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rrayhka commited on Jan 19

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d0e04e1

1 Parent(s): 8f4b8cc

init

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.gitattributes +2 -0
data/Bangkalan 1th hourly.csv +0 -0
data/Sby 1th hourly.csv +0 -0
data/cluster/climate_data_clustered.csv +0 -0
data/forecast/bkl_temp_forecast.csv +722 -0
data/forecast/sby_temp_forecast.csv +722 -0
data/model/cnn.ipynb +0 -0
data/model/cnnModel.keras +3 -0
data/model/wp_CNN_test_preprocess.ipynb +114 -0
data/model/wp_EDA_preprocessing.ipynb +0 -0
data/model/wp_confusion_matrix.ipynb +225 -0
data/model/wp_prediction_and_log-loss.ipynb +138 -0
data/predict/3526.json +0 -0
data/predict/3578.json +0 -0
data/predict/clear-day.png +0 -0
data/predict/label_encoder.pkl +3 -0
data/predict/label_encoder_bgkln.pkl +3 -0
data/predict/model_rf.pkl +3 -0
data/predict/model_rf_bgkln.pkl +3 -0
data/predict/rain.png +0 -0
data/predict/sun-cloudy.png +0 -0
data/predict/sun.png +0 -0
main.py +183 -0
programs/load_data.py +61 -0
programs/map.py +31 -0
programs/predict_cuaca.py +130 -0
requirements.txt +8 -0
static/cloudicon.png +0 -0
static/clouds.png +0 -0
static/feelslike.png +0 -0
static/humidity.png +0 -0
static/low-visibility.png +0 -0
static/meteorology.png +0 -0
static/rain.png +0 -0
static/storm.png +0 -0
static/sun-cloudy.png +0 -0
static/sun.png +0 -0
static/temp.png +0 -0
static/weather-forecast.png +0 -0
static/wind.png +0 -0
templates/index.html +250 -0
templates/index_cluster.html +0 -0
templates/peta_persebaran.html +0 -0
templates/prediksi_cuaca.html +289 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

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 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text
+*.xlsx filter=lfs diff=lfs merge=lfs -text

data/Bangkalan 1th hourly.csv ADDED Viewed

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data/Sby 1th hourly.csv ADDED Viewed

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data/cluster/climate_data_clustered.csv ADDED Viewed

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data/forecast/bkl_temp_forecast.csv ADDED Viewed

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data/model/cnnModel.keras ADDED Viewed

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7191af5a3ad7e8eb103508a9ac5a9dd8ae9c7a19cabb7e9c1d5ede8444851737
+size 38517504

data/model/wp_CNN_test_preprocess.ipynb ADDED Viewed

	@@ -0,0 +1,114 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.preprocessing.image import load_img, img_to_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_width, img_height = 256, 256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_image(path):\n",
+    "    img = load_img(path, target_size = (img_height, img_width))\n",
+    "    a = img_to_array(img)\n",
+    "    a = np.expand_dims(a, axis = 0)\n",
+    "    a /= 255.\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_images_dir = '../dataset/alien_test/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df = pd.read_csv('../dataset/test.csv')\n",
+    "\n",
+    "test_dfToList = test_df['Image_id'].tolist()\n",
+    "test_ids = [str(item) for item in test_dfToList]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_images = [test_images_dir+item for item in test_ids]\n",
+    "test_preprocessed_images = np.vstack([preprocess_image(fn) for fn in test_images])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.save('../test_preproc_CNN.npy', test_preprocessed_images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/model/wp_EDA_preprocessing.ipynb ADDED Viewed

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data/model/wp_confusion_matrix.ipynb ADDED Viewed

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_preprocessed_images = np.load('../test_preproc_CNN.npy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = '../scripts/cnnModel.keras'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Prediction Function\n",
+    "array = model.predict(test_preprocessed_images, batch_size=1, verbose=1)\n",
+    "y_pred = np.argmax(array, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df = pd.read_csv('../dataset/test.csv')\n",
+    "y_true = test_df['labels']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "conf_mat = confusion_matrix(y_true, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dir = '../weather_pred/Data/training/'\n",
+    "classes = os.listdir(train_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "def plot_confusion_matrix(cm, classes,\n",
+    "                          normalize=False,\n",
+    "                          title='Confusion matrix',\n",
+    "                          cmap=plt.cm.Reds):\n",
+    "    \"\"\"\n",
+    "    This function prints and plots the confusion matrix.\n",
+    "    Normalization can be applied by setting `normalize=True`.\n",
+    "    \"\"\"\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title(title)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45)\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "\n",
+    "    if normalize:\n",
+    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+    "        cm = cm.round(2)\n",
+    "        #print(\"Normalized confusion matrix\")\n",
+    "    else:\n",
+    "        cm=cm\n",
+    "        #print('Confusion matrix, without normalization')\n",
+    "\n",
+    "    #print(cm)\n",
+    "\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, cm[i, j],\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.set_printoptions(precision=2)\n",
+    "\n",
+    "fig1 = plt.figure(figsize=(7,6))\n",
+    "plot_confusion_matrix(conf_mat, classes=classes, title='Confusion matrix, without normalization')\n",
+    "#fig1.savefig('../cm_wo_norm.jpg')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.set_printoptions(precision=2)\n",
+    "\n",
+    "fig2 = plt.figure(figsize=(7,6))\n",
+    "plot_confusion_matrix(conf_mat, classes=classes, normalize = True, title='Normalized Confusion matrix')\n",
+    "fig2.savefig('../cm_norm.jpg')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/model/wp_prediction_and_log-loss.ipynb ADDED Viewed

	@@ -0,0 +1,138 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_width, img_height = 256, 256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_preprocessed_images = np.load('../test_preproc_CNN.npy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = '../scripts/cnnModel.keras'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\akhyar\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\saving\\saving_lib.py:719: UserWarning: Skipping variable loading for optimizer 'rmsprop', because it has 26 variables whereas the saved optimizer has 50 variables. \n",
+      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Load the pre-trained models\n",
+    "model = load_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Prediction Function\n",
+    "y_pred = model.predict(test_preprocessed_images, batch_size=1, verbose=1)\n",
+    "#y_pred = np.argmax(array, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df = pd.read_csv('../dataset/test.csv')\n",
+    "y_true = test_df['labels']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8849632600653125\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import log_loss\n",
+    "loss = log_loss(y_true, y_pred, normalize=True, sample_weight=None, labels=None)\n",
+    "print(loss)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/predict/3526.json ADDED Viewed

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data/predict/3578.json ADDED Viewed

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data/predict/clear-day.png ADDED Viewed

data/predict/label_encoder.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:27b30c4ed02896818303f36a2cc0ceed60a4718b0b739a1bf452f7c1c3fc968d
+size 554

data/predict/label_encoder_bgkln.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:27b30c4ed02896818303f36a2cc0ceed60a4718b0b739a1bf452f7c1c3fc968d
+size 554

data/predict/model_rf.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:8fd4bde0e3feb4885329a9af1a1fe93a1232f487186da4aea061723bd3c3a370
+size 66745009

data/predict/model_rf_bgkln.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:f083207178f1a0bc9592b15f172f5532a225da1b0ce4bb2ae382b3be862e857f
+size 22559217

data/predict/rain.png ADDED Viewed

data/predict/sun-cloudy.png ADDED Viewed

data/predict/sun.png ADDED Viewed

main.py ADDED Viewed

	@@ -0,0 +1,183 @@

+from flask import Flask, render_template, request, redirect
+from werkzeug.utils import secure_filename
+from programs.map import *
+from programs.predict_cuaca import *
+from datetime import datetime
+import os
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+import requests
+import sys
+import pandas as pd
+import json
+import joblib
+app = Flask(__name__)
+model = load_model('data/model/cnnModel.keras')
+# Tentukan ukuran gambar yang dibutuhkan oleh model
+img_width, img_height = 256, 256
+# Tentukan direktori penyimpanan gambar yang diupload
+app.config['UPLOAD_FOLDER'] = 'static/uploads'
+if not os.path.exists(app.config['UPLOAD_FOLDER']):
+    os.makedirs(app.config['UPLOAD_FOLDER'])
+@app.route('/', methods=['POST', 'GET'])
+def home():
+    sby_timeseries, sby_temp, bkl_timeseries, bkl_temp = get_forecast_data()
+    sby_data, bkl_data = get_combined_data()
+    filename = ''
+    predicted_class = ''
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            return redirect(request.url)
+        file = request.files['file']
+        if file.filename == '':
+            return redirect(request.url)
+        if file:
+            # Simpan gambar yang diupload
+            filename = secure_filename(file.filename)
+            file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
+            file.save(file_path)
+            # Proses gambar untuk prediksi
+            img = Image.open(file_path).convert('RGB')
+            img = img.resize((img_width, img_height))
+            img_array = np.array(img) / 255.0  # Normalisasi gambar
+            img_array = np.expand_dims(img_array, axis=0)  # Tambahkan dimensi batch
+            prediction = model.predict(img_array)
+            class_idx = np.argmax(prediction, axis=1)[0]
+            class_names = ['Berawan', 'Berkabut', 'Hujan', 'Cerah', 'Sunrise']
+            predicted_class = class_names[class_idx]
+    return render_template('index.html',
+                        h_now=datetime.now().strftime('%H'),
+                        d_now=datetime.now().strftime('%d'),
+                        dname_now=datetime.now().strftime('%A'),
+                        sby_timeseries=sby_timeseries,
+                        sby_temp=sby_temp,
+                        bkl_timeseries=bkl_timeseries,
+                        bkl_temp=bkl_temp,
+                        sby_data=sby_data,
+                        bkl_data=bkl_data,
+                        filename=filename, predicted_class=predicted_class
+                        )
+@app.route('/cuaca', methods=['POST', 'GET'])
+def prediksi_cuaca():
+    wilayah = request.args.get('wilayah')
+    filename = ''
+    predicted_class = ''
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            return redirect(request.url)
+        file = request.files['file']
+        if file.filename == '':
+            return redirect(request.url)
+        if file:
+            # Simpan gambar yang diupload
+            filename = secure_filename(file.filename)
+            file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
+            file.save(file_path)
+            # Proses gambar untuk prediksi
+            img = Image.open(file_path).convert('RGB')
+            img = img.resize((img_width, img_height))
+            img_array = np.array(img) / 255.0  # Normalisasi gambar
+            img_array = np.expand_dims(img_array, axis=0)  # Tambahkan dimensi batch
+            prediction = model.predict(img_array)
+            class_idx = np.argmax(prediction, axis=1)[0]
+            class_names = ['Berawan', 'Berkabut', 'Hujan', 'Cerah', 'Sunrise']
+            predicted_class = class_names[class_idx]
+    if request.method == 'GET':
+        if not wilayah:
+            return redirect('/cuaca?wilayah=surabaya')
+        if wilayah.lower() == "surabaya":
+            pred_class_weather, pred_logo_weather, pred_color_weather, pred_rgb_weather , df_weather, pred_icon_weather = predict_sby()
+            json_sby = 'data/predict/3578.json'
+            with open(json_sby) as f:
+                json_geo = json.load(f)
+                json_coor1 = -7.2890784
+                json_coor2 = 112.7786537
+        if wilayah.lower() == "bangkalan":
+            pred_class_weather, pred_logo_weather, pred_color_weather, pred_rgb_weather , df_weather, pred_icon_weather = predict_bangkalan()
+            json_bkl = 'data/predict/3526.json'
+            with open(json_bkl) as f:
+                json_geo = json.load(f)
+                json_coor1 = -7.0416324
+                json_coor2 = 112.9204642
+        if wilayah.lower() not in ['surabaya', 'bangkalan']:
+            return redirect('/cuaca?wilayah=surabaya')
+    return render_template('prediksi_cuaca.html',
+                        h_now=datetime.now().strftime('%H'),
+                        d_now=datetime.now().strftime('%d'),
+                        dname_now=datetime.now().strftime('%A'),
+                        pred_class_weather=pred_class_weather,
+                        pred_logo_weather=pred_logo_weather,
+                        pred_color_weather=pred_color_weather,
+                        pred_rgb_weather=pred_rgb_weather,
+                        df_weather=df_weather,
+                        pred_icon_weather=pred_icon_weather,
+                        json_geo=json_geo,
+                        json_coor1=json_coor1,
+                        json_coor2=json_coor2,
+                        filename=filename, predicted_class=predicted_class
+                        )
+@app.route("/cluster", methods=['POST', 'GET'])
+def hello():
+    peta_persebaran = folium.Map(location=[-2.5, 118], zoom_start=5)
+    df = pd.read_csv('data/cluster/climate_data_clustered.csv')
+    colors = ['red', 'yellow', 'green']
+    risk_mapping = {'low risk': 0 , 'medium risk': 1, 'high risk': 2 }
+    y_kmeans = df['cluster'].map(risk_mapping)
+    # Tambahkan kluster ke peta
+    for i in range(len(y_kmeans)):
+        clusternya = y_kmeans[i]
+        folium.CircleMarker(
+            location=[-7.2053, 112.7353],
+            radius=13,
+            color=colors[clusternya],
+            fill=True,
+            fill_color=colors[clusternya],
+            fill_opacity=0.1,
+        ).add_to(peta_persebaran)
+    peta_persebaran.save('./templates/peta_persebaran.html')
+    data = df.to_html(classes='table table-striped')
+    return render_template('index_cluster.html', dataframe=data)
+if __name__ == '__main__':
+    import os
+    os.environ['FLASK_ENV'] = 'development'
+    app.run(host='0.0.0.0', port=8001, debug=True)

programs/load_data.py ADDED Viewed

	@@ -0,0 +1,61 @@

+import pandas as pd
+import datetime
+def get_time(data, time=datetime.datetime.now()):
+    current_date = time.strftime("%Y-%m-%d %H")
+    return data[data['date'] == current_date]
+def get_data(data):
+    df = pd.read_csv(data, parse_dates=[0])
+    df.rename(columns={df.columns[0]: 'date'}, inplace=True)
+    df['date'] = df['date'].dt.strftime("%Y-%m-%d %H")
+    df = get_time(df)
+    timeseries = df.date.values
+    temp = df.predicted_mean.values
+    return timeseries, temp
+def get_data_all(data):
+    df = pd.read_csv(data, parse_dates=[0])
+    df.rename(columns={df.columns[0]: 'date'}, inplace=True)
+    df['years'] = df['date'].dt.strftime("%Y")
+    df['months'] = df['date'].dt.strftime("%m")
+    df['days'] = df['date'].dt.strftime("%A")
+    df['daysn'] = df['date'].dt.strftime("%d")
+    df['hours'] = df['date'].dt.strftime("%H").astype(int)
+    df['temp'] = df.predicted_mean.values.astype(int)
+    df = df[df['hours'] % 3 == 0]
+    return df.drop(columns=['predicted_mean'])
+def to_dict(df):
+	data_dict = {}
+	# Loop through years
+	for year in df['years'].unique():
+		data_dict[year] = {}
+		# Filter data for the current year
+		year_data = df[df['years'] == year]
+		# Loop through months
+		for month in year_data['months'].unique():
+			data_dict[year][month] = {}
+			# Filter data for the current month
+			month_data = year_data[year_data['months'] == month]
+			# Loop through unique dates
+			for date in month_data['daysn'].unique():
+				data_dict[year][month][date] = {}
+				# Filter data for the current date
+				date_data = month_data[month_data['daysn'] == date]
+				# Get the name of the day
+				day_name = date_data['days'].values[0]
+				data_dict[year][month][date]['day_name'] = day_name
+				# Loop through the hours and store the corresponding temperature
+				for hour in date_data['hours'].unique():
+					temp = date_data[date_data['hours'] == hour]['temp'].values[0]
+					data_dict[year][month][date][hour] = temp
+	return data_dict

programs/map.py ADDED Viewed

	@@ -0,0 +1,31 @@

+import folium
+from .load_data import *
+# Data Forecast
+bkl_data = 'data/forecast/bkl_temp_forecast.csv'
+sby_data = 'data/forecast/sby_temp_forecast.csv'
+# Generate map
+sby_coords = [-7.246, 112.738]
+bkl_coords = [-7.030, 112.747]
+map = folium.Map(location=[-7.169,112.779], zoom_start=11, max_zoom=16, min_zoom=10,
+                 tiles="https://{s}.basemaps.cartocdn.com/light_nolabels/{z}/{x}/{y}.png",
+                 attr='&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors & <a href="https://carto.com/attributions">CARTO</a>'
+)
+def get_forecast_data():
+    sby_timeseries, sby_temp = get_data(sby_data)
+    bkl_timeseries, bkl_temp = get_data(bkl_data)
+    return\
+        sby_timeseries, sby_temp,\
+        bkl_timeseries, bkl_temp
+def get_combined_data():
+    sby_data_all = to_dict(get_data_all(bkl_data))
+    bkl_data_all = to_dict(get_data_all(bkl_data))
+    return\
+        sby_data_all, \
+        bkl_data_all

programs/predict_cuaca.py ADDED Viewed

	@@ -0,0 +1,130 @@

+import requests
+from datetime import datetime
+import pandas as pd
+import joblib
+def predict_sby():
+    api = 'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/Surabaya,ID/today?unitGroup=metric&include=hours&key=AQCL3EG5SNW9XDN44A67J95UB'
+    response = requests.get(api)
+    if response.status_code == 200:
+        weather_data = response.json()
+    else:
+        print(f"Error: {response.status_code}")
+        print(response.text)
+    rounded_current_hour = datetime.now().replace(minute=0, second=0, microsecond=0).strftime('%H:%M:%S')  # Misal: '01:00:00'
+    print(f"Rounded current hour: {rounded_current_hour}")
+    selected_hour = next((hour for hour in weather_data['days'][0]['hours'] if hour['datetime'] == rounded_current_hour), None)
+    sby_weather = pd.DataFrame(selected_hour, index=[0])
+    df_tes = sby_weather.copy()
+    df_tes.drop(columns=['snowdepth', 'snow', 'preciptype', 'precipprob', 'precip', 'datetime', 'icon', 'stations', 'source', 'datetime', 'datetimeEpoch', 'solarradiation', 'solarenergy', 'uvindex' , 'severerisk', 'pressure'], inplace=True)
+    label_mapping = {
+        'Partially cloudy': 'Cloudy',
+        'Rain, Partially cloudy': 'Rain',
+        'Overcast': 'Cloudy',
+        'Rain, Overcast': 'Rain',
+        'Clear': 'Clear',
+        'Rain': 'Rain'
+    }
+    # Mengganti label pada kolom 'conditions'
+    df_tes['conditions'] = df_tes['conditions'].map(label_mapping)
+    label_encoder = joblib.load('data/predict/label_encoder.pkl')
+    model = joblib.load('data/predict/model_rf.pkl')
+    df_tes['conditions'] = label_encoder.transform(df_tes['conditions'])
+    df_tes = df_tes[['temp', 'feelslike', 'dew', 'humidity', 'windgust', 'windspeed',
+    'winddir', 'cloudcover', 'visibility']]
+    predicted_class_origin = model.predict(df_tes)
+    predicted_class_decode = label_encoder.inverse_transform(predicted_class_origin.reshape(-1))
+    if predicted_class_decode[0] == 'Cloudy':
+        pred_logo = 'static/sun-cloudy.png'
+        pred_color = '#BCCCDC'
+        pred_rgb = 'rgba(188, 204, 220, 0.2)'
+        pred_icon = 'fa-solid fa-cloud'
+    elif predicted_class_decode[0] == 'Clear':
+        pred_logo = 'static/sun.png'
+        pred_color = '#B1F0F7'
+        pred_rgb = 'rgba(177, 240, 247, 0.2)'
+        pred_icon = 'fa-solid fa-sun'
+    elif predicted_class_decode[0] == 'Rain':
+        pred_logo = 'static/rain.png'
+        pred_color = '#63839c'
+        pred_rgb = 'rgba(99, 131, 156, 0.2)'
+        pred_icon = 'fa-solid fa-cloud-showers-heavy'
+    return predicted_class_decode[0], pred_logo, pred_color, pred_rgb, df_tes, pred_icon
+def predict_bangkalan():
+    api = 'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/Bangkalan,ID/today?unitGroup=metric&include=hours&key=AQCL3EG5SNW9XDN44A67J95UB'
+    response = requests.get(api)
+    if response.status_code == 200:
+        weather_data = response.json()
+    else:
+        print(f"Error: {response.status_code}")
+        print(response.text)
+    rounded_current_hour = datetime.now().replace(minute=0, second=0, microsecond=0).strftime('%H:%M:%S')  # Misal: '01:00:00'
+    print(f"Rounded current hour: {rounded_current_hour}")
+    selected_hour = next((hour for hour in weather_data['days'][0]['hours'] if hour['datetime'] == rounded_current_hour), None)
+    sby_weather = pd.DataFrame(selected_hour, index=[0])
+    df_tes = sby_weather.copy()
+    df_tes.drop(columns=['snowdepth', 'snow', 'preciptype', 'precipprob', 'precip', 'datetime', 'icon', 'stations', 'source', 'datetime', 'datetimeEpoch', 'solarradiation', 'solarenergy', 'uvindex' , 'severerisk', 'pressure'], inplace=True)
+    label_mapping = {
+        'Partially cloudy': 'Cloudy',
+        'Rain, Partially cloudy': 'Rain',
+        'Overcast': 'Cloudy',
+        'Rain, Overcast': 'Rain',
+        'Clear': 'Clear',
+        'Rain': 'Rain'
+    }
+    # Mengganti label pada kolom 'conditions'
+    df_tes['conditions'] = df_tes['conditions'].map(label_mapping)
+    label_encoder = joblib.load('data/predict/label_encoder_bgkln.pkl')
+    model = joblib.load('data/predict/model_rf_bgkln.pkl')
+    df_tes['conditions'] = label_encoder.transform(df_tes['conditions'])
+    df_tes = df_tes[['temp', 'feelslike', 'dew', 'humidity', 'windgust', 'windspeed',
+    'winddir', 'cloudcover', 'visibility']]
+    predicted_class_origin = model.predict(df_tes)
+    predicted_class_decode = label_encoder.inverse_transform(predicted_class_origin.reshape(-1))
+    if predicted_class_decode[0] == 'Cloudy':
+        pred_logo = 'static/sun-cloudy.png'
+        pred_color = '#BCCCDC'
+        pred_rgb = 'rgba(188, 204, 220, 0.2)'
+        pred_icon = 'fa-solid fa-cloud'
+    elif predicted_class_decode[0] == 'Clear':
+        pred_logo = 'static/sun.png'
+        pred_color = '#B1F0F7'
+        pred_rgb = 'rgba(177, 240, 247, 0.2)'
+        pred_icon = 'fa-solid fa-sun'
+    elif predicted_class_decode[0] == 'Rain':
+        pred_logo = 'static/rain.png'
+        pred_color = '#63839c'
+        pred_rgb = 'rgba(99, 131, 156, 0.2)'
+        pred_icon = 'fa-solid fa-cloud-showers-heavy'
+    return predicted_class_decode[0], pred_logo, pred_color, pred_rgb, df_tes, pred_icon

requirements.txt ADDED Viewed

	@@ -0,0 +1,8 @@

+folium
+jinja2
+requests
+pandas
+flask
+tensorflow
+pillow
+numpy

static/cloudicon.png ADDED Viewed

static/clouds.png ADDED Viewed

static/feelslike.png ADDED Viewed

static/humidity.png ADDED Viewed

static/low-visibility.png ADDED Viewed

static/meteorology.png ADDED Viewed

static/rain.png ADDED Viewed

static/storm.png ADDED Viewed

static/sun-cloudy.png ADDED Viewed

static/sun.png ADDED Viewed

static/temp.png ADDED Viewed

static/weather-forecast.png ADDED Viewed

static/wind.png ADDED Viewed

templates/index.html ADDED Viewed

	@@ -0,0 +1,250 @@

+<!DOCTYPE html>
+<html>
+<head>
+    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+        <script>
+            L_NO_TOUCH = false;
+            L_DISABLE_3D = false;
+        </script>
+    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
+    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.js"></script>
+    <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"/>
+    <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap-glyphicons.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/css/all.min.css"/>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/font/bootstrap-icons.min.css">
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_38eb5ea6a30ae8bbf12217ea861d0106 {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+            </style>
+</head>
+<body>
+    <div class="info-time position-absolute fw-bold p-3 rounded font-size-1.2rem" style="top: 12px; left: 50px; z-index: 9999; background-color: #d49500; color:white;">
+        <span>{{ d_now }} {{ dname_now }} - {{ h_now }}:00</span>
+    </div>
+    <div data-bs-toggle="modal" data-bs-target="#klasifikasiCuaca" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 border-1"  style="top: 60px; right: 20px; z-index: 999; cursor: pointer;">
+        <span>Klasifikasi Cuaca</span>
+        <img src="{{ url_for('static', filename='cloudicon.png') }}" alt="icon cuaca" style="height: 50px;">
+    </div>
+    <a href="/" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 mt-4" style="top: 120px; right: 20px; z-index: 999">
+        <span class="me-2 text-dark" >Peramalan Cuaca</span>
+        <img src="{{ url_for('static', filename='weather-forecast.png') }}" alt="icon cuaca" style="height: 50px" />
+      </a>
+      <a href="/cuaca" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 mt-4" style="top: 190px; right: 20px; z-index: 999">
+        <span class="me-2 text-dark" >Prediksi Cuaca</span>
+        <img src="{{ url_for('static', filename='meteorology.png') }}" alt="icon cuaca" style="height: 50px" />
+      </a>
+      <a href="/cluster" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 mt-4" style="top: 260px; right: 20px; z-index: 999">
+        <span class="me-2 text-dark" >Tingkat Resiko Cuaca Ekstrim</span>
+        <img src="{{ url_for('static', filename='storm.png') }}" alt="icon cuaca" style="height: 50px" />
+      </a>
+    <!-- Modal -->
+    <div class="modal fade" id="klasifikasiCuaca" data-bs-backdrop="static" data-bs-keyboard="false" tabindex="-1" aria-labelledby="staticBackdropLabel" aria-hidden="true">
+        <div class="modal-dialog modal-dialog-centered">
+        <div class="modal-content">
+            <div class="modal-header">
+            <h1 class="modal-title fs-5" id="staticBackdropLabel">Klasifikasi Cuaca</h1>
+            <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+            </div>
+            <div class="modal-body">
+                Masukkan gambar yang ingin diklasifikasikan:
+                <p>
+                <form action="{{ url_for('home') }}" method="POST" enctype="multipart/form-data">
+                    <label for="file">Pilih Gambar:</label>
+                    <input type="file" class="form-control" name="file" required>
+                <hr>
+                {% if predicted_class %}
+                    Hasil prediksi dari: <img src="{{ url_for('static', filename='uploads/' + filename) }}" alt="Uploaded Image" style="max-width: 100%; height: auto;"><br>
+                    dengan cuaca: <b>{{ predicted_class }}</b>
+                {% endif %}
+            </div>
+            <div class="modal-footer">
+            <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">Close</button>
+            <button type="submit" class="btn btn-primary">Prediksi</button>
+            </form>
+            </div>
+        </div>
+        </div>
+    </div>
+    <!-- Map Canvas -->
+    <div class="folium-map" id="map_38eb5ea6a30ae8bbf12217ea861d0106" ></div>
+    <!-- Surabaya Temperature Forecasting -->
+    <div class="offcanvas offcanvas-bottom h-30" style="opacity: 0.8;" tabindex="-1" id="surabayaOffCanvas" aria-labelledby="offcanvasBottomLabel" data-bs-backdrop='false' >
+        <div class="offcanvas-header">
+            <h5 class="offcanvas-title" id="offcanvasBottomLabel"><b>Peramalan suhu Surabaya 2024</b></h5>
+            <button type="button" class="btn-close" data-bs-dismiss="offcanvas" aria-label="Close"></button>
+        </div>
+        <div class="offcanvas-body">
+            <div class="d-flex ps-5">
+                <div class="row flex-nowrap">
+                    <div class="col-auto">
+                        <div class="hourly-weather sticky-top" style="top: 0;">
+                            <div>Days</div>
+                            <div>Hours</div>
+                            <div>Celsius</div>
+                        </div>
+                    </div>
+                    <div class="d-flex flex-nowrap gap-5 ">
+                        {% for year, months in sby_data.items() %}
+                            {% for month, days in months.items() %}
+                                {% for day, data in days.items() %}
+                                <div class="day-column" style="display: inline-block; width: 240px; vertical-align: top;">
+                                    <h5 class="text-center fw-bold">{{ data['day_name'] }} {{ day }}</h5>
+                                    <div class="d-flex flex-wrap gap-3">
+                                        {% for hour, temp in data.items() if hour != 'day_name' %}
+                                        {% set current_hour = h_now | int %}
+                                        <div class="hourly-weather {% if day == d_now and hour == current_hour %}bg-warning rounded text-white'{% endif %}">
+                                            <div class="fw-bold">{{ hour }}</div>
+                                            <div>{{ temp }}°</div>
+                                        </div>
+                                        {% endfor %}
+                                    </div>
+                                    <div class="temperature-line"></div>
+                                </div>
+                                {% endfor %}
+                            {% endfor %}
+                        {% endfor %}
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+    <!-- Bangkalan Temperature Forecasting -->
+    <div class="offcanvas offcanvas-bottom h-30" style="opacity: 0.8;" tabindex="-1" id="bangkalanOffCanvas" aria-labelledby="offcanvasBottomLabel" data-bs-backdrop='false' >
+        <div class="offcanvas-header">
+            <h5 class="offcanvas-title" id="offcanvasBottomLabel"><b>Peramalan suhu Bangkalan 2024</b></h5>
+            <button type="button" class="btn-close" data-bs-dismiss="offcanvas" aria-label="Close"></button>
+        </div>
+        <div class="offcanvas-body">
+            <div class="d-flex ps-5">
+                <div class="row flex-nowrap">
+                    <div class="col-auto">
+                        <div class="hourly-weather sticky-top" style="top: 0;">
+                            <div>Days</div>
+                            <div>Hours</div>
+                            <div>Celsius</div>
+                        </div>
+                    </div>
+                    <div class="d-flex flex-nowrap gap-5 ">
+                        {% for year, months in bkl_data.items() %}
+                            {% for month, days in months.items() %}
+                                {% for day, data in days.items() %}
+                                <div class="day-column" style="display: inline-block; width: 240px; vertical-align: top;">
+                                    <h5 class="text-center fw-bold">{{ data['day_name'] }} {{ day }}</h5>
+                                    <div class="d-flex flex-wrap gap-3">
+                                        {% for hour, temp in data.items() if hour != 'day_name' %}
+                                        {% set current_hour = h_now | int %}
+                                        <div class="hourly-weather {% if day == d_now and hour == current_hour %}bg-warning rounded text-white{% endif %}">
+                                            <div class="fw-bold">{{ hour }}</div>
+                                            <div>{{ temp }}°</div>
+                                        </div>
+                                        {% endfor %}
+                                    </div>
+                                    <div class="temperature-line"></div>
+                                </div>
+                                {% endfor %}
+                            {% endfor %}
+                        {% endfor %}
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+    </body>
+    <script>
+        if ( window.history.replaceState ) {
+            window.history.replaceState( null, null, window.location.href );
+        }
+        let predicted = "{{ predicted_class }}"
+        document.addEventListener("DOMContentLoaded", function() {
+            if(predicted){
+                var myModal = new bootstrap.Modal(document.getElementById('klasifikasiCuaca'), {
+                    keyboard: false
+                });
+                myModal.show();
+            }
+        });
+        // Control map
+        var map_38eb5ea6a30ae8bbf12217ea861d0106 = L.map(
+            "map_38eb5ea6a30ae8bbf12217ea861d0106",
+            {
+                center: [-7.169, 112.779],
+                crs: L.CRS.EPSG3857,
+                zoom: 11,
+                zoomControl: true,
+                preferCanvas: false,
+            }
+        );
+        // Source map layout
+        var tile_layer_4fb0ad090cde9f05f658f5dc4c7863ec = L.tileLayer(
+            "https://{s}.basemaps.cartocdn.com/light_nolabels/{z}/{x}/{y}.png",
+            {"attribution": "\u0026copy; \u003ca href=\"https://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors \u0026 \u003ca href=\"https://carto.com/attributions\"\u003eCARTO\u003c/a\u003e", "detectRetina": false, "maxZoom": 16, "minZoom": 10, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+        );
+        tile_layer_4fb0ad090cde9f05f658f5dc4c7863ec.addTo(map_38eb5ea6a30ae8bbf12217ea861d0106);
+        // Marker Surabaya Coordinate
+        var marker = L.marker(
+            [-7.246, 112.738],
+            {}
+        ).addTo(map_38eb5ea6a30ae8bbf12217ea861d0106);
+        var div_icon = L.divIcon({"className": "empty", "html": "<div data-bs-toggle='offcanvas' data-bs-target='#surabayaOffCanvas' style='font-size: 14px; color: black;'><b>Surabaya {{ sby_temp[0].round(2) }}℃</b></div>"});
+        marker.setIcon(div_icon);
+        marker.on('click', function() {
+            $('#tempModal').modal('show');
+        });
+        // Marker Bangkalan Coordinate
+        var marker = L.marker(
+            [-7.03, 112.747],
+            {}
+        ).addTo(map_38eb5ea6a30ae8bbf12217ea861d0106);
+        var div_icon = L.divIcon({"className": "empty", "html": "<div data-bs-toggle='offcanvas' data-bs-target='#bangkalanOffCanvas' style='font-size: 14px; color: black;'><b>Bangkalan {{ bkl_temp[0].round(2) }}℃</b></div>"});
+        marker.setIcon(div_icon);
+        marker.on('click', function() {
+            $('#tempModal').modal('show');
+        });
+    </script>
+</html>

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templates/prediksi_cuaca.html ADDED Viewed

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+<!DOCTYPE html>
+<html>
+<head>
+    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+        <script>
+            L_NO_TOUCH = false;
+            L_DISABLE_3D = false;
+        </script>
+    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
+    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.js"></script>
+    <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"/>
+    <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap-glyphicons.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/css/all.min.css"/>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/font/bootstrap-icons.min.css">
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet-draw/dist/leaflet.draw.css" />
+    <script src="https://cdn.jsdelivr.net/npm/leaflet-draw/dist/leaflet.draw.js"></script>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.7.1/css/all.min.css">
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_38eb5ea6a30ae8bbf12217ea861d0106 {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+                .weather-marker {
+                    font-size: 14px;
+                    color: white;
+                    background: rgba(0, 123, 255, 0.8);
+                    border-radius: 10px;
+                    padding: 5px;
+                }
+            </style>
+</head>
+<body>
+    <!-- <div class="info-time position-absolute fw-bold p-3 rounded font-size-1.2rem" style="top: 12px; left: 50px; z-index: 9999; background-color: #d49500; color:white;">
+        <span>{{ d_now }} {{ dname_now }} - {{ h_now }}:00</span>
+    </div> -->
+    <div data-bs-toggle="modal" data-bs-target="#klasifikasiCuaca" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 border-1"  style="top: 60px; right: 20px; z-index: 999; cursor: pointer;">
+        <span class="me-2" >Klasifikasi Cuaca</span>
+        <img src="{{ url_for('static', filename='cloudicon.png') }}" alt="icon cuaca" style="height: 50px;">
+    </div>
+    <a href="/" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 mt-4" style="top: 120px; right: 20px; z-index: 999">
+        <span class="me-2 text-dark" >Peramalan Cuaca</span>
+        <img src="{{ url_for('static', filename='weather-forecast.png') }}" alt="icon cuaca" style="height: 50px" />
+      </a>
+      <a href="/cuaca" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 mt-4" style="top: 190px; right: 20px; z-index: 999">
+        <span class="me-2 text-dark" >Prediksi Cuaca</span>
+        <img src="{{ url_for('static', filename='meteorology.png') }}" alt="icon cuaca" style="height: 50px" />
+      </a>
+      <a href="/cluster" class="position-absolute shadow bg-body-tertiary fw-bold rounded p-1 mt-4" style="top: 260px; right: 20px; z-index: 999">
+        <span class="me-2 text-dark" >Tingkat Resiko Cuaca Ekstrim</span>
+        <img src="{{ url_for('static', filename='storm.png') }}" alt="icon cuaca" style="height: 50px" />
+      </a>
+    <!-- Modal -->
+    <div class="modal fade" id="klasifikasiCuaca" data-bs-backdrop="static" data-bs-keyboard="false" tabindex="-1" aria-labelledby="staticBackdropLabel" aria-hidden="true">
+        <div class="modal-dialog modal-dialog-centered">
+        <div class="modal-content">
+            <div class="modal-header">
+            <h1 class="modal-title fs-5" id="staticBackdropLabel">Klasifikasi Cuaca</h1>
+            <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+            </div>
+            <div class="modal-body">
+                Masukkan gambar yang ingin diklasifikasikan:
+                <p>
+                <form action="{{ url_for('home') }}" method="POST" enctype="multipart/form-data">
+                    <label for="file">Pilih Gambar:</label>
+                    <input type="file" class="form-control" name="file" required>
+                <hr>
+                {% if predicted_class %}
+                    Hasil prediksi dari: <img src="{{ url_for('static', filename='uploads/' + filename) }}" alt="Uploaded Image" style="max-width: 100%; height: auto;"><br>
+                    dengan cuaca: <b>{{ predicted_class }}</b>
+                {% endif %}
+            </div>
+            <div class="modal-footer">
+            <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">Close</button>
+            <button type="submit" class="btn btn-primary">Prediksi</button>
+            </form>
+            </div>
+        </div>
+        </div>
+    </div>
+    <!-- Map Canvas -->
+    <!-- <div class="folium-map" id="map_38eb5ea6a30ae8bbf12217ea861d0106" ></div> -->
+<div class="container-fluid py-2 sticky-top bg-body-tertiary">
+    <nav class="navbar navbar-expand-lg bg-body-tertiary">
+        <div class="container-fluid">
+            <div class="me-5 info-time fw-bold p-3 rounded font-size-1.2rem" style=" background-color: #d49500; color:white;">
+                <span>{{ d_now }} {{ dname_now }} - {{ h_now }}:00</span>
+            </div>
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNavAltMarkup" aria-controls="navbarNavAltMarkup" aria-expanded="false" aria-label="Toggle navigation">
+            <span class="navbar-toggler-icon"></span>
+          </button>
+          <div class="collapse navbar-collapse" id="navbarNavAltMarkup">
+            <div class="navbar-nav gap-3">
+                <a href="/cuaca?wilayah=surabaya" class="me-2 info-time fw-bold p-3 border border-1 border-light rounded font-size-1.2rem" style=" background-color: #213b5e; color:white;">
+                    Surabaya <i class="{{pred_icon_sby}} ms-2"></i>
+                </a>
+                <a href="/cuaca?wilayah=bangkalan" class="info-time fw-bold p-3 border border-1 border-light rounded font-size-1.2rem" style=" background-color: #213b5e; color:white;">
+                    Bangkalan <i class="{{pred_icon_bkl}} ms-2"></i>
+                </a>
+            </div>
+          </div>
+        </div>
+      </nav>
+</div>
+<div style="max-height: 300px;" class="container mt-4">
+    <div style="background-color: #213b5e; max-height: 300px;" class="row align-items-start w-75 rounded">
+        <div style="background-color: #213b5e;" class="col-6 rounded">
+            <div style="background-color: #213b5e;" class="h1 p-3 text-light" >
+                {{request.args.get('wilayah')}}
+            </div>
+            <div class="d-flex flex-row row mt-3">
+                <div class="col-3" ><img style="height: 100px; width: 100px;" src="{{pred_logo_weather}}" alt="" srcset=""></div>
+                <div class="col-9 text-light" ><h2>{{pred_class_weather}}</h2>
+                <h2>{{df_weather['temp'][0]}} °C</h2></div>
+            </div>
+        </div>
+        <div class="col-6 p-0 rounded">
+            <div class="folium-map" id="map_38eb5ea6a30ae8bbf12217ea861d0106" style="height: 300px;"></div>
+        </div>
+    </div>
+</div>
+<div class="container mt-4 mb-4">
+    <h1>Weather Detail</h1>
+    <div style="background-color: #213b5e;" class="row align-items-start w-75 rounded text-light gap-3 p-3">
+        <div style="height: 200px; background-color: rgba(255, 255, 255, 0.15);" class="col rounded p-3">
+            <div class="h2">Temperature</div>
+            <div class="flex-row row mt-5" >
+                <img class="col-6" style="height: 80px; width: 80px;" src="static/temp.png" alt="" srcset="">
+                <div class="col-8 fs-1 d-flex align-items-center" >
+                    <div style="font-size: 4rem;">{{df_weather['temp'][0]}} °c</div>
+                </div>
+            </div>
+          </div>
+          <div style="height: 200px; background-color: rgba(255, 255, 255, 0.15);" class="col rounded p-3">
+            <div class="h2">Feels Like</div>
+            <div class="flex-row row mt-5" >
+                <img class="col-6" style="height: 80px; width: 80px;" src="static/feelslike.png" alt="" srcset="">
+                <div class="col-8 fs-1 d-flex align-items-center" >
+                    <div style="font-size: 4rem;">{{df_weather['feelslike'][0]}} °c</div>
+                </div>
+            </div>
+          </div>
+          <div style="height: 200px; background-color: rgba(255, 255, 255, 0.15);" class="col rounded p-3">
+            <div class="h2">Humidity</div>
+            <div class="flex-row row mt-5" >
+                <img class="col-6" style="height: 80px; width: 80px;" src="static/humidity.png" alt="" srcset="">
+                <div class="col-8 fs-1 d-flex align-items-center" >
+                    <div style="font-size: 4rem;">{{df_weather['humidity'][0]}} %</div>
+                </div>
+            </div>
+          </div>
+    </div>
+          <div style="background-color: #213b5e;" class="row align-items-start w-75 rounded text-light gap-3 mt-2 p-3">
+            <div style="height: 200px; background-color: rgba(255, 255, 255, 0.15);" class="col rounded p-3">
+                <div class="h2">Wind</div>
+                <div class="flex-row row mt-5" >
+                    <img class="col-6" style="height: 80px; width: 80px;" src="static/wind.png" alt="" srcset="">
+                    <div class="col-8 fs-1 d-flex align-items-center" >
+                        <div style="font-size: 4rem;">3{{df_weather['windspeed'][0]}} kph</div>
+                    </div>
+                </div>
+              </div>
+              <div style="height: 200px; background-color: rgba(255, 255, 255, 0.15);" class="col rounded p-3">
+                <div class="h2">Cloud Cover</div>
+                <div class="flex-row row mt-5" >
+                    <img class="col-6" style="height: 80px; width: 80px;" src="static/clouds.png" alt="" srcset="">
+                    <div class="col-8 fs-1 d-flex align-items-center" >
+                        <div style="font-size: 4rem;">{{df_weather['cloudcover'][0]}} %</div>
+                    </div>
+                </div>
+              </div>
+              <div style="height: 200px; background-color: rgba(255, 255, 255, 0.15);" class="col rounded p-3">
+                <div class="h2">Visibility</div>
+                <div class="flex-row row mt-5" >
+                    <img class="col-6" style="height: 80px; width: 80px;" src="static/low-visibility.png" alt="" srcset="">
+                    <div class="col-8 fs-1 d-flex align-items-center" >
+                        <div style="font-size: 4rem;">{{df_weather['visibility'][0]}} km</div>
+                    </div>
+                </div>
+              </div>
+          </div>
+    </div>
+</div>
+<div class="h-25" ></div>
+    </body>
+    <script>
+        if ( window.history.replaceState ) {
+            window.history.replaceState( null, null, window.location.href );
+        }
+        let predicted = "{{ predicted_class }}"
+        document.addEventListener("DOMContentLoaded", function() {
+            if(predicted){
+                var myModal = new bootstrap.Modal(document.getElementById('klasifikasiCuaca'), {
+                    keyboard: false
+                });
+                myModal.show();
+            }
+        });
+        var map_38eb5ea6a30ae8bbf12217ea861d0106 = L.map(
+            "map_38eb5ea6a30ae8bbf12217ea861d0106",
+            {
+                center: ['{{json_coor1}}', '{{json_coor2}}'],
+                crs: L.CRS.EPSG3857,
+                zoom: 10,
+                zoomControl: true,
+                preferCanvas: false,
+            }
+        );
+        // Source map layout
+        var tile_layer_colored = L.tileLayer(
+        "https://{s}.basemaps.cartocdn.com/rastertiles/voyager/{z}/{x}/{y}{r}.png",
+        {
+            attribution: "\u0026copy; \u003ca href=\"https://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors \u0026 \u003ca href=\"https://carto.com/attributions\"\u003eCARTO\u003c/a\u003e",
+            detectRetina: false,
+            maxZoom: 16,
+            minZoom: 10,
+            noWrap: false,
+            opacity: 1,
+            subdomains: "abc",
+            tms: false
+        }
+    );
+    tile_layer_colored.addTo(map_38eb5ea6a30ae8bbf12217ea861d0106);
+    var wilayahGeoJSON = JSON.parse('{{ json_geo | tojson | safe }}');
+    // Tambahkan GeoJSON untuk wilayah Surabaya
+    var wilayahLayer = L.geoJSON(wilayahGeoJSON, {
+        style: {
+            color: "{{ pred_color_weather }}",
+            weight: 2,
+            fillColor: "{{ pred_color_weather }}",
+            fillOpacity: 0.5
+        }
+    }).addTo(map_38eb5ea6a30ae8bbf12217ea861d0106);
+        var wilayahCenter = wilayahLayer.getBounds().getCenter();
+        var surabayaMarker = L.marker(wilayahCenter, {
+            icon: L.divIcon({
+                className: "weather-marker",
+                html: `<div style="text-align:center;">
+                    <b>{{request.args.get('wilayah')}}</b><br>
+                    <b><i class="{{pred_icon_weather}} me-2"></i></b>{{pred_class_weather}}
+                    </div>`,
+                iconSize: [80, 50]
+            })
+        });
+        wilayahLayer.addLayer(surabayaMarker);
+    </script>
+</html>