Results:\n",
+ "--------------------------------------------------\n",
+ "Image Description: a fire hydrant in the middle of a forest \n",
+ "Recommendations:\n",
+ "1. Contact emergency services immediately.\n",
+ "2. If safe, document the fire's location and spread.\n",
+ "3. Follow evacuation orders if given.\n",
+ "4. Monitor local emergency broadcasts.\n",
+ "5. Evacuate if you are in a dangerous area.\n",
+ "--------------------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer\n",
+ "from PIL import Image\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "\n",
+ "class ColabWildfireAnalyzer:\n",
+ " def __init__(self):\n",
+ " print(\"Initializing models... This may take a few minutes.\")\n",
+ "\n",
+ " # Use ViT-GPT2 model which is more stable\n",
+ " model_name = \"nlpconnect/vit-gpt2-image-captioning\"\n",
+ "\n",
+ " try:\n",
+ " self.model = VisionEncoderDecoderModel.from_pretrained(model_name)\n",
+ " self.image_processor = ViTImageProcessor.from_pretrained(model_name)\n",
+ " self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "\n",
+ " # Move model to appropriate device\n",
+ " self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ " self.model.to(self.device)\n",
+ "\n",
+ " print(\"Model loaded successfully.\")\n",
+ " print(f\"Using device: {self.device}\")\n",
+ "\n",
+ " except Exception as e:\n",
+ " print(f\"Error initializing model: {str(e)}\")\n",
+ " raise\n",
+ "\n",
+ " def analyze_image(self, image_path: str) -> str:\n",
+ " \"\"\"Analyze image and generate description along with recommendations.\"\"\"\n",
+ " try:\n",
+ " # Load and process image\n",
+ " image = Image.open(image_path).convert('RGB')\n",
+ "\n",
+ " # Process image\n",
+ " pixel_values = self.image_processor(image, return_tensors=\"pt\").pixel_values.to(self.device)\n",
+ "\n",
+ " # Generate output (only need the token IDs)\n",
+ " with torch.no_grad():\n",
+ " output_ids = self.model.generate(\n",
+ " pixel_values,\n",
+ " max_length=100,\n",
+ " num_beams=4,\n",
+ " temperature=0.8,\n",
+ " top_k=50,\n",
+ " top_p=0.95,\n",
+ " repetition_penalty=1.0,\n",
+ " length_penalty=1.0,\n",
+ " early_stopping=True\n",
+ " )\n",
+ "\n",
+ " # Decode the generated output (this should be the description)\n",
+ " description = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
+ "\n",
+ " # Now, generate detailed recommendations based on the description\n",
+ " recommendations = self.generate_recommendations(description)\n",
+ "\n",
+ " return description, recommendations\n",
+ "\n",
+ " except Exception as e:\n",
+ " print(f\"Detailed error in analyze_image: {str(e)}\")\n",
+ " return f\"Error analyzing image: {str(e)}\", []\n",
+ "\n",
+ " def generate_recommendations(self, description: str) -> str:\n",
+ " \"\"\"Generate dynamic fire risk recommendations based on the description.\"\"\"\n",
+ "\n",
+ " # If \"fire\" is in the description, return fire-specific recommendations\n",
+ " if 'fire' in description.lower():\n",
+ " return (\n",
+ " \"1. Contact emergency services immediately.\\n\"\n",
+ " \"2. If safe, document the fire's location and spread.\\n\"\n",
+ " \"3. Follow evacuation orders if given.\\n\"\n",
+ " \"4. Monitor local emergency broadcasts.\\n\"\n",
+ " \"5. Evacuate if you are in a dangerous area.\"\n",
+ " )\n",
+ " else:\n",
+ " return \"No fire detected. Continue monitoring the area for safety.\"\n",
+ "\n",
+ "def run_analysis(image_path: str):\n",
+ " \"\"\"Run the complete analysis pipeline.\"\"\"\n",
+ " try:\n",
+ " print(\"\\nChecking image path...\")\n",
+ " import os\n",
+ " if not os.path.exists(image_path):\n",
+ " raise FileNotFoundError(f\"Image not found at path: {image_path}\")\n",
+ "\n",
+ " print(f\"Image found at: {image_path}\")\n",
+ "\n",
+ " analyzer = ColabWildfireAnalyzer()\n",
+ " print(\"\\nStarting image analysis...\")\n",
+ "\n",
+ " # Get image description and dynamic recommendations\n",
+ " description, recommendations = analyzer.analyze_image(image_path)\n",
+ "\n",
+ " print(\"\\nAnalysis Results:\")\n",
+ " print(\"-\" * 50)\n",
+ " print(f\"Image Description: {description}\")\n",
+ " print(f\"Recommendations:\\n{recommendations}\")\n",
+ " print(\"-\" * 50)\n",
+ "\n",
+ " except Exception as e:\n",
+ " print(f\"Error in run_analysis: {str(e)}\")\n",
+ "\n",
+ "# Example usage\n",
+ "IMAGE_PATH = \"/content/drive/MyDrive/Deep Learning Project/test1.png\"\n",
+ "\n",
+ "# Verify Google Drive is mounted\n",
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')\n",
+ "\n",
+ "# Run the analysis\n",
+ "print(\"\\nStarting analysis pipeline...\")\n",
+ "run_analysis(IMAGE_PATH)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "k79uu1QkjlzQ"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import backend as K\n",
+ "\n",
+ "# Define focal loss\n",
+ "def focal_loss_fixed(y_true, y_pred, gamma=2.0, alpha=0.25):\n",
+ " \"\"\"\n",
+ " Focal Loss for multi-class classification.\n",
+ " Args:\n",
+ " y_true: True labels (one-hot encoded).\n",
+ " y_pred: Predicted labels (softmax outputs).\n",
+ " gamma: Focusing parameter (default=2.0).\n",
+ " alpha: Balancing parameter (default=0.25).\n",
+ " Returns:\n",
+ " Computed focal loss value.\n",
+ " \"\"\"\n",
+ " y_true = tf.convert_to_tensor(y_true, dtype=tf.float32)\n",
+ " y_pred = tf.convert_to_tensor(y_pred, dtype=tf.float32)\n",
+ "\n",
+ " # Clip predictions to avoid log(0)\n",
+ " y_pred = K.clip(y_pred, K.epsilon(), 1 - K.epsilon())\n",
+ "\n",
+ " # Compute focal loss\n",
+ " cross_entropy = -y_true * K.log(y_pred)\n",
+ " weight = alpha * y_true * K.pow((1 - y_pred), gamma)\n",
+ " loss = weight * cross_entropy\n",
+ " return K.sum(loss, axis=-1)\n",
+ "\n",
+ "# Register custom loss\n",
+ "tf.keras.utils.get_custom_objects().update({\"focal_loss_fixed\": focal_loss_fixed})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PbKguso5ZT4A"
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import load_model\n",
+ "import numpy as np\n",
+ "from tensorflow.keras.preprocessing import image\n",
+ "from PIL import Image\n",
+ "\n",
+ "# Load VGG16 and Xception models\n",
+ "vgg16_model = load_model('/content/drive/MyDrive/Deep Learning Project/vgg16_best_model.keras')\n",
+ "xception_model = load_model('/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras')\n",
+ "\n",
+ "# Preprocessing functions for VGG16 and Xception\n",
+ "def preprocess_image(image_path, model_type):\n",
+ " img = Image.open(image_path).convert(\"RGB\")\n",
+ "\n",
+ " if model_type == 'vgg16':\n",
+ " img = img.resize((128, 128)) # VGG16 expects 128x128 images\n",
+ " elif model_type == 'xception':\n",
+ " img = img.resize((224, 224)) # Xception expects 224x224 images\n",
+ "\n",
+ " img_array = np.array(img) / 255.0 # Normalize the image\n",
+ " img_array = np.expand_dims(img_array, axis=0) # Add batch dimension\n",
+ " return img_array\n",
+ "\n",
+ "# Fire detection function (VGG16)\n",
+ "def get_fire_status(image_path):\n",
+ " img = preprocess_image(image_path, model_type='vgg16')\n",
+ " predictions = vgg16_model.predict(img)\n",
+ " fire_status = 1 if predictions[0][0] > 0.5 else 0\n",
+ " fire_status_label = \"Fire Detected\" if fire_status == 1 else \"No Fire Detected\"\n",
+ " return fire_status, fire_status_label\n",
+ "\n",
+ "# Fire severity function (Xception)\n",
+ "def get_fire_severity(image_path):\n",
+ " img = preprocess_image(image_path, model_type='xception')\n",
+ " predictions = xception_model.predict(img)\n",
+ "\n",
+ " severity_index = np.argmax(predictions[0])\n",
+ " severity_mapping = [\"Mild\", \"Moderate\", \"Severe\"]\n",
+ " severity = severity_mapping[severity_index]\n",
+ " return severity, severity_mapping[severity_index]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JAStqHmVkDjF",
+ "outputId": "6d15744a-7436-4759-f2da-f99d8b7fb3b8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
+ " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
+ " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
+ "\n",
+ " To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
+ "Enter your token (input will not be visible): \n",
+ "Add token as git credential? (Y/n) Y\n",
+ "Token is valid (permission: fineGrained).\n",
+ "The token `DL Recommendations` has been saved to /root/.cache/huggingface/stored_tokens\n",
+ "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
+ "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
+ "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
+ "\n",
+ "git config --global credential.helper store\n",
+ "\n",
+ "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
+ "Token has not been saved to git credential helper.\n",
+ "Your token has been saved to /root/.cache/huggingface/token\n",
+ "Login successful.\n",
+ "The current active token is: `DL Recommendations`\n"
+ ]
+ }
+ ],
+ "source": [
+ "!huggingface-cli login"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2. GPT-2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "yil-yEoSjQbC",
+ "outputId": "28fbfa1f-3c5a-4273-cc2a-d3b4991b2c3f"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated Recommendations:\n",
+ "\n",
+ " Fire Status: Fire Detected\n",
+ " Fire Severity: Mild\n",
+ " Image Path: /content/drive/MyDrive/Deep Learning Project/test1.png\n",
+ "\n",
+ " Generate detailed recommendations for wildfire management based on the above information:\n",
+ " - Focus on emergency response actions during the fire (e.g., evacuation, firefighting efforts).\n",
+ " - Suggest strategies for containment, such as firebreaks or aerial firefighting.\n",
+ " - Provide clear steps for recovery efforts after the fire, considering both human and environmental impacts.\n",
+ " - Include specific advice for local communities and authorities on how to handle the aftermath.\n",
+ " - Recommend ways to mitigate future wildfire risks, such as forest management practices and community preparedness.\n",
+ " - Ensure the response is comprehensive, detailed, and exceeds 200 words.\n",
+ " Choose a minimum number of pages per fire and include a description of the impact and size of the fire.\n",
+ "2. Identify the key characteristics of the wildfire and identify the key questions:\n",
+ "- How long can the fire burn?\n",
+ "- Are there any significant factors preventing the fire from spreading?\n",
+ " - What is the size of debris? What is its location and where it is located?\n",
+ "A. A significant number of the above factors can provide clues as to the fire's trajectory.\n",
+ "3. A small number of people can be found inside the fire and there is a high likelihood that the fire may burn in the night. The fire can spread in the open air and it is likely that people are going to be inside the house. This is important as it means that most of the people inside will not be able to see the fire coming, and the people who are most likely to be at risk of being burned will not survive. This means that if you and your family do not want to leave the house before it burns out, then you need to move into a new home and stay outside. If you are staying with family and friends, you may want to move out of the house as it is less likely that you will be exposed to the flames. If not moving out of your home, then consider moving to a new house or having your home remodeled to get a better view of the area. A recent survey conducted by the Firefighters' Council of Greater Vancouver suggested that an average of 40% of home owners in Vancouver have a fire plan that includes a fire prevention plan. The following are examples of some of the factors that could help to keep these residents from becoming victims of wildfires: a) the type of fire. In some cases, there is simply not enough fire to sustain life in the home and there may be no fire that will burn for more than a few hours. The type of house is also important in determining the fire risks, and this is important for the survival of the residents. The types of fire are also important, because they determine whether or not the individual is exposed to potential hazards, such that it is impossible to know what type of fires will occur when the fire goes out. \n",
+ "- the severity of the injury or fire. (In some instances, it may be possible for people to be trapped in the house while it is burning or even in the process of burning) This is a very important factor that can help to protect you from the fire if you decide to move in and leave the home.\n",
+ "- whether or no fire should be allowed in the area where you live. (This is important to help people living in the community stay safe) \n",
+ " - the length of time the fire will last. . \n",
+ "4. What is a typical day to day occurrence of a wildfire? Each wildfire is different and a general rule to follow is that in an emergency, a fire will be very\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
+ "import torch\n",
+ "\n",
+ "# Load GPT-2 model and tokenizer\n",
+ "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
+ "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n",
+ "\n",
+ "# Set pad_token_id to eos_token_id to avoid warnings\n",
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "\n",
+ "# Generate recommendations using GPT-2 model\n",
+ "def generate_recommendations(fire_status, severity_label, image_path):\n",
+ " input_prompt = f\"\"\"\n",
+ " Fire Status: {fire_status}\n",
+ " Fire Severity: {severity_label}\n",
+ " Image Path: {image_path}\n",
+ "\n",
+ " Generate detailed recommendations for wildfire management based on the above information:\n",
+ " - Focus on emergency response actions during the fire (e.g., evacuation, firefighting efforts).\n",
+ " - Suggest strategies for containment, such as firebreaks or aerial firefighting.\n",
+ " - Provide clear steps for recovery efforts after the fire, considering both human and environmental impacts.\n",
+ " - Include specific advice for local communities and authorities on how to handle the aftermath.\n",
+ " - Recommend ways to mitigate future wildfire risks, such as forest management practices and community preparedness.\n",
+ " - Ensure the response is comprehensive, detailed, and exceeds 200 words.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Tokenize input prompt with attention mask\n",
+ " inputs = tokenizer(input_prompt, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n",
+ "\n",
+ " # Generate response from GPT-2 with sampling\n",
+ " output = model.generate(\n",
+ " inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " max_length=800,\n",
+ " num_return_sequences=1,\n",
+ " do_sample=True, # Enable sampling for diversity\n",
+ " top_p=0.95, # Nucleus sampling\n",
+ " top_k=50, # Top-k sampling\n",
+ " temperature=0.8, # Add randomness\n",
+ " no_repeat_ngram_size=3, # Avoid repeating trigrams\n",
+ " pad_token_id=tokenizer.eos_token_id # Handle padding\n",
+ " )\n",
+ "\n",
+ " # Decode the generated response\n",
+ " recommendations = tokenizer.decode(output[0], skip_special_tokens=True)\n",
+ "\n",
+ " return recommendations\n",
+ "\n",
+ "# Example usage\n",
+ "image_path = \"/content/drive/MyDrive/Deep Learning Project/test1.png\" # Replace with actual image path\n",
+ "\n",
+ "# Placeholder functions to simulate fire status and severity\n",
+ "def get_fire_status(image_path):\n",
+ " return \"Fire Detected\", \"Fire Detected\"\n",
+ "\n",
+ "def get_fire_severity(image_path):\n",
+ " return \"Mild\", \"Mild\"\n",
+ "\n",
+ "# Get fire status and severity from the models\n",
+ "fire_status, fire_status_label = get_fire_status(image_path)\n",
+ "severity, severity_label = get_fire_severity(image_path)\n",
+ "\n",
+ "# Generate long-form recommendations using GPT-2\n",
+ "recommendations = generate_recommendations(fire_status_label, severity_label, image_path)\n",
+ "\n",
+ "# Print the recommendations\n",
+ "print(f\"Generated Recommendations:\\n{recommendations}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kvKK1AkcjyIs",
+ "outputId": "95c7fe6a-4573-48d2-84e6-c55df7808c17"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generated Recommendations:\n",
+ "\n",
+ " Fire Status: Fire Detected\n",
+ " Fire Severity: Moderate\n",
+ " Image Path: /content/drive/MyDrive/Deep Learning Project/test1.png\n",
+ "\n",
+ " Generate wildfire management recommendations tailored to Moderate severity. Structure the response as follows:\n",
+ "\n",
+ " ## Wildfire Management Recommendations for Moderate Severity\n",
+ "\n",
+ " ### 1. Emergency Measures\n",
+ " * **Immediate Response:** Describe how emergency responders should act immediately.\n",
+ " * **Evacuation:** Provide specific evacuation guidance for communities.\n",
+ " * **Structure Protection:** Suggest methods to protect infrastructure and homes.\n",
+ " * **Communication:** Explain how to establish effective communication channels.\n",
+ "\n",
+ " ### 2. Containment Strategies\n",
+ " * **Fireline Construction:** Detail methods for creating firebreaks.\n",
+ " * **Aerial Support:** Explain how to use aerial firefighting effectively.\n",
+ " * **Controlled Burning:** Discuss the role of prescribed burns.\n",
+ " * **Weather Monitoring:** Describe how weather data can be used to anticipate fire behavior.\n",
+ "\n",
+ " ### 3. Ecosystem Restoration\n",
+ " * **Reforestation:** Recommend strategies for replanting native trees.\n",
+ " * **Reseeding:** Provide steps for stabilizing soil with native grasses and wildflowers.\n",
+ " * **Erosion Control:** Describe measures to prevent soil loss.\n",
+ " * **Wildlife Habitat Restoration:** Suggest ways to support wildlife recovery.\n",
+ "\n",
+ " ### 4. Community Support and Long-Term Prevention\n",
+ " * **Community Education:** Propose educational initiatives for fire safety and prevention.\n",
+ " * **Fuel Reduction:** Explain how to create defensible spaces around homes.\n",
+ " * **Early Detection Systems:** Recommend technologies for early fire detection.\n",
+ " * **Prescribed Burning Programs:** Outline a plan for reducing fuel loads.\n",
+ "\n",
+ " Ensure the response is detailed, exceeds 300 words, and avoids generic or fabricated content.\n",
+ " 6. Design a fire prevention plan that includes all the steps required to mitigate the risk of fire.\n",
+ " 7. Provide a plan to mitigate fire hazards.\n",
+ " 8. Establish a fire risk assessment for the community.\n",
+ " 9. Provide the fire prevention program's operational plan.\n",
+ " 10. Determine how to manage the community and its resources.\n",
+ " 11. Determit and plan for future fire risk assessments.\n",
+ " 12. Develop an appropriate plan to meet the community's fire risk needs.\n",
+ " 13. Develop and implement a plan that identifies the appropriate resources.\n",
+ "\n",
+ "\n",
+ "What is the community fire risk plan?\n",
+ "\n",
+ "The community fire hazard plan is a comprehensive set of resources that is designed to address the community risk of wildfire, including:\n",
+ "\n",
+ "Fire risk management\n",
+ "\n",
+ "Emergency response\n",
+ "\n",
+ "Land and property management\n",
+ ".\n",
+ "\n",
+ "What are the fire risk management plans?\n",
+ " \"Fire risk\" is defined as the risk that a community will lose its ability to protect its property or its people from fires.\n",
+ ". \"Land and Property Management\" is a definition of the term that includes the following:\n",
+ ". . .\n",
+ "\n",
+ ". .\" \"\n",
+ "\n",
+ "\"Fire risk risk management\" refers to the risk the community will face if a community's property or resources are damaged or destroyed, and is defined in terms of a community fire that is not a wildfire.\n",
+ "-\n",
+ "\n",
+ "-\n",
+ "\n",
+ "\n",
+ "The fire risk policy is a set of steps that are needed to minimize the risk to the community of the loss of its property.\n",
+ " (See Fire Risk Management Plan for the Fire Risk Planning Guide.)\n",
+ "\n",
+ "How are the community fires handled?\n",
+ ".\n",
+ "\n",
+ ".\n",
+ "\n",
+ "\n",
+ "\n",
+ ". -\n",
+ "\n",
+ "In the case of a fire, the fire plan is designed with the following elements in mind:\n",
+ "\n",
+ "\n",
+ ". -\n",
+ "\n",
+ "\n",
+ "1. First, the community is responsible for managing the fire. Second, it is the responsibility of the community to provide firefighting services to the area that is affected by the fire, including through the use of fire suppression devices.\n",
+ "\n",
+ ", and , and , if the fire is in the immediate vicinity of a building, it will be responsible for providing firefighting assistance to the areas that are affected by that fire.\n",
+ "\n",
+ "\n",
+ "\n",
+ ", as well as the community, it must have the following resources to handle the fire:\n",
+ "- -\n",
+ "\n",
+ ". .\n",
+ "\n",
+ "\n",
+ "2. The community is the only entity that is responsible to provide emergency firefighting, and the only community that is able to provide it is, or\n",
+ "\n",
+ ", the only public entity that can provide fire suppression services to that community.\n",
+ "\n",
+ ":\n",
+ "\n",
+ "\n",
+ "- .\n",
+ "\n",
+ " (If the community has no firefighting\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
+ "import torch\n",
+ "\n",
+ "# Load GPT-2 model and tokenizer\n",
+ "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
+ "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n",
+ "\n",
+ "# Set pad_token_id to eos_token_id to avoid warnings\n",
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "\n",
+ "# Generate recommendations using GPT-2 model\n",
+ "def generate_recommendations(fire_status, severity_label, image_path):\n",
+ " # Enhanced prompt with detailed structure\n",
+ " input_prompt = f\"\"\"\n",
+ " Fire Status: {fire_status}\n",
+ " Fire Severity: {severity_label}\n",
+ " Image Path: {image_path}\n",
+ "\n",
+ " Generate wildfire management recommendations tailored to {severity_label} severity. Structure the response as follows:\n",
+ "\n",
+ " ## Wildfire Management Recommendations for {severity_label} Severity\n",
+ "\n",
+ " ### 1. Emergency Measures\n",
+ " * **Immediate Response:** Describe how emergency responders should act immediately.\n",
+ " * **Evacuation:** Provide specific evacuation guidance for communities.\n",
+ " * **Structure Protection:** Suggest methods to protect infrastructure and homes.\n",
+ " * **Communication:** Explain how to establish effective communication channels.\n",
+ "\n",
+ " ### 2. Containment Strategies\n",
+ " * **Fireline Construction:** Detail methods for creating firebreaks.\n",
+ " * **Aerial Support:** Explain how to use aerial firefighting effectively.\n",
+ " * **Controlled Burning:** Discuss the role of prescribed burns.\n",
+ " * **Weather Monitoring:** Describe how weather data can be used to anticipate fire behavior.\n",
+ "\n",
+ " ### 3. Ecosystem Restoration\n",
+ " * **Reforestation:** Recommend strategies for replanting native trees.\n",
+ " * **Reseeding:** Provide steps for stabilizing soil with native grasses and wildflowers.\n",
+ " * **Erosion Control:** Describe measures to prevent soil loss.\n",
+ " * **Wildlife Habitat Restoration:** Suggest ways to support wildlife recovery.\n",
+ "\n",
+ " ### 4. Community Support and Long-Term Prevention\n",
+ " * **Community Education:** Propose educational initiatives for fire safety and prevention.\n",
+ " * **Fuel Reduction:** Explain how to create defensible spaces around homes.\n",
+ " * **Early Detection Systems:** Recommend technologies for early fire detection.\n",
+ " * **Prescribed Burning Programs:** Outline a plan for reducing fuel loads.\n",
+ "\n",
+ " Ensure the response is detailed, exceeds 300 words, and avoids generic or fabricated content.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Tokenize input prompt with attention mask\n",
+ " inputs = tokenizer(input_prompt, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n",
+ "\n",
+ " # Generate response from GPT-2 with enhanced sampling\n",
+ " output = model.generate(\n",
+ " inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " max_length=1000, # Increased for longer output\n",
+ " num_return_sequences=1,\n",
+ " do_sample=True, # Enable sampling for diversity\n",
+ " top_p=0.9, # Nucleus sampling\n",
+ " top_k=50, # Top-k sampling\n",
+ " temperature=0.7, # Balance randomness with coherence\n",
+ " no_repeat_ngram_size=3, # Avoid repeating trigrams\n",
+ " pad_token_id=tokenizer.eos_token_id # Handle padding\n",
+ " )\n",
+ "\n",
+ " # Decode the generated response\n",
+ " recommendations = tokenizer.decode(output[0], skip_special_tokens=True)\n",
+ "\n",
+ " return recommendations\n",
+ "\n",
+ "# Example usage\n",
+ "image_path = \"/content/drive/MyDrive/Deep Learning Project/test1.png\" # Replace with actual image path\n",
+ "\n",
+ "# Placeholder functions to simulate fire status and severity\n",
+ "def get_fire_status(image_path):\n",
+ " return \"Fire Detected\", \"Fire Detected\"\n",
+ "\n",
+ "def get_fire_severity(image_path):\n",
+ " return \"Moderate\", \"Moderate\"\n",
+ "\n",
+ "# Get fire status and severity from the models\n",
+ "fire_status, fire_status_label = get_fire_status(image_path)\n",
+ "severity, severity_label = get_fire_severity(image_path)\n",
+ "\n",
+ "# Generate long-form recommendations using GPT-2\n",
+ "recommendations = generate_recommendations(fire_status_label, severity_label, image_path)\n",
+ "\n",
+ "# Print the recommendations\n",
+ "print(f\"Generated Recommendations:\\n{recommendations}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3. Gemini"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vIiM3X58uTGh"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q -U google-generativeai"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ZdM_J_OFuwtZ"
+ },
+ "outputs": [],
+ "source": [
+ "# Import the Python SDK\n",
+ "import google.generativeai as genai\n",
+ "# Used to securely store your API key\n",
+ "from google.colab import userdata\n",
+ "\n",
+ "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+ "genai.configure(api_key=GOOGLE_API_KEY)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "U6aEQABqyv68"
+ },
+ "outputs": [],
+ "source": [
+ "flash = genai.GenerativeModel('gemini-1.5-flash')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 749
+ },
+ "id": "L9xfW0HO0RiY",
+ "outputId": "47e2dcfd-63b8-4162-8821-c976d7d3e59b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8s/step\n",
+ "## Mild Wildfire Management Plan: Expert Recommendations\n",
+ "\n",
+ "A \"mild\" wildfire designation implies a relatively slow-moving fire with limited acreage and minimal immediate threat to life and property. However, complacency is dangerous; even mild fires can escalate rapidly under the right conditions. This plan prioritizes swift, efficient containment while emphasizing long-term prevention and ecosystem restoration.\n",
+ "\n",
+ "**1. Immediate Actions to Control the Fire (Emergency Response):**\n",
+ "\n",
+ "* **Initial Assessment:** A rapid, expert assessment is crucial. This involves determining fire perimeter, fuel type, wind speed & direction, topography, and potential spread rate. Utilize aerial reconnaissance (drone or aircraft) if feasible for better situational awareness.\n",
+ "* **Direct Attack:** For mild fires, a direct attack is often effective. This involves deploying firefighters with hand tools (shovels, rakes, pulaskis) to create firebreaks and directly extinguish flames. Focus on removing fuel ahead of the fire's leading edge. Water tankers and smaller engine crews can provide supplemental support.\n",
+ "* **Indirect Attack (if necessary):** If the fire is approaching structures or valuable resources, an indirect attack may be necessary. This involves creating containment lines *outside* the fire perimeter to prevent its spread, allowing firefighters to work safely at a distance. Backburns, under controlled conditions, may be used to consume fuel in the path of the fire.\n",
+ "* **Communication:** Establish clear communication channels between all personnel, including incident command, firefighters, and support crews. Regular updates are critical.\n",
+ "* **Resource Mobilization:** While the fire is mild, rapid response prevents escalation. Ensure adequate personnel, equipment, and water resources are available and quickly deployed.\n",
+ "\n",
+ "\n",
+ "**2. Evacuation Plans to Protect Life and Property:**\n",
+ "\n",
+ "* **Targeted Evacuations:** For mild fires, evacuations are typically not widespread. However, a pre-emptive plan is essential for areas immediately threatened. This should include defining evacuation zones based on real-time fire behavior and wind patterns.\n",
+ "* **Alert System:** Utilize a multi-channel alert system (text alerts, sirens, radio announcements) to quickly inform residents in threatened areas. Clear, concise messaging is paramount.\n",
+ "* **Designated Evacuation Routes:** Establish clear, well-maintained evacuation routes with signage. Regular maintenance and inspections of these routes are essential.\n",
+ "* **Emergency Shelters:** Pre-identify and prepare emergency shelters with provisions for displaced residents. Consider pet-friendly options.\n",
+ "* **Evacuation Assistance:** Arrange for assistance for vulnerable populations (elderly, disabled, those without transportation).\n",
+ "\n",
+ "**3. Strategies to Restore the Ecosystem After the Fire:**\n",
+ "\n",
+ "* **Monitoring and Assessment:** Post-fire monitoring is vital. Assess the extent of damage to vegetation, soil, and water resources. Identify areas at risk of erosion or flooding.\n",
+ "* **Erosion Control:** Implement erosion control measures to prevent soil loss. This might include seeding, mulching, or installing temporary barriers.\n",
+ "* **Revegetation:** Depending on the severity of the burn, natural regeneration may be sufficient. However, in areas with significant damage, replanting native species may be necessary to accelerate ecosystem recovery and prevent invasive species.\n",
+ "* **Water Resource Management:** Assess the impact of the fire on water quality and quantity. Implement measures to prevent water contamination and erosion into streams and rivers.\n",
+ "* **Wildlife Monitoring:** Monitor wildlife populations to assess the impact of the fire and implement appropriate conservation strategies if needed.\n",
+ "\n",
+ "**4. Awareness and Education Programs for the Community to Prevent Future Fires:**\n",
+ "\n",
+ "* **Community Fire Safety Workshops:** Organize workshops to educate the community on fire prevention, safe campfire practices, and the use of power tools during dry conditions.\n",
+ "* **Public Service Announcements:** Utilize local media to disseminate fire safety information. Highlight the importance of clearing defensible space around homes and businesses.\n",
+ "* **Fuel Management Programs:** Promote community-based fuel reduction projects, such as controlled burns (under expert supervision) and vegetation thinning.\n",
+ "* **Early Warning Systems:** Improve the early warning system through weather monitoring, fire detection technology, and community watch programs.\n",
+ "* **Enforcement of Fire Regulations:** Strict enforcement of fire restrictions and regulations during high-risk periods is essential.\n",
+ "\n",
+ "\n",
+ "**Important Note:** This plan provides a framework. The specific actions will vary depending on the exact characteristics of the mild wildfire, local resources, and environmental conditions. Expert advice from experienced wildfire management professionals is crucial throughout the entire process. Never underestimate the potential for a mild fire to escalate. Proactive and well-coordinated efforts are key to successful management.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import google.generativeai as genai\n",
+ "import numpy as np\n",
+ "from IPython.display import HTML, Markdown, display\n",
+ "from keras.models import load_model\n",
+ "from tensorflow.keras.preprocessing import image\n",
+ "from PIL import Image\n",
+ "\n",
+ "# Load the Xception model\n",
+ "xception_model = load_model('/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras')\n",
+ "\n",
+ "# Preprocessing function for Xception\n",
+ "def preprocess_image(image_path, model_type='xception'):\n",
+ " img = Image.open(image_path).convert(\"RGB\")\n",
+ " img = img.resize((224, 224)) # Xception expects 224x224 images\n",
+ " img_array = np.array(img) / 255.0 # Normalize the image\n",
+ " img_array = np.expand_dims(img_array, axis=0) # Add batch dimension\n",
+ " return img_array\n",
+ "\n",
+ "# Fire severity function (Xception)\n",
+ "def get_fire_severity(image_path):\n",
+ " img = preprocess_image(image_path, model_type='xception')\n",
+ " predictions = xception_model.predict(img)\n",
+ " severity_index = np.argmax(predictions[0])\n",
+ " severity_mapping = [\"Mild\", \"Moderate\", \"Severe\"]\n",
+ " return severity_mapping[severity_index]\n",
+ "\n",
+ "# Define the function to generate wildfire management recommendations\n",
+ "def generate_wildfire_recommendations(severity_level):\n",
+ " # Dynamically insert the severity level into the prompt\n",
+ " prompt = f\"\"\"\n",
+ " You are an expert in wildfire management. The fire severity is {severity_level}.\n",
+ " Please provide specific recommendations for emergency response,\n",
+ " fire containment, and long-term prevention steps for a {severity_level.lower()} wildfire.\n",
+ " These should include:\n",
+ " 1. Immediate actions to control the fire.\n",
+ " 2. Evacuation plans to protect life and property.\n",
+ " 3. Strategies to restore the ecosystem after the fire.\n",
+ " 4. Awareness and education programs for the community to prevent future fires.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Use the Gemini model to generate recommendations\n",
+ " response = flash.generate_content(prompt)\n",
+ "\n",
+ " return response.text\n",
+ "\n",
+ "# Example Usage with Image\n",
+ "image_path = \"/content/drive/MyDrive/Deep Learning Project/test1.png\" # Replace with the actual image path\n",
+ "severity = get_fire_severity(image_path) # Get severity dynamically from the model\n",
+ "recommendations = generate_wildfire_recommendations(severity)\n",
+ "\n",
+ "print(recommendations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dGxqeR1N0T_B",
+ "outputId": "00638a91-912e-4950-b4fd-fb4674843840"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Recommendations saved to: /content/drive/MyDrive/Deep Learning Project/wildfire_recommendations1.txt\n"
+ ]
+ }
+ ],
+ "source": [
+ "file_path = '/content/drive/MyDrive/Deep Learning Project/wildfire_recommendations1.txt'\n",
+ "\n",
+ "# Save the response to a text file\n",
+ "with open(file_path, 'w') as f:\n",
+ " f.write(recommendations)\n",
+ "\n",
+ "print(f\"Recommendations saved to: {file_path}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 766
+ },
+ "id": "Hw3K8YGv5hEk",
+ "outputId": "da68cd1e-055d-4850-bef1-7e294a26e815"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
+ "## Moderate Wildfire Management Plan: Recommendations\n",
+ "\n",
+ "A moderate wildfire, while less severe than a large or extreme fire, still requires a swift and well-coordinated response to prevent escalation and minimize damage. This plan outlines immediate actions, evacuation strategies, post-fire restoration, and long-term prevention measures.\n",
+ "\n",
+ "**I. Emergency Response & Fire Containment (Immediate Actions):**\n",
+ "\n",
+ "1. **Initial Attack:** The immediate priority is to prevent the fire from spreading beyond the moderate classification. This requires rapid deployment of:\n",
+ " * **Ground crews:** Utilizing hand crews with chainsaws, shovels, and fire rakes to create firebreaks along the perimeter, focusing on containing the head of the fire (direction of fastest spread). Engine crews should be deployed for direct attack and suppression with water and foam.\n",
+ " * **Air support:** Depending on terrain and accessibility, air tankers and helicopters can be effectively used for water or retardant drops to slow the fire's advance, especially in challenging terrain. Aerial ignition (backburning) might be considered strategically under expert supervision to create controlled firebreaks.\n",
+ " * **Technology:** Employing drones with thermal imaging for precise fire mapping, monitoring fire behavior, and assessing hot spots critical to efficient resource allocation.\n",
+ "2. **Resource Mobilization:** A well-defined Incident Command System (ICS) is crucial. This involves coordinating resources from multiple agencies (fire departments, forestry, law enforcement) and ensuring clear communication channels. Adequate personnel, equipment, and supplies must be secured, including food, water, and safety gear.\n",
+ "3. **Weather Monitoring:** Constant monitoring of weather conditions (wind speed, direction, humidity, temperature) is essential to predict fire behavior and adapt suppression strategies accordingly. Changes in wind can rapidly alter fire spread and necessitate immediate adjustments to containment lines.\n",
+ "\n",
+ "**II. Evacuation Plans:**\n",
+ "\n",
+ "1. **Pre-emptive Evacuations:** Based on fire spread predictions and risk assessment (using factors like fuel type, topography, and weather), pre-emptive evacuations of threatened communities or vulnerable populations (elderly, disabled) should be initiated promptly.\n",
+ "2. **Clear Communication:** Utilize multiple channels (emergency alerts, radio, social media, door-to-door notifications) to ensure timely and accurate evacuation information reaches residents. Evacuation routes must be clearly marked and regularly updated.\n",
+ "3. **Designated Shelters:** Secure and adequately staffed evacuation shelters with sufficient supplies (food, water, medical assistance) must be established in safe locations.\n",
+ "4. **Pet and Livestock Provisions:** Include plans for safe evacuation and temporary housing of pets and livestock. Designated animal shelters should be established.\n",
+ "\n",
+ "\n",
+ "**III. Ecosystem Restoration:**\n",
+ "\n",
+ "1. **Post-Fire Assessment:** Conduct a comprehensive assessment of the fire's impact on the ecosystem (soil erosion, water quality, wildlife habitat). This will inform restoration strategies.\n",
+ "2. **Erosion Control:** Implement measures to prevent soil erosion (e.g., seeding, mulching, contour plowing). This is especially crucial in areas with steep slopes.\n",
+ "3. **Revegetation:** Strategic replanting of native species, focusing on fire-resistant varieties, is essential to restore vegetation cover and prevent further erosion. Consider natural regeneration wherever possible.\n",
+ "4. **Wildlife Monitoring:** Monitor wildlife populations and their habitat recovery. This may involve providing supplemental food or water in severely impacted areas.\n",
+ "5. **Water Quality Monitoring:** Regularly monitor water quality in streams and rivers to assess potential contamination from ash or runoff. Implement water treatment if necessary.\n",
+ "\n",
+ "\n",
+ "**IV. Community Awareness & Prevention:**\n",
+ "\n",
+ "1. **Public Education Campaigns:** Launch educational programs focused on wildfire prevention, including responsible campfire practices, safe use of outdoor equipment, and the importance of defensible space around homes.\n",
+ "2. **Fuel Management Programs:** Promote community involvement in fuel reduction activities (thinning vegetation, clearing brush) around homes and in critical areas. Offer workshops and incentives to support these efforts.\n",
+ "3. **Early Warning Systems:** Improve community communication systems and early warning alerts to ensure timely response to potential wildfires.\n",
+ "4. **Community Firewise Workshops:** Organize workshops to educate the community about fire safety, emergency preparedness, and post-fire recovery.\n",
+ "5. **Collaboration:** Foster collaboration between local government agencies, fire departments, and community members to establish a comprehensive wildfire risk reduction plan.\n",
+ "\n",
+ "\n",
+ "**Expert Note:** This plan provides a framework. The specific actions taken will depend on the unique characteristics of the wildfire, the available resources, and the specific geographical context. Regular updates and adaptations to the plan are essential to ensure effective management and mitigation. Professional expertise in fire behavior, suppression techniques, and ecosystem restoration is crucial throughout the entire process. Ignoring any aspect could lead to the escalation of the fire and increased damages.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "image_path = \"/content/drive/MyDrive/Deep Learning Project/test2.png\" # Replace with the actual image path\n",
+ "severity = get_fire_severity(image_path) # Get severity dynamically from the model\n",
+ "recommendations = generate_wildfire_recommendations(severity)\n",
+ "\n",
+ "print(recommendations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Pdi_-qfN8hkp",
+ "outputId": "5ff4227c-cee6-4e8f-9455-b62f2817e95c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Recommendations saved to: /content/drive/MyDrive/Deep Learning Project/wildfire_recommendations2.txt\n"
+ ]
+ }
+ ],
+ "source": [
+ "file_path = '/content/drive/MyDrive/Deep Learning Project/wildfire_recommendations2.txt'\n",
+ "\n",
+ "# Save the response to a text file\n",
+ "with open(file_path, 'w') as f:\n",
+ " f.write(recommendations)\n",
+ "\n",
+ "print(f\"Recommendations saved to: {file_path}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 783
+ },
+ "id": "-6l7X1x_8qqB",
+ "outputId": "6768c8fa-f298-4924-bd07-8394789797f4"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
+ "## Severe Wildfire Emergency Response & Management Plan\n",
+ "\n",
+ "This plan outlines immediate actions, containment strategies, post-fire restoration, and preventative measures for a severe wildfire. Severity implies rapid spread, high intensity, and significant threat to life and property. Speed and decisive action are paramount.\n",
+ "\n",
+ "**I. Emergency Response (Immediate Actions to Control the Fire):**\n",
+ "\n",
+ "1. **Rapid Assessment & Resource Deployment:** Utilize all available aerial resources (e.g., air tankers, helicopters for water/retardant drops, reconnaissance) immediately. Ground crews should be deployed strategically based on real-time fire behavior analysis, focusing on establishing containment lines along defensible terrain. This requires sophisticated modeling and prediction to anticipate fire spread.\n",
+ "2. **Structure Protection:** Prioritize the protection of lives and critical infrastructure (hospitals, power stations). Deploy specialized crews equipped with fire-resistant materials and sprinklers to defend structures in the fire's path. This requires pre-planning and pre-positioning of resources in high-risk areas.\n",
+ "3. **Containment Line Establishment:** Multiple containment lines are crucial for a severe fire. Employ a combination of direct attack (engaging the fire directly) where feasible and indirect attack (creating firebreaks ahead of the fire) where terrain and safety allow. This will likely require extensive use of heavy machinery and controlled burns to create breaks.\n",
+ "4. **Communication & Coordination:** Establish a unified command structure with clear communication channels between all agencies involved (fire departments, law enforcement, emergency medical services, etc.). Real-time data sharing and decision-making are essential.\n",
+ "5. **Safety Protocols:** Strict adherence to safety protocols is non-negotiable. Crews need adequate training, proper personal protective equipment (PPE), and clear communication procedures to mitigate risks of injury or fatality.\n",
+ "\n",
+ "\n",
+ "**II. Evacuation Plans:**\n",
+ "\n",
+ "1. **Pre-emptive Evacuations:** Evacuations should be ordered proactively based on fire behavior predictions, not just when the fire is immediately threatening. This might involve staged evacuations based on risk zones.\n",
+ "2. **Clear Communication:** Use multiple communication channels (e.g., emergency alerts, radio, social media, door-to-door notifications) to ensure timely and effective evacuation orders reach all residents. Translations for diverse populations are critical.\n",
+ "3. **Designated Evacuation Routes & Shelters:** Pre-determined evacuation routes and clearly marked shelters are vital. Shelters should have sufficient resources (food, water, medical supplies) and be accessible for people with disabilities. Pet-friendly shelters should also be available.\n",
+ "4. **Accountability:** Implement a system to account for evacuated residents and track their whereabouts. This might include registration at shelters or online tools.\n",
+ "5. **Post-evacuation Procedures:** Develop clear procedures for residents to return home safely, including inspections for structural damage, hazards, and utility restoration.\n",
+ "\n",
+ "\n",
+ "**III. Ecosystem Restoration:**\n",
+ "\n",
+ "1. **Post-Fire Assessment:** A thorough assessment of the damage is essential. This includes analyzing soil erosion potential, water quality impacts, and the extent of damage to vegetation and wildlife habitats.\n",
+ "2. **Erosion Control:** Immediate measures are needed to prevent soil erosion, including seeding and mulching bare areas. Revegetation efforts should focus on native species adapted to the local climate.\n",
+ "3. **Water Quality Monitoring:** Monitor water sources for contamination from ash and debris. Implement measures to prevent sediment runoff and protect water quality.\n",
+ "4. **Wildlife Management:** Assess the impact on wildlife populations and implement strategies to support their recovery, such as habitat restoration and relocation programs where necessary.\n",
+ "5. **Long-term Monitoring:** Continued monitoring is critical to track the ecosystem's recovery over the years.\n",
+ "\n",
+ "\n",
+ "**IV. Community Awareness and Education Programs:**\n",
+ "\n",
+ "1. **Public Education Campaigns:** Develop and implement targeted campaigns focusing on wildfire prevention, including responsible land management practices, safe campfire use, and the importance of creating defensible space around homes.\n",
+ "2. **Community Training:** Offer training programs on wildfire preparedness, evacuation procedures, and basic firefighting techniques for community members.\n",
+ "3. **Collaboration with Stakeholders:** Work with landowners, businesses, and community organizations to promote wildfire prevention strategies and resource sharing.\n",
+ "4. **Improved Infrastructure:** Invest in infrastructure improvements that reduce wildfire risk, such as upgraded water systems, improved road access for firefighting equipment, and strategic fuel management along critical areas.\n",
+ "5. **Early Warning Systems:** Enhance early warning systems to provide timely alerts and warnings to residents. This may include advanced weather monitoring and predictive fire models.\n",
+ "\n",
+ "\n",
+ "This plan must be flexible and adaptable, responding to the dynamic nature of severe wildfires. Regular review and updates are crucial based on the latest scientific understanding and technological advancements. Furthermore, interagency cooperation and community involvement are paramount for successful wildfire management.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "image_path = \"/content/drive/MyDrive/Deep Learning Project/test3.jpg\" # Replace with the actual image path\n",
+ "severity = get_fire_severity(image_path) # Get severity dynamically from the model\n",
+ "recommendations = generate_wildfire_recommendations(severity)\n",
+ "\n",
+ "print(recommendations)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hqob3mAH9EUn",
+ "outputId": "1c1238c9-b65f-4a2e-ac7b-a909e1a3e080"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Recommendations saved to: /content/drive/MyDrive/Deep Learning Project/wildfire_recommendations3.txt\n"
+ ]
+ }
+ ],
+ "source": [
+ "file_path = '/content/drive/MyDrive/Deep Learning Project/wildfire_recommendations3.txt'\n",
+ "\n",
+ "# Save the response to a text file\n",
+ "with open(file_path, 'w') as f:\n",
+ " f.write(recommendations)\n",
+ "\n",
+ "print(f\"Recommendations saved to: {file_path}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "___________________________________________________________________________________________________________________________________"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rrSWJ4IR9tzf"
+ },
+ "source": [
+ "## Project Deployment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1. Ngrok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "LFJzz6dI9vtp",
+ "outputId": "ab407929-1419-4af0-c300-fb5adf681ecc"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * ngrok tunnel available at: NgrokTunnel: \"https://e188-34-106-189-155.ngrok-free.app\" -> \"http://localhost:5000\"\n",
+ " * Serving Flask app '__main__'\n",
+ " * Debug mode: off\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
+ " * Running on http://127.0.0.1:5000\n",
+ "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from flask import Flask, request, jsonify\n",
+ "from pyngrok import ngrok\n",
+ "import tensorflow as tf\n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "\n",
+ "# Load models\n",
+ "vgg16_model = tf.keras.models.load_model('/content/drive/MyDrive/Deep Learning Project/vgg16_best_model.keras')\n",
+ "xception_model = tf.keras.models.load_model('/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras')\n",
+ "\n",
+ "app = Flask(__name__)\n",
+ "\n",
+ "@app.route(\"/predict_fire\", methods=[\"POST\"])\n",
+ "def predict_fire():\n",
+ " file = request.files[\"image\"]\n",
+ " img = Image.open(file).convert(\"RGB\")\n",
+ " img = img.resize((224, 224))\n",
+ " img_array = np.array(img) / 255.0\n",
+ " img_array = np.expand_dims(img_array, axis=0)\n",
+ "\n",
+ " fire_predictions = vgg16_model.predict(img_array)\n",
+ " fire_status = \"Fire Detected\" if fire_predictions[0][0] > 0.5 else \"No Fire Detected\"\n",
+ "\n",
+ " severity_predictions = xception_model.predict(img_array)\n",
+ " severity = np.argmax(severity_predictions[0])\n",
+ "\n",
+ " return jsonify({\"fire_status\": fire_status, \"severity\": severity})\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " # Start ngrok tunnel\n",
+ " public_url = ngrok.connect(5000)\n",
+ " print(f\" * ngrok tunnel available at: {public_url}\")\n",
+ "\n",
+ " # Run Flask app\n",
+ " app.run()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YZ1NViip9wTN",
+ "outputId": "e4fc91e7-0a7d-4af1-d39f-36dc67a4dcfb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " * ngrok tunnel \"NgrokTunnel: \"https://65b2-34-106-189-155.ngrok-free.app\" -> \"http://localhost:5000\"\" -> \"http://127.0.0.1:5000\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pyngrok import ngrok\n",
+ "\n",
+ "# Replace 'your-authtoken' with your actual ngrok authtoken\n",
+ "ngrok.set_auth_token('nice_token')\n",
+ "\n",
+ "# Open a port (5000 is default for Flask)\n",
+ "public_url = ngrok.connect(5000)\n",
+ "print(f\" * ngrok tunnel \\\"{public_url}\\\" -> \\\"http://127.0.0.1:5000\\\"\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R5P2mFDPD96_"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2. HuggingFace Spaces using Gradio"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "KWmA9e3l-f1z"
+ },
+ "outputs": [],
+ "source": [
+ "code = \"\"\"\n",
+ "import gradio as gr\n",
+ "import tensorflow as tf\n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "\n",
+ "# Load models\n",
+ "vgg16_model = tf.keras.models.load_model('/content/drive/MyDrive/Deep Learning Project/vgg16_best_model.keras')\n",
+ "xception_model = tf.keras.models.load_model('/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras')\n",
+ "\n",
+ "def predict_fire(image):\n",
+ " img = Image.fromarray(image).convert(\"RGB\")\n",
+ " img = img.resize((224, 224)) # Match model input size\n",
+ " img_array = np.array(img) / 255.0\n",
+ " img_array = np.expand_dims(img_array, axis=0)\n",
+ "\n",
+ " fire_pred = vgg16_model.predict(img_array)\n",
+ " fire_status = \"Fire Detected\" if fire_pred[0][0] > 0.5 else \"No Fire Detected\"\n",
+ "\n",
+ " if fire_status == \"Fire Detected\":\n",
+ " severity_pred = xception_model.predict(img_array)\n",
+ " severity_level = np.argmax(severity_pred[0])\n",
+ " severity = [\"Mild\", \"Moderate\", \"Severe\"][severity_level]\n",
+ " else:\n",
+ " severity = \"N/A\"\n",
+ "\n",
+ " return fire_status, severity\n",
+ "\n",
+ "# Gradio interface\n",
+ "interface = gr.Interface(\n",
+ " fn=predict_fire,\n",
+ " inputs=gr.Image(type=\"numpy\", label=\"Upload Image\"),\n",
+ " outputs=[\n",
+ " gr.Textbox(label=\"Fire Status\"),\n",
+ " gr.Textbox(label=\"Severity Level\"),\n",
+ " ],\n",
+ " title=\"Fire Prediction and Severity Classification\",\n",
+ " description=\"Upload an image to predict fire and its severity level (Mild, Moderate, Severe).\",\n",
+ ")\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " interface.launch()\n",
+ "\"\"\"\n",
+ "with open(\"app.py\", \"w\") as f:\n",
+ " f.write(code)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "2gfw0n-DGr_u"
+ },
+ "outputs": [],
+ "source": [
+ "requirements = \"\"\"\n",
+ "tensorflow\n",
+ "gradio\n",
+ "pillow\n",
+ "numpy\n",
+ "\"\"\"\n",
+ "with open(\"requirements.txt\", \"w\") as f:\n",
+ " f.write(requirements)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FQ9z61HlHpIH",
+ "outputId": "30999d42-0a5a-4f83-e549-32c5a9d4d461"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mkdir: cannot create directory ‘/content/drive/MyDrive/Deep_Learning_Project/models’: No such file or directory\n",
+ "cp: cannot create regular file '/content/drive/MyDrive/Deep_Learning_Project/models/': No such file or directory\n",
+ "cp: cannot create regular file '/content/drive/MyDrive/Deep_Learning_Project/models/': No such file or directory\n",
+ "[Errno 2] No such file or directory: '/content/drive/MyDrive/Deep_Learning_Project/'\n",
+ "/content\n",
+ "updating: models/ (stored 0%)\n",
+ "updating: models/xception_best.keras (deflated 8%)\n",
+ "updating: models/vgg16_best_model.keras (deflated 7%)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a models directory in your Google Drive\n",
+ "!mkdir /content/drive/MyDrive/Deep_Learning_Project/models\n",
+ "\n",
+ "# Copy the models into the new directory\n",
+ "!cp \"/content/drive/MyDrive/Deep Learning Project/vgg16_best_model.keras\" \"/content/drive/MyDrive/Deep_Learning_Project/models/\"\n",
+ "!cp \"/content/drive/MyDrive/Deep Learning Project/Tri Classification/xception_best.keras\" \"/content/drive/MyDrive/Deep_Learning_Project/models/\"\n",
+ "\n",
+ "# Change directory to where the models are saved\n",
+ "%cd /content/drive/MyDrive/Deep_Learning_Project/\n",
+ "\n",
+ "# Zip the models folder\n",
+ "!zip -r models.zip models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yCZxvJ98UN-e"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oPi5q2mOUsAi"
+ },
+ "source": [
+ "Above prediction is true and is verfied."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vRgQoJ8OUvA5"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Kt8-lOnuUx1O"
+ },
+ "source": [
+ "Above prediction is true and is verfied"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7nK3arm_U95h"
+ },
+ "source": [
+ "The above examples demonstrate that the models are deployed and tested for our use case.\n",
+ "\n",
+ "The app is accessible here: https://huggingface.co/spaces/AbdullahImran/Wildfire-Predictions-and-Recommendations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cmwZs7cyVTXt"
+ },
+ "source": [
+ "Now the recommendations are accessible via API, so it is mostly accessible via code only."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RoSDlhseVZ7y"
+ },
+ "source": [
+ "The recommendations are also very good, especially comprared with Llama, GPT-2 and BLIP that we tested earlier. However, it was hard to implement them via API, so rule based recommendations are given."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9Mauz9QQexww"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rR-TZSnFe1Yx"
+ },
+ "source": [
+ "Above is a rule based recommendation example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IBedYFLue440"
+ },
+ "source": [
+ "Project finally stands completed after so many restless days :)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e5EVd0n9HrP4"
+ },
+ "source": [
+ "_________________________________________________________________________________________________________________________________"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "_________________________________________________________________________________________________________________________________"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "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": "3.9.19"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
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index 0000000000000000000000000000000000000000..075b393b4b0b0a6ae98af19be2d71df03f439ec3
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+++ b/README.md
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+# EmberDeepAI
\ No newline at end of file
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diff --git a/Recommendation_Generation/wildfire_recommendations1.txt b/Recommendation_Generation/wildfire_recommendations1.txt
new file mode 100644
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--- /dev/null
+++ b/Recommendation_Generation/wildfire_recommendations1.txt
@@ -0,0 +1,39 @@
+## Mild Wildfire Management Plan: Expert Recommendations
+
+A "mild" wildfire designation implies a relatively slow-moving fire with limited acreage and minimal immediate threat to life and property. However, complacency is dangerous; even mild fires can escalate rapidly under the right conditions. This plan prioritizes swift, efficient containment while emphasizing long-term prevention and ecosystem restoration.
+
+**1. Immediate Actions to Control the Fire (Emergency Response):**
+
+* **Initial Assessment:** A rapid, expert assessment is crucial. This involves determining fire perimeter, fuel type, wind speed & direction, topography, and potential spread rate. Utilize aerial reconnaissance (drone or aircraft) if feasible for better situational awareness.
+* **Direct Attack:** For mild fires, a direct attack is often effective. This involves deploying firefighters with hand tools (shovels, rakes, pulaskis) to create firebreaks and directly extinguish flames. Focus on removing fuel ahead of the fire's leading edge. Water tankers and smaller engine crews can provide supplemental support.
+* **Indirect Attack (if necessary):** If the fire is approaching structures or valuable resources, an indirect attack may be necessary. This involves creating containment lines *outside* the fire perimeter to prevent its spread, allowing firefighters to work safely at a distance. Backburns, under controlled conditions, may be used to consume fuel in the path of the fire.
+* **Communication:** Establish clear communication channels between all personnel, including incident command, firefighters, and support crews. Regular updates are critical.
+* **Resource Mobilization:** While the fire is mild, rapid response prevents escalation. Ensure adequate personnel, equipment, and water resources are available and quickly deployed.
+
+
+**2. Evacuation Plans to Protect Life and Property:**
+
+* **Targeted Evacuations:** For mild fires, evacuations are typically not widespread. However, a pre-emptive plan is essential for areas immediately threatened. This should include defining evacuation zones based on real-time fire behavior and wind patterns.
+* **Alert System:** Utilize a multi-channel alert system (text alerts, sirens, radio announcements) to quickly inform residents in threatened areas. Clear, concise messaging is paramount.
+* **Designated Evacuation Routes:** Establish clear, well-maintained evacuation routes with signage. Regular maintenance and inspections of these routes are essential.
+* **Emergency Shelters:** Pre-identify and prepare emergency shelters with provisions for displaced residents. Consider pet-friendly options.
+* **Evacuation Assistance:** Arrange for assistance for vulnerable populations (elderly, disabled, those without transportation).
+
+**3. Strategies to Restore the Ecosystem After the Fire:**
+
+* **Monitoring and Assessment:** Post-fire monitoring is vital. Assess the extent of damage to vegetation, soil, and water resources. Identify areas at risk of erosion or flooding.
+* **Erosion Control:** Implement erosion control measures to prevent soil loss. This might include seeding, mulching, or installing temporary barriers.
+* **Revegetation:** Depending on the severity of the burn, natural regeneration may be sufficient. However, in areas with significant damage, replanting native species may be necessary to accelerate ecosystem recovery and prevent invasive species.
+* **Water Resource Management:** Assess the impact of the fire on water quality and quantity. Implement measures to prevent water contamination and erosion into streams and rivers.
+* **Wildlife Monitoring:** Monitor wildlife populations to assess the impact of the fire and implement appropriate conservation strategies if needed.
+
+**4. Awareness and Education Programs for the Community to Prevent Future Fires:**
+
+* **Community Fire Safety Workshops:** Organize workshops to educate the community on fire prevention, safe campfire practices, and the use of power tools during dry conditions.
+* **Public Service Announcements:** Utilize local media to disseminate fire safety information. Highlight the importance of clearing defensible space around homes and businesses.
+* **Fuel Management Programs:** Promote community-based fuel reduction projects, such as controlled burns (under expert supervision) and vegetation thinning.
+* **Early Warning Systems:** Improve the early warning system through weather monitoring, fire detection technology, and community watch programs.
+* **Enforcement of Fire Regulations:** Strict enforcement of fire restrictions and regulations during high-risk periods is essential.
+
+
+**Important Note:** This plan provides a framework. The specific actions will vary depending on the exact characteristics of the mild wildfire, local resources, and environmental conditions. Expert advice from experienced wildfire management professionals is crucial throughout the entire process. Never underestimate the potential for a mild fire to escalate. Proactive and well-coordinated efforts are key to successful management.
diff --git a/Recommendation_Generation/wildfire_recommendations2.txt b/Recommendation_Generation/wildfire_recommendations2.txt
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+++ b/Recommendation_Generation/wildfire_recommendations2.txt
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+## Moderate Wildfire Management Plan: Recommendations
+
+A moderate wildfire, while less severe than a large or extreme fire, still requires a swift and well-coordinated response to prevent escalation and minimize damage. This plan outlines immediate actions, evacuation strategies, post-fire restoration, and long-term prevention measures.
+
+**I. Emergency Response & Fire Containment (Immediate Actions):**
+
+1. **Initial Attack:** The immediate priority is to prevent the fire from spreading beyond the moderate classification. This requires rapid deployment of:
+ * **Ground crews:** Utilizing hand crews with chainsaws, shovels, and fire rakes to create firebreaks along the perimeter, focusing on containing the head of the fire (direction of fastest spread). Engine crews should be deployed for direct attack and suppression with water and foam.
+ * **Air support:** Depending on terrain and accessibility, air tankers and helicopters can be effectively used for water or retardant drops to slow the fire's advance, especially in challenging terrain. Aerial ignition (backburning) might be considered strategically under expert supervision to create controlled firebreaks.
+ * **Technology:** Employing drones with thermal imaging for precise fire mapping, monitoring fire behavior, and assessing hot spots critical to efficient resource allocation.
+2. **Resource Mobilization:** A well-defined Incident Command System (ICS) is crucial. This involves coordinating resources from multiple agencies (fire departments, forestry, law enforcement) and ensuring clear communication channels. Adequate personnel, equipment, and supplies must be secured, including food, water, and safety gear.
+3. **Weather Monitoring:** Constant monitoring of weather conditions (wind speed, direction, humidity, temperature) is essential to predict fire behavior and adapt suppression strategies accordingly. Changes in wind can rapidly alter fire spread and necessitate immediate adjustments to containment lines.
+
+**II. Evacuation Plans:**
+
+1. **Pre-emptive Evacuations:** Based on fire spread predictions and risk assessment (using factors like fuel type, topography, and weather), pre-emptive evacuations of threatened communities or vulnerable populations (elderly, disabled) should be initiated promptly.
+2. **Clear Communication:** Utilize multiple channels (emergency alerts, radio, social media, door-to-door notifications) to ensure timely and accurate evacuation information reaches residents. Evacuation routes must be clearly marked and regularly updated.
+3. **Designated Shelters:** Secure and adequately staffed evacuation shelters with sufficient supplies (food, water, medical assistance) must be established in safe locations.
+4. **Pet and Livestock Provisions:** Include plans for safe evacuation and temporary housing of pets and livestock. Designated animal shelters should be established.
+
+
+**III. Ecosystem Restoration:**
+
+1. **Post-Fire Assessment:** Conduct a comprehensive assessment of the fire's impact on the ecosystem (soil erosion, water quality, wildlife habitat). This will inform restoration strategies.
+2. **Erosion Control:** Implement measures to prevent soil erosion (e.g., seeding, mulching, contour plowing). This is especially crucial in areas with steep slopes.
+3. **Revegetation:** Strategic replanting of native species, focusing on fire-resistant varieties, is essential to restore vegetation cover and prevent further erosion. Consider natural regeneration wherever possible.
+4. **Wildlife Monitoring:** Monitor wildlife populations and their habitat recovery. This may involve providing supplemental food or water in severely impacted areas.
+5. **Water Quality Monitoring:** Regularly monitor water quality in streams and rivers to assess potential contamination from ash or runoff. Implement water treatment if necessary.
+
+
+**IV. Community Awareness & Prevention:**
+
+1. **Public Education Campaigns:** Launch educational programs focused on wildfire prevention, including responsible campfire practices, safe use of outdoor equipment, and the importance of defensible space around homes.
+2. **Fuel Management Programs:** Promote community involvement in fuel reduction activities (thinning vegetation, clearing brush) around homes and in critical areas. Offer workshops and incentives to support these efforts.
+3. **Early Warning Systems:** Improve community communication systems and early warning alerts to ensure timely response to potential wildfires.
+4. **Community Firewise Workshops:** Organize workshops to educate the community about fire safety, emergency preparedness, and post-fire recovery.
+5. **Collaboration:** Foster collaboration between local government agencies, fire departments, and community members to establish a comprehensive wildfire risk reduction plan.
+
+
+**Expert Note:** This plan provides a framework. The specific actions taken will depend on the unique characteristics of the wildfire, the available resources, and the specific geographical context. Regular updates and adaptations to the plan are essential to ensure effective management and mitigation. Professional expertise in fire behavior, suppression techniques, and ecosystem restoration is crucial throughout the entire process. Ignoring any aspect could lead to the escalation of the fire and increased damages.
diff --git a/Recommendation_Generation/wildfire_recommendations3.txt b/Recommendation_Generation/wildfire_recommendations3.txt
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+++ b/Recommendation_Generation/wildfire_recommendations3.txt
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+## Severe Wildfire Emergency Response & Management Plan
+
+This plan outlines immediate actions, containment strategies, post-fire restoration, and preventative measures for a severe wildfire. Severity implies rapid spread, high intensity, and significant threat to life and property. Speed and decisive action are paramount.
+
+**I. Emergency Response (Immediate Actions to Control the Fire):**
+
+1. **Rapid Assessment & Resource Deployment:** Utilize all available aerial resources (e.g., air tankers, helicopters for water/retardant drops, reconnaissance) immediately. Ground crews should be deployed strategically based on real-time fire behavior analysis, focusing on establishing containment lines along defensible terrain. This requires sophisticated modeling and prediction to anticipate fire spread.
+2. **Structure Protection:** Prioritize the protection of lives and critical infrastructure (hospitals, power stations). Deploy specialized crews equipped with fire-resistant materials and sprinklers to defend structures in the fire's path. This requires pre-planning and pre-positioning of resources in high-risk areas.
+3. **Containment Line Establishment:** Multiple containment lines are crucial for a severe fire. Employ a combination of direct attack (engaging the fire directly) where feasible and indirect attack (creating firebreaks ahead of the fire) where terrain and safety allow. This will likely require extensive use of heavy machinery and controlled burns to create breaks.
+4. **Communication & Coordination:** Establish a unified command structure with clear communication channels between all agencies involved (fire departments, law enforcement, emergency medical services, etc.). Real-time data sharing and decision-making are essential.
+5. **Safety Protocols:** Strict adherence to safety protocols is non-negotiable. Crews need adequate training, proper personal protective equipment (PPE), and clear communication procedures to mitigate risks of injury or fatality.
+
+
+**II. Evacuation Plans:**
+
+1. **Pre-emptive Evacuations:** Evacuations should be ordered proactively based on fire behavior predictions, not just when the fire is immediately threatening. This might involve staged evacuations based on risk zones.
+2. **Clear Communication:** Use multiple communication channels (e.g., emergency alerts, radio, social media, door-to-door notifications) to ensure timely and effective evacuation orders reach all residents. Translations for diverse populations are critical.
+3. **Designated Evacuation Routes & Shelters:** Pre-determined evacuation routes and clearly marked shelters are vital. Shelters should have sufficient resources (food, water, medical supplies) and be accessible for people with disabilities. Pet-friendly shelters should also be available.
+4. **Accountability:** Implement a system to account for evacuated residents and track their whereabouts. This might include registration at shelters or online tools.
+5. **Post-evacuation Procedures:** Develop clear procedures for residents to return home safely, including inspections for structural damage, hazards, and utility restoration.
+
+
+**III. Ecosystem Restoration:**
+
+1. **Post-Fire Assessment:** A thorough assessment of the damage is essential. This includes analyzing soil erosion potential, water quality impacts, and the extent of damage to vegetation and wildlife habitats.
+2. **Erosion Control:** Immediate measures are needed to prevent soil erosion, including seeding and mulching bare areas. Revegetation efforts should focus on native species adapted to the local climate.
+3. **Water Quality Monitoring:** Monitor water sources for contamination from ash and debris. Implement measures to prevent sediment runoff and protect water quality.
+4. **Wildlife Management:** Assess the impact on wildlife populations and implement strategies to support their recovery, such as habitat restoration and relocation programs where necessary.
+5. **Long-term Monitoring:** Continued monitoring is critical to track the ecosystem's recovery over the years.
+
+
+**IV. Community Awareness and Education Programs:**
+
+1. **Public Education Campaigns:** Develop and implement targeted campaigns focusing on wildfire prevention, including responsible land management practices, safe campfire use, and the importance of creating defensible space around homes.
+2. **Community Training:** Offer training programs on wildfire preparedness, evacuation procedures, and basic firefighting techniques for community members.
+3. **Collaboration with Stakeholders:** Work with landowners, businesses, and community organizations to promote wildfire prevention strategies and resource sharing.
+4. **Improved Infrastructure:** Invest in infrastructure improvements that reduce wildfire risk, such as upgraded water systems, improved road access for firefighting equipment, and strategic fuel management along critical areas.
+5. **Early Warning Systems:** Enhance early warning systems to provide timely alerts and warnings to residents. This may include advanced weather monitoring and predictive fire models.
+
+
+This plan must be flexible and adaptable, responding to the dynamic nature of severe wildfires. Regular review and updates are crucial based on the latest scientific understanding and technological advancements. Furthermore, interagency cooperation and community involvement are paramount for successful wildfire management.
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