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FUTURE_AQI_v1.json

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+{"module": "keras", "class_name": "Sequential", "config": {"name": "sequential_1", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_shape": [null, 22], "dtype": "float32", "sparse": false, "name": "input_layer_1"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 22]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "units": 256, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": {"module": "keras.regularizers", "class_name": "L2", "config": {"l2": 0.001}, "registered_name": null}, "bias_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 128]}}, {"module": "keras.layers", "class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "rate": 0.3, "seed": null, "noise_shape": null}, "registered_name": null}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": {"module": "keras.regularizers", "class_name": "L2", "config": {"l2": 0.001}, "registered_name": null}, "bias_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 256]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "units": 3, "activation": "linear", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 128]}}], "build_input_shape": [null, 22]}, "registered_name": null, "build_config": {"input_shape": [null, 22]}, "compile_config": {"optimizer": {"module": "keras.optimizers", "class_name": "Adam", "config": {"name": "adam", "learning_rate": 0.00010000000474974513, "weight_decay": null, "clipnorm": null, "global_clipnorm": null, "clipvalue": null, "use_ema": false, "ema_momentum": 0.99, "ema_overwrite_frequency": null, "loss_scale_factor": null, "gradient_accumulation_steps": null, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07, "amsgrad": false}, "registered_name": null}, "loss": "mae", "loss_weights": null, "metrics": ["mae"], "weighted_metrics": null, "run_eagerly": false, "steps_per_execution": 1, "jit_compile": false}}

FUTURE_AQI_v1.weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73a4b6288d471be366832d6337e25ac55798beb7025943c025ed3b3c5f7e347d
+size 860320

app.py

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+import matplotlib
+matplotlib.use('Agg')  # Set non-GUI backend for Matplotlib
+
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from flask import Flask, render_template, request
+from keras.models import model_from_json
+import folium
+import json
+import requests
+from pathlib import Path
+from io import BytesIO
+import base64
+
+app = Flask(__name__)
+
+# Load the model
+def load_model(name):
+    with open(f"{name}.json", "r") as json_file:
+        loaded_model_json = json_file.read()
+    loaded_model = model_from_json(loaded_model_json)
+    
+    weights_file = f"{name}.weights.h5"
+    if not Path(weights_file).is_file():
+        raise FileNotFoundError(f"Weight file {weights_file} not found.")
+    
+    loaded_model.load_weights(weights_file)
+    print("Loaded model from disk")
+    return loaded_model
+
+#MAP CREATING
+# Function to create map
+def create_map(df, predicted_wind_speed, map_file):
+    # Extract latitude, longitude, and actual wind speed
+    latitude = df['lat'].iloc[0]
+    longitude = df['lon'].iloc[0]
+    actual_wind_speed = df['wind_kph'].iloc[0]
+
+    # Ensure single numeric values for plotting
+    if isinstance(actual_wind_speed, (list, np.ndarray)):
+        actual_wind_speed = actual_wind_speed[0]
+    if isinstance(predicted_wind_speed, (list, np.ndarray)):
+        predicted_wind_speed = predicted_wind_speed[0]
+
+    # Create folium map
+    m = folium.Map(location=[latitude, longitude], zoom_start=10)
+
+    # Create a bar chart comparing actual and predicted wind speeds
+    fig, ax = plt.subplots(figsize=(3, 3))
+    bars = ax.bar(['Actual', 'Predicted'], [actual_wind_speed, predicted_wind_speed], color=['blue', 'green'])
+    ax.set_ylabel('Wind Speed (km/h)')
+    ax.set_title('Wind Speed Comparison')
+
+    # Add labels inside each bar
+    for bar in bars:
+        yval = bar.get_height()  # Get the height of each bar (wind speed value)
+        # Position the label inside the bar, slightly adjusted to avoid overlap with the top
+        ax.text(bar.get_x() + bar.get_width() / 2, yval / 2, round(yval, 2), ha='center', va='center', color='white')
+
+    # Adjust layout
+    plt.tight_layout()
+
+    # Save chart to a BytesIO buffer in PNG format and encode to base64
+    buffer = BytesIO()
+    plt.savefig(buffer, format="png")
+    plt.close(fig)
+    buffer.seek(0)
+    image_base64 = base64.b64encode(buffer.read()).decode('utf-8')
+    
+    # HTML for the popup with base64 image
+    popup_html = f'<img src="data:image/png;base64,{image_base64}" alt="Wind Speed Comparison">'
+    popup = folium.Popup(html=popup_html, max_width=300)
+
+    # Add a marker to the map with popup containing the chart
+    folium.Marker(
+        location=[latitude, longitude],
+        popup=popup,
+        tooltip=df['location'].iloc[0]
+    ).add_to(m)
+
+    # Save the folium map as an HTML file
+    m.save(map_file)
+
+model = load_model('Wind_Model_New_v1')
+
+# Route to input form
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+# Prediction route
+@app.route('/predict', methods=['POST'])
+def predict():
+    latitude = float(request.form['latitude'])
+    longitude = float(request.form['longitude'])
+
+    api_key = "846ca0bb2fa144cdb7195352240711"  
+    base_url = "http://api.weatherapi.com/v1/current.json"
+    params = {"key": api_key, "q": f"{latitude},{longitude}"}
+    response = requests.get(base_url, params=params)
+
+    if response.status_code == 200:
+        current_weather = response.json()
+        extracted_data = {
+            'location': current_weather['location']['name'],
+            'lat': latitude,
+            'lon': longitude,        
+            'date': current_weather['location']['localtime'].split()[0],
+            'time': current_weather['location']['localtime'],
+            'temp_c': current_weather['current']['temp_c'],
+            'temp_f': current_weather['current']['temp_f'],
+            'is_day': current_weather['current']['is_day'],
+            'condition': current_weather['current']['condition']['text'],
+            'wind_mph': current_weather['current']['wind_mph'],
+            'wind_kph': current_weather['current']['wind_kph'],
+            'humidity': current_weather['current']['humidity'],
+            'cloud': current_weather['current']['cloud'],
+            'feelslike_c': current_weather['current']['feelslike_c'],
+            'feelslike_f': current_weather['current']['feelslike_f'],
+            'windchill_c': current_weather['current']['windchill_c'],
+            'windchill_f': current_weather['current']['windchill_f'],
+            'heatindex_c': current_weather['current']['heatindex_c'],
+            'heatindex_f': current_weather['current']['heatindex_f'],
+            'dewpoint_c': current_weather['current']['dewpoint_c'],
+            'dewpoint_f': current_weather['current']['dewpoint_f'],
+            'vis_km': current_weather['current']['vis_km'],
+            'vis_miles': current_weather['current']['vis_miles'],
+            'gust_mph': current_weather['current']['gust_mph'],
+            'gust_kph': current_weather['current']['gust_kph'],
+            'uv': current_weather['current']['uv']
+        }
+        df = pd.DataFrame([extracted_data])
+        print("dataframe",df)
+        df['time'] = pd.to_datetime(df['time'])
+        df['day'] = df['time'].dt.day
+        df['month'] = df['time'].dt.month
+        df['hour'] = df['time'].dt.hour
+
+        dx=df[['temp_c', 'temp_f', 'is_day','wind_mph', 'wind_kph', 'humidity', 'cloud', 'feelslike_c',
+        'feelslike_f', 'windchill_c', 'windchill_f', 'heatindex_c','heatindex_f', 'dewpoint_c', 
+        'dewpoint_f', 'vis_km', 'vis_miles','gust_mph', 'gust_kph', 'uv', 'day', 'month', 'hour']]
+        x=dx.drop(['wind_mph', 'wind_kph'],axis=1)
+        x_unk = np.array(x)
+        x_unk = x_unk / 100
+        predictions = model.predict(x_unk)
+        predicted_wind_speed = predictions[0][0]
+        print("predictions",predictions)
+        map_file = "static/multiple_wind_speed_map.html"
+        create_map(df, predicted_wind_speed, map_file)
+
+        return render_template('results.html',
+                               actual=df['wind_kph'][0],
+                               predicted=predicted_wind_speed,
+                               map_file='multiple_wind_speed_map.html')
+    else:
+        return "Failed to retrieve weather data."
+
+# forcast 
+@app.route('/forcast', methods=['POST'])
+def forcast():
+    latitude = float(request.form['latitude'])
+    longitude = float(request.form['longitude'])
+
+    api_key = "846ca0bb2fa144cdb7195352240711"  
+    base_url = "http://api.weatherapi.com/v1/forecast.json"
+    params = {
+            "key": api_key,
+            "q": f"{latitude},{longitude}",
+            "days": 1  # Fetch forecast for 1 day
+    }
+    response = requests.get(base_url, params=params)
+    extracted_data = {}
+    if response.status_code == 200:
+        data = response.json()
+        for location in data:
+            location_name = data['location']['name']
+            forecast_days = data['forecast']['forecastday']
+            # Loop through each day in forecastdays
+            for day in forecast_days:
+                date = day['date']
+                hours = day['hour']
+                # Extract hourly data for each hour
+                for hour_data in hours:
+                    # Add extra details (date, location) for each hourly data
+                    extracted_data={
+                        "location": location_name,
+                        "lat": latitude,
+                        "lon": longitude,   
+                        "date": date,
+                        "time": hour_data["time"],
+                        "temp_c": hour_data["temp_c"],
+                        "temp_f": hour_data["temp_f"],
+                        "is_day": hour_data["is_day"],
+                        "condition": hour_data["condition"]["text"],
+                        "wind_mph": hour_data["wind_mph"],
+                        "wind_kph": hour_data["wind_kph"],
+                        "humidity": hour_data["humidity"],
+                        "cloud": hour_data["cloud"],
+                        "feelslike_c": hour_data["feelslike_c"],
+                        "feelslike_f": hour_data["feelslike_f"],
+                        "windchill_c": hour_data["windchill_c"],
+                        "windchill_f": hour_data["windchill_f"],
+                        "heatindex_c": hour_data["heatindex_c"],
+                        "heatindex_f": hour_data["heatindex_f"],
+                        "dewpoint_c": hour_data["dewpoint_c"],
+                        "dewpoint_f": hour_data["dewpoint_f"],
+                        "vis_km": hour_data["vis_km"],
+                        "vis_miles": hour_data["vis_miles"],
+                        "gust_mph": hour_data["gust_mph"],
+                        "gust_kph": hour_data["gust_kph"],
+                        "uv": hour_data["uv"]
+                    }
+
+        # Convert extracted data into a DataFrame
+        df = pd.DataFrame([extracted_data])
+        print("dataframe", df)
+        # Extracting additional date/time features
+        df['time']=pd.to_datetime(df['time'])
+        df['datetime']=pd.to_datetime(df['time'])
+        df['day'] = df['datetime'].dt.day       # Extracts day of the month
+        df['month'] = df['datetime'].dt.month   # Extracts month
+        df['hour'] = df['datetime'].dt.hour
+        # Prepare features for prediction
+        dx=df[['temp_c', 'temp_f', 'is_day','wind_mph', 'wind_kph', 'humidity', 'cloud', 'feelslike_c','feelslike_f', 'windchill_c', 'windchill_f', 'heatindex_c','heatindex_f', 'dewpoint_c', 'dewpoint_f', 'vis_km', 'vis_miles','gust_mph', 'gust_kph', 'uv', 'day', 'month', 'hour']]
+        x = dx.drop(['wind_mph', 'wind_kph'], axis=1)
+        x_unk = np.array(x)
+        x_unk = x_unk / 110
+        predictions = model.predict(x_unk)
+        predicted_wind_speed = predictions[0][0]
+        print("predictions",predictions)
+        map_file = "static/multiple_wind_speed_map.html"
+        create_map(df, predicted_wind_speed, map_file)
+
+        return render_template('results.html',
+                               actual=df['wind_kph'][0],
+                               predicted=predicted_wind_speed,
+                               map_file='multiple_wind_speed_map.html')
+    else:
+        return "Failed to retrieve weather data."
+
+if __name__ == '__main__':
+    app.run(debug=True)

app3.py

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+from flask import Flask, render_template, request, redirect, url_for
+import pandas as pd
+import numpy as np
+import joblib
+import requests
+from keras.models import model_from_json
+import folium
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+import os
+from collections import defaultdict
+
+app = Flask(__name__)
+
+# Load model and scalers
+def load_model(name):
+    with open(f"{name}.json", "r") as json_file:
+        loaded_model_json = json_file.read()
+    model = model_from_json(loaded_model_json)
+    model.load_weights(f"{name}.weights.h5")
+    return model
+
+model = load_model("FUTURE_AQI_v1")
+scaler_X = joblib.load('scaler_X_cpcb_4.pkl')
+scaler_y = joblib.load('scaler_y_cpcb_4.pkl')
+
+API_KEY = "26daca1b78f44099a755b921be4bfcf1"
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/forecast', methods=['POST'])
+def forecast():
+    # Get user input
+    latitude = float(request.form['latitude'])
+    longitude = float(request.form['longitude'])
+
+    # Fetch current AQI from API
+    current_url = f"https://api.weatherbit.io/v2.0/current/airquality?lat={latitude}&lon={longitude}&key={API_KEY}"
+    response = requests.get(current_url)
+    if response.status_code == 200:
+        current_data = response.json()['data'][0]
+
+    # Prepare input for the model
+    now = pd.to_datetime("now")
+    input_data = pd.DataFrame([{
+        'PM2.5': current_data['pm25'],
+        'PM10': current_data['pm10'],
+        'NO2': current_data['no2'],
+        'SO2': current_data['so2'],
+        'CO': current_data['co'],
+        'AQI': current_data['aqi'],
+        'Day': now.day,
+        'Month': now.month,
+        'Hour': now.hour
+    }])
+
+    # Scale and predict
+    input_scaled = scaler_X.transform(input_data)
+    predictions = model.predict(input_scaled)
+    predictions_actual = scaler_y.inverse_transform(predictions)
+
+    # Fetch forecasted AQI from API
+    forecast_url = f"https://api.weatherbit.io/v2.0/forecast/airquality?lat={latitude}&lon={longitude}&key={API_KEY}"
+    response = requests.get(forecast_url)
+    forecast_data = response.json()['data']
+
+    grouped_aqi = defaultdict(list)
+    for entry in forecast_data:
+        date = entry['datetime'].split(':')[0]
+        grouped_aqi[date].append(entry['aqi'])
+
+    api_predictions = {date: max(values) for date, values in grouped_aqi.items()}
+
+    # Save results to CSV
+    forecast_df = pd.DataFrame([{
+        **input_data.iloc[0],
+        'lat': latitude,
+        'lon': longitude,
+        'AQI_step_1': predictions_actual[0, 0],
+        'AQI_step_2': predictions_actual[0, 1],
+        'AQI_step_3': predictions_actual[0, 2]
+    }])
+    forecast_df.to_csv('aqi_data.csv', mode='a', header=False, index=False)
+
+    api_df = pd.DataFrame([{
+        'AQI_currrent_API': current_data['aqi'],
+        'AQI_step_1_API': api_predictions.get(list(api_predictions.keys())[0], None),
+        'AQI_step_2_API': api_predictions.get(list(api_predictions.keys())[1], None),
+        'AQI_step_3_API': api_predictions.get(list(api_predictions.keys())[2], None)
+    }])
+    api_df.to_csv('aqi_data_actual_api.csv', mode='a', header=False, index=False)
+
+    # Generate updated map and return
+    generate_map()
+    return redirect(url_for('result'))
+
+@app.route('/result')
+def result():
+    return render_template('result.html')
+
+def generate_map():
+    # Load data
+    df1 = pd.read_csv('aqi_data.csv')
+    df2 = pd.read_csv('aqi_data_actual_api.csv')
+    data = pd.concat([df1, df2], axis=1)
+
+    # Create Folium map
+    # Create the Folium map
+    map_center = [data['lat'].mean(), data['lon'].mean()]
+    m = folium.Map(location=map_center, zoom_start=10)
+
+    # AQI Color Legend
+    legend_html = """
+    <div style="
+        position: fixed; 
+        bottom: 20px; left: 20px; width: 350px; height: 225px; 
+        background-color: white; 
+        z-index:9999; font-size:14px; border:2px solid grey; 
+        padding: 10px; overflow-y: auto;">
+        <b>AQI Color Legend</b>
+        <table style="width: 100%; border-collapse: collapse; text-align: left;">
+            <thead>
+                <tr style="border-bottom: 2px solid grey;">
+                    <th style="padding: 5px;">Color</th>
+                    <th style="padding: 5px;">Remark</th>
+                    <th style="padding: 5px;">Range</th>
+                </tr>
+            </thead>
+            <tbody>
+                <tr>
+                    <td><i style="background:green; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                    <td>Good</td>
+                    <td>0-50</td>
+                </tr>
+                <tr>
+                    <td><i style="background:yellow; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                    <td>Moderate</td>
+                    <td>51-100</td>
+                </tr>
+                <tr>
+                    <td><i style="background:orange; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                    <td>Unhealthy for Sensitive Groups</td>
+                    <td>101-150</td>
+                </tr>
+                <tr>
+                    <td><i style="background:red; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                    <td>Unhealthy</td>
+                    <td>151-200</td>
+                </tr>
+                <tr>
+                    <td><i style="background:purple; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                    <td>Very Unhealthy</td>
+                    <td>201-300</td>
+                </tr>
+                <tr>
+                    <td><i style="background:maroon; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                    <td>Hazardous</td>
+                    <td>301+</td>
+                </tr>
+            </tbody>
+        </table>
+    </div>
+    """
+    m.get_root().html.add_child(folium.Element(legend_html))
+    
+    for _, row in data.iterrows():
+        popup_html = create_plot(row)
+        color = get_color_for_aqi(row['AQI_step_1'])
+        folium.Marker(
+            location=[row["lat"], row["lon"]],
+            popup=folium.Popup(html=popup_html, max_width=500),
+            icon=folium.Icon(color=color)
+        ).add_to(m)
+
+    # Save the map
+    m.save('static/aqi_forecast_with_legend.html')
+
+def create_plot(data):
+    # Bar plot generation logic (same as before)
+    fig, ax = plt.subplots(figsize=(5, 2))
+    categories = ['DAY 1', 'DAY 2', 'DAY 3']
+    actual_values = [data['AQI_step_1'], data['AQI_step_2'], data['AQI_step_3']]
+    api_values = [data['AQI_step_1_API'], data['AQI_step_2_API'], data['AQI_step_3_API']]
+
+    bar_width = 0.35
+    index = range(len(categories))
+
+    # Plot horizontal bars
+    bars_actual = ax.barh(index, actual_values, bar_width, label="Model AQI", color='blue')
+    bars_api = ax.barh([i + bar_width for i in index], api_values, bar_width, label="API AQI", color='green')
+
+    # Add values to each bar
+    max_value = 0  # Track the maximum value for axis limit adjustment
+    for bar in bars_actual:
+        value = bar.get_width()
+        ax.text(value + 2, bar.get_y() + bar.get_height() / 2, 
+                f'{value:.1f}', va='center', fontsize=10)
+        max_value = max(max_value, value)
+    for bar in bars_api:
+        value = bar.get_width()
+        ax.text(value + 2, bar.get_y() + bar.get_height() / 2, 
+                f'{value:.1f}', va='center', fontsize=10)
+        max_value = max(max_value, value)
+
+    # Adjust x-axis limits to accommodate annotations
+    ax.set_xlim(0, max_value * 1.2)
+
+    # Customize y-ticks and labels
+    ax.set_yticks([i + bar_width / 2 for i in index])
+    ax.set_yticklabels(categories)
+    ax.set_xlabel('AQI')
+    ax.set_title('AQI Comparison')
+
+    # Place legend outside the plot area
+    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+    plt.tight_layout()
+
+    # Save the plot to a PNG image in memory
+    buffer = BytesIO()
+    plt.savefig(buffer, format="png", bbox_inches='tight')
+    plt.close(fig)
+    buffer.seek(0)
+
+    # Encode the image to base64 to embed it in the HTML
+    image_base64 = base64.b64encode(buffer.read()).decode()
+    return f'<img src="data:image/png;base64,{image_base64}">'
+
+def get_color_for_aqi(aqi_value):
+    # Color logic (same as before)
+    if aqi_value <= 50:
+        return 'green'
+    elif aqi_value <= 100:
+        return 'yellow'
+    elif aqi_value <= 150:
+        return 'orange'
+    elif aqi_value <= 200:
+        return 'red'
+    elif aqi_value <= 300:
+        return 'purple'
+    else:
+        return 'maroon'
+
+if __name__ == '__main__':
+    app.run(debug=True)

app_map.py

@@ -0,0 +1,246 @@
+from flask import Flask, render_template, request, redirect, url_for
+import pandas as pd
+import numpy as np
+import joblib
+import requests
+from keras.models import model_from_json
+import folium
+from collections import defaultdict
+import matplotlib
+matplotlib.use('Agg')  # Use non-GUI backend
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+import branca
+
+app = Flask(__name__)
+
+# Load model and scalers
+def load_model(name):
+    with open(f"{name}.json", "r") as json_file:
+        loaded_model_json = json_file.read()
+    model = model_from_json(loaded_model_json)
+    model.load_weights(f"{name}.weights.h5")
+    return model
+
+model = load_model("FUTURE_AQI_v1")
+scaler_X = joblib.load('scaler_X_cpcb_4.pkl')
+scaler_y = joblib.load('scaler_y_cpcb_4.pkl')
+
+API_KEY = "26daca1b78f44099a755b921be4bfcf1"
+
+# Route to display the form and the map
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    map_html = generate_map() 
+    if request.method == 'POST':
+        # Get user input from form
+        latitude = float(request.form['latitude'])
+        longitude = float(request.form['longitude'])
+
+        # Fetch current AQI from API
+        current_url = f"https://api.weatherbit.io/v2.0/current/airquality?lat={latitude}&lon={longitude}&key={API_KEY}"
+        response = requests.get(current_url)
+        if response.status_code == 200:
+            current_data = response.json()['data'][0]
+        
+        # Prepare input for the model
+        now = pd.to_datetime("now")
+        input_data = pd.DataFrame([{
+            'PM2.5': current_data['pm25'],
+            'PM10': current_data['pm10'],
+            'NO2': current_data['no2'],
+            'SO2': current_data['so2'],
+            'CO': current_data['co'],
+            'AQI': current_data['aqi'],
+            'Day': now.day,
+            'Month': now.month,
+            'Hour': now.hour
+        }])
+
+        # Scale and predict
+        input_scaled = scaler_X.transform(input_data)
+        predictions = model.predict(input_scaled)
+        predictions_actual = scaler_y.inverse_transform(predictions)
+
+        # Save results to CSV
+        forecast_df = pd.DataFrame([{
+            **input_data.iloc[0],
+            'lat': latitude,
+            'lon': longitude,
+            'AQI_step_1': predictions_actual[0, 0],
+            'AQI_step_2': predictions_actual[0, 1],
+            'AQI_step_3': predictions_actual[0, 2]
+        }])
+        forecast_df.to_csv('aqi_data.csv', mode='a', header=False, index=False)
+
+        # Generate the updated map
+        map_html = generate_map()
+
+    return render_template('aqi_forecast_with_legend.html', map_html=map_html)
+
+# Route to handle the forecast data submission and AQI prediction
+@app.route('/forecast', methods=['POST'])
+def forecast():
+    # Get user input from form
+    latitude = float(request.form['latitude'])
+    longitude = float(request.form['longitude'])
+
+    # Fetch current AQI from API
+    current_url = f"https://api.weatherbit.io/v2.0/current/airquality?lat={latitude}&lon={longitude}&key={API_KEY}"
+    response = requests.get(current_url)
+    if response.status_code == 200:
+        current_data = response.json()['data'][0]
+
+    # Prepare input for the model
+    now = pd.to_datetime("now")
+    input_data = pd.DataFrame([{
+        'PM2.5': current_data['pm25'],
+        'PM10': current_data['pm10'],
+        'NO2': current_data['no2'],
+        'SO2': current_data['so2'],
+        'CO': current_data['co'],
+        'AQI': current_data['aqi'],
+        'Day': now.day,
+        'Month': now.month,
+        'Hour': now.hour
+    }])
+
+    # Scale and predict
+    input_scaled = scaler_X.transform(input_data)
+    predictions = model.predict(input_scaled)
+    predictions_actual = scaler_y.inverse_transform(predictions)
+
+    # Fetch forecasted AQI from API
+    forecast_url = f"https://api.weatherbit.io/v2.0/forecast/airquality?lat={latitude}&lon={longitude}&key={API_KEY}"
+    response = requests.get(forecast_url)
+    forecast_data = response.json()['data']
+
+    # Group AQI data by date
+    grouped_aqi = defaultdict(list)
+    for entry in forecast_data:
+        date = entry['datetime'].split(':')[0]
+        grouped_aqi[date].append(entry['aqi'])
+
+    # Get the maximum AQI forecasted for the next days
+    api_predictions = {date: max(values) for date, values in grouped_aqi.items()}
+
+    # Save the forecasted AQI results to CSV
+    api_df = pd.DataFrame([{
+        'AQI_currrent_API': current_data['aqi'],
+        'AQI_step_1_API': api_predictions.get(list(api_predictions.keys())[0], None),
+        'AQI_step_2_API': api_predictions.get(list(api_predictions.keys())[1], None),
+        'AQI_step_3_API': api_predictions.get(list(api_predictions.keys())[2], None)
+    }])
+    api_df.to_csv('aqi_data_actual_api.csv', mode='a', header=False, index=False)
+
+    # Save the predicted results to CSV
+    forecast_df = pd.DataFrame([{
+        **input_data.iloc[0],
+        'lat': latitude,
+        'lon': longitude,
+        'AQI_step_1': predictions_actual[0, 0],
+        'AQI_step_2': predictions_actual[0, 1],
+        'AQI_step_3': predictions_actual[0, 2]
+    }])
+    forecast_df.to_csv('aqi_data.csv', mode='a', header=False, index=False)
+
+    # Generate the updated map
+    map_html = generate_map()
+
+    return render_template('aqi_forecast_with_legend.html', map_html=map_html)
+
+def generate_map(output_file='templates/aqi_forecast_with_legend.html'):
+    # Load data
+    df1 = pd.read_csv('aqi_data.csv', on_bad_lines='skip')
+    df2 = pd.read_csv('aqi_data_actual_api.csv', on_bad_lines='skip')
+    data = pd.concat([df1, df2], axis=1)
+
+    # Create Folium map
+    map_center = [data['lat'].mean(), data['lon'].mean()]
+    m = folium.Map(location=map_center, zoom_start=10)
+
+    # Add AQI data points
+    for _, row in data.iterrows():
+        popup_html = create_plot(row)
+        color = get_color_for_aqi(row['AQI_step_1'])
+        if color:  # Ensure a valid color is returned
+            folium.Marker(
+                location=[row["lat"], row["lon"]],
+                popup=folium.Popup(html=popup_html, max_width=500),
+                icon=folium.Icon(color=color)
+            ).add_to(m) 
+
+    # Return map as an HTML string
+    map_html = m._repr_html_()
+
+    # Save the map
+    #m.save(output_file)
+    return map_html
+
+def get_color_for_aqi(aqi_value):
+    if aqi_value <= 50:
+        return 'green'
+    elif aqi_value <= 100:
+        return 'lightgreen'
+    elif aqi_value <= 150:
+        return 'orange'
+    elif aqi_value <= 200:
+        return 'red'
+    elif aqi_value <= 300:
+        return 'purple'
+    else:
+        return 'gray'  # Supported Folium color
+
+def create_plot(data):
+    fig, ax = plt.subplots(figsize=(5, 2))
+    categories = ['DAY 1', 'DAY 2', 'DAY 3']
+    actual_values = [data['AQI_step_1'], data['AQI_step_2'], data['AQI_step_3']]
+    api_values = [data['AQI_step_1_API'], data['AQI_step_2_API'], data['AQI_step_3_API']]
+
+    bar_width = 0.35
+    index = range(len(categories))
+
+    # Plot horizontal bars
+    bars_actual = ax.barh(index, actual_values, bar_width, label="Model AQI", color='blue')
+    bars_api = ax.barh([i + bar_width for i in index], api_values, bar_width, label="API AQI", color='green')
+
+    # Add values to each bar
+    max_value = 0  # Track the maximum value for axis limit adjustment
+    for bar in bars_actual:
+        value = bar.get_width()
+        ax.text(value + 2, bar.get_y() + bar.get_height() / 2, 
+                f'{value:.1f}', va='center', fontsize=10)
+        max_value = max(max_value, value)
+    for bar in bars_api:
+        value = bar.get_width()
+        ax.text(value + 2, bar.get_y() + bar.get_height() / 2, 
+                f'{value:.1f}', va='center', fontsize=10)
+        max_value = max(max_value, value)
+
+    # Adjust x-axis limits to accommodate annotations
+    ax.set_xlim(0, max_value * 1.2)
+
+    # Customize y-ticks and labels
+    ax.set_yticks([i + bar_width / 2 for i in index])
+    ax.set_yticklabels(categories)
+    ax.set_xlabel('AQI')
+    ax.set_title('AQI Comparison')
+
+    # Place legend outside the plot area
+    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+    plt.tight_layout()
+
+    # Save the plot to a PNG image in memory
+    buffer = BytesIO()
+    plt.savefig(buffer, format="png", bbox_inches='tight')
+    plt.close(fig)
+    buffer.seek(0)
+
+    # Encode the image to base64 to embed it in the HTML
+    image_base64 = base64.b64encode(buffer.read()).decode()
+    return f'<img src="data:image/png;base64,{image_base64}">'
+
+if __name__ == '__main__':
+    app.run(debug=True)

app_test.py

@@ -0,0 +1,340 @@
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from pathlib import Path
+from keras.models import model_from_json
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import joblib
+import requests
+import json
+from datetime import datetime
+
+def load_model(name):
+    # Load JSON and create model
+    json_file = open("%s.json" % name, "r")
+    loaded_model_json = json_file.read()
+    json_file.close()
+    loaded_model = model_from_json(loaded_model_json)
+
+    # Check if the weights file exists before loading
+    weights_file = f"{name}.weights.h5"
+    if not Path(weights_file).is_file():
+        raise FileNotFoundError(f"Weight file {weights_file} not found.")
+    
+    # Load weights into the new model
+    loaded_model.load_weights(weights_file)
+    print("Loaded model from disk")
+    return loaded_model
+
+model = load_model("3_day_forecast_AQI_v5")
+
+####################################
+
+# Load the scalers
+scaler_X = joblib.load('scaler_X_AQI.pkl')
+scaler_y = joblib.load('scaler_y_AQI.pkl')
+
+import requests
+import pandas as pd
+import joblib
+import os
+from datetime import datetime
+# delhi  28.639638713652012, 77.19002000205269
+# bhopal 23.23731292701139, 77.44433463788636
+# ahemdabad 23.0364012974141, 72.58238347964425
+# ankleshwar 21.62880896774956, 73.0043990197163
+# jamnagar 22.3033564155508, 70.8012921707898
+# 21.22050672027795  72.83355967457062#  
+# 21.236796371788703, 72.8665479925569
+
+# Define API parameters
+api_key = "26daca1b78f44099a755b921be4bfcf1"  # Your WeatherAPI key
+latitude = 21.236796371788703  # Example latitude
+longitude = 72.8665479925569  #  # Example longitude
+base_url = f"https://api.weatherbit.io/v2.0/current/airquality?lat={latitude}&lon={longitude}&key={api_key}"
+
+# Make the API request
+response = requests.get(base_url)
+if response.status_code == 200:
+    data = response.json()
+
+# Extract forecast data
+dx = [data['data'][0]]
+test = pd.DataFrame(dx)
+
+# Add time-based features
+now = datetime.now()
+current_time = now.strftime("%Y-%m-%d %H:%M:%S")
+test = test[['pm25', 'pm10', 'no2', 'so2', 'co', 'aqi']]
+test['Date'] = pd.to_datetime(current_time)
+test['Day'] = test['Date'].dt.day
+test['Month'] = test['Date'].dt.month
+test['Hour'] = test['Date'].dt.hour
+test = test[['pm25', 'pm10', 'no2', 'so2', 'co', 'aqi', 'Day', 'Month', 'Hour']]
+test.columns = ['PM2.5', 'PM10', 'NO2', 'SO2', 'CO', 'AQI', 'Day', 'Month', 'Hour']
+
+# Load scalers
+scaler_X = joblib.load('scaler_X_AQI.pkl')
+scaler_y = joblib.load('scaler_y_AQI.pkl')
+
+# Standardize data and make predictions
+data_normalized = scaler_X.transform(test)
+prediction_Test = model.predict(data_normalized)
+predictions_actual = scaler_y.inverse_transform(prediction_Test)
+test['lat']= latitude
+test['lon']= longitude
+# Create a DataFrame for predictions
+pred = pd.DataFrame(predictions_actual, columns=['AQI_step_1', 'AQI_step_2', 'AQI_step_3'])
+df = pd.concat([test, pred], axis=1)
+
+# Define the CSV file path
+csv_file_path = "aqi_data.csv"
+
+# Create the CSV file with headers if it doesn't exist
+if not os.path.exists(csv_file_path):
+    columns = ['PM2.5', 'PM10', 'NO2', 'SO2', 'CO', 'AQI', 'Day', 'Month', 'Hour', 'lat', 'lon', 'AQI_step_1', 'AQI_step_2', 'AQI_step_3']
+    df_empty = pd.DataFrame(columns=columns)
+    df_empty.to_csv(csv_file_path, index=False)
+
+# Append new data to the existing CSV
+df.to_csv(csv_file_path, mode='a', index=False, header=False)
+
+print(f"Data appended to {csv_file_path}")
+
+
+####################################
+
+
+import requests
+import json
+from datetime import datetime
+
+# Define API parameters
+api_key = "26daca1b78f44099a755b921be4bfcf1"  # Your WeatherAPI key
+# 21.195069775800516,72.79324648126439
+# 21.22050672027795, 72.83355967457062
+# 28.639638713652012,77.19002000205269
+# 23.23731292701139,77.44433463788636,
+# 23.0364012974141,72.58238347964425,
+# 21.62880896774956,73.0043990197163,
+# jamnagar 22.3033564155508, 70.8012921707898
+# new delhi 28.619913380208967, 77.20633325621425
+
+latitude = 21.236796371788703  # Example latitude
+longitude = 72.8665479925569  #  # Example longitude
+
+base_url = f"https://api.weatherbit.io/v2.0/forecast/airquality?lat={latitude}&lon={longitude}&key={api_key}"
+
+# Make the API request
+response = requests.get(base_url)
+
+if response.status_code == 200:
+    # Parse the returned JSON data
+    data = response.json()
+    
+data
+
+data=data['data']
+
+from collections import defaultdict
+
+# Group AQI values by date (ignoring the hour)
+grouped_aqi = defaultdict(list)
+
+for entry in data:
+    # Extract date part only from the datetime (before the colon)
+    date = entry['datetime'].split(':')[0]
+    aqi = entry['aqi']
+    grouped_aqi[date].append(aqi)
+
+# Convert defaultdict to a regular dictionary
+grouped_aqi = dict(grouped_aqi)
+
+# Display the result
+print(grouped_aqi)
+index=11
+key= grouped_aqi.keys()
+samp={}
+samp.clear()
+for i in key:
+    print(i)
+    ls=grouped_aqi[i]
+    if index<len(ls):
+        print(ls[11])
+        samp[i]=ls[11]
+    else:
+        print(ls[-1])
+        samp[i]=ls[-1]
+print(samp)
+df = pd.DataFrame([samp])
+df.columns=['AQI_currrent','AQI_step_1', 'AQI_step_2', 'AQI_step_3']
+print(df)
+# Define the CSV file path
+csv_file_path = "aqi_data_actual_api.csv"
+
+# Create the CSV file with headers if it doesn't exist
+if not os.path.exists(csv_file_path):
+    columns = ['AQI_currrent_API','AQI_step_1_API', 'AQI_step_2_API', 'AQI_step_3_API']
+    df_empty = pd.DataFrame(columns=columns)
+    df_empty.to_csv(csv_file_path, index=False)
+    
+# Append new data to the existing CSV
+df.to_csv(csv_file_path, mode='a', index=False, header=False)
+
+
+##########################################################
+
+
+import folium
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+import pandas as pd
+
+
+df1= pd.read_csv('aqi_data.csv')
+df2= pd.read_csv('aqi_data_actual_api.csv')
+data = pd.concat([df1,df2],axis=1)
+data = data.head(3)
+# Create the Folium map
+map_center = [data['lat'].mean(), data['lon'].mean()]
+m = folium.Map(location=map_center, zoom_start=10)
+
+# AQI Color Legend
+legend_html = """
+<div style="
+    position: fixed; 
+    bottom: 20px; left: 20px; width: 350px; height: 225px; 
+    background-color: white; 
+    z-index:9999; font-size:14px; border:2px solid grey; 
+    padding: 10px; overflow-y: auto;">
+    <b>AQI Color Legend</b>
+    <table style="width: 100%; border-collapse: collapse; text-align: left;">
+        <thead>
+            <tr style="border-bottom: 2px solid grey;">
+                <th style="padding: 5px;">Color</th>
+                <th style="padding: 5px;">Remark</th>
+                <th style="padding: 5px;">Range</th>
+            </tr>
+        </thead>
+        <tbody>
+            <tr>
+                <td><i style="background:green; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td>Good</td>
+                <td>0-50</td>
+            </tr>
+            <tr>
+                <td><i style="background:yellow; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td>Moderate</td>
+                <td>51-100</td>
+            </tr>
+            <tr>
+                <td><i style="background:orange; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td>Unhealthy for Sensitive Groups</td>
+                <td>101-150</td>
+            </tr>
+            <tr>
+                <td><i style="background:red; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td>Unhealthy</td>
+                <td>151-200</td>
+            </tr>
+            <tr>
+                <td><i style="background:purple; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td>Very Unhealthy</td>
+                <td>201-300</td>
+            </tr>
+            <tr>
+                <td><i style="background:maroon; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td>Hazardous</td>
+                <td>301+</td>
+            </tr>
+        </tbody>
+    </table>
+</div>
+"""
+# Add the legend to the map
+legend = folium.Element(legend_html)
+m.get_root().html.add_child(legend)
+
+# Function to generate a horizontal bar plot
+def create_aqi_comparison_plot(data):
+    fig, ax = plt.subplots(figsize=(5, 2))
+    categories = ['DAY 1', 'DAY 2', 'DAY 3']
+    actual_values = [data['AQI_step_1'], data['AQI_step_2'], data['AQI_step_3']]
+    api_values = [data['AQI_step_1_API'], data['AQI_step_2_API'], data['AQI_step_3_API']]
+
+    bar_width = 0.35
+    index = range(len(categories))
+
+    # Plot horizontal bars
+    bars_actual = ax.barh(index, actual_values, bar_width, label="Model AQI", color='blue')
+    bars_api = ax.barh([i + bar_width for i in index], api_values, bar_width, label="API AQI", color='green')
+
+    # Add values to each bar
+    max_value = 0  # Track the maximum value for axis limit adjustment
+    for bar in bars_actual:
+        value = bar.get_width()
+        ax.text(value + 2, bar.get_y() + bar.get_height() / 2, 
+                f'{value:.1f}', va='center', fontsize=10)
+        max_value = max(max_value, value)
+    for bar in bars_api:
+        value = bar.get_width()
+        ax.text(value + 2, bar.get_y() + bar.get_height() / 2, 
+                f'{value:.1f}', va='center', fontsize=10)
+        max_value = max(max_value, value)
+
+    # Adjust x-axis limits to accommodate annotations
+    ax.set_xlim(0, max_value * 1.2)
+
+    # Customize y-ticks and labels
+    ax.set_yticks([i + bar_width / 2 for i in index])
+    ax.set_yticklabels(categories)
+    ax.set_xlabel('AQI')
+    ax.set_title('AQI Comparison')
+
+    # Place legend outside the plot area
+    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
+
+    plt.tight_layout()
+
+    # Save the plot to a PNG image in memory
+    buffer = BytesIO()
+    plt.savefig(buffer, format="png", bbox_inches='tight')
+    plt.close(fig)
+    buffer.seek(0)
+
+    # Encode the image to base64 to embed it in the HTML
+    image_base64 = base64.b64encode(buffer.read()).decode()
+    return f'<img src="data:image/png;base64,{image_base64}">'
+
+# Function to determine AQI marker color
+def get_color_for_aqi(aqi_value):
+    if aqi_value <= 50:
+        return 'green'
+    elif aqi_value <= 100:
+        return 'yellow'
+    elif aqi_value <= 150:
+        return 'orange'
+    elif aqi_value <= 200:
+        return 'red'
+    elif aqi_value <= 300:
+        return 'purple'
+    else:
+        return 'maroon'
+
+# Add markers with AQI comparison plot
+for _, row in data.iterrows():
+    color = get_color_for_aqi(row['AQI_step_1'])
+    popup_html = create_aqi_comparison_plot(row)
+    folium.Marker(
+        location=[row["lat"], row["lon"]],
+        popup=folium.Popup(html=popup_html, max_width=500),
+        #tooltip=row["name"],
+        icon=folium.Icon(color=color)
+    ).add_to(m)
+
+# Save the map
+m.save("aqi_forecast_with_legend.html")
+m

aqi_data.csv

@@ -0,0 +1,15 @@
+PM2.5,PM10,NO2,SO2,CO,AQI,Day,Month,Hour,lat,lon,AQI_step_1,AQI_step_2,AQI_step_3
+87,89,2,127,123.1396,192,20,11,11,21.195069775800516,72.79324648126439,174.50804,175.36728,176.44917
+86,89,7,65,138.3132,191,20,11,17,21.22050672027795,72.83355967457062,171.93951,172.93735,174.26135
+86,89,7,65,138.3132,191,20,11,17,21.236796371788703,72.8665479925569,171.93951,172.93735,174.26135
+84.0,88.0,3.0,89.0,132.4772,188.0,21.0,11.0,13.0,21.236796371788703,72.8665479925569,169.29886,170.1785,171.38548
+205.0,256.0,8.0,99.0,151.1524,205.0,21.0,11.0,13.0,28.639638713652012,77.19002000205269,178.70003,179.95726,181.62497
+54.0,58.0,2.0,40.0,124.8904,150.0,21.0,11.0,15.0,21.634633971544297,72.99221466910912,143.35014,143.9174,144.78899
+106.0,116.0,2.0,80.0,170.4112,217.0,21.0,11.0,15.0,26.846962942608172,80.90966630609816,196.3583,198.081,200.372
+35.0,37.0,3.0,40.0,141.2312,101.0,21.0,11.0,15.0,31.335876535998896,75.565248172424,111.138115,111.64326,112.21634
+34.0,36.0,5.0,95.0,139.4804,101.0,21.0,11.0,16.0,30.921956088524542,75.85192818745244,112.60642,113.12772,113.72198
+74.0,83.0,1.0,27.0,113.2184,175.0,21.0,11.0,16.0,23.238552725422572,77.43189201233218,157.19151,157.69708,158.61711
+89.0,111.0,3.0,27.0,104.4644,195.0,21.0,11.0,16.0,22.622719257719115,88.36320858480713,173.02531,173.67334,174.52841
+29.0,30.0,3.0,36.0,114.3856,87.0,21.0,11.0,16.0,11.301601687488285,77.19399206074418,94.49498,94.79229,95.16263
+15.0,17.0,0.0,2.0,82.8712,56.0,21.0,11.0,16.0,10.569512756592735,72.63877516454528,56.11896,56.067623,56.15389
+15.0,17.0,0.0,2.0,82.8712,56.0,21.0,11.0,16.0,10.569512756592735,72.63877516454528,56.11896,56.067623,56.15389

aqi_data_actual_api.csv

@@ -0,0 +1,15 @@
+AQI_currrent_API,AQI_step_1_API,AQI_step_2_API,AQI_step_3_API
+172,177,176,177
+171,176,178,188
+171,176,178,188
+188,189,203,225
+205,264,272,257
+150,150,173,167
+217,229,265,217
+101,241,252,262
+101,252,264,268
+175,191,184,167
+195,253,274,273
+87,163,163,153
+56,56,72,93
+56,56,72,93

requirement.txt

@@ -0,0 +1,116 @@
+absl-py==2.1.0
+accelerate==1.1.1
+aiohappyeyeballs==2.4.3
+aiohttp==3.11.2
+aiosignal==1.3.1
+asttokens==2.4.1
+astunparse==1.6.3
+async-timeout==5.0.1
+attrs==24.2.0
+blinker==1.9.0
+branca==0.8.0
+certifi==2024.8.30
+charset-normalizer==3.4.0
+click==8.1.7
+colorama==0.4.6
+comm==0.2.2
+contourpy==1.3.0
+cycler==0.12.1
+datasets==3.1.0
+debugpy==1.8.7
+decorator==5.1.1
+dill==0.3.8
+exceptiongroup==1.2.2
+executing==2.1.0
+filelock==3.16.1
+Flask==3.0.3
+flatbuffers==24.3.25
+folium==0.18.0
+fonttools==4.54.1
+frozenlist==1.5.0
+fsspec==2024.9.0
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.67.1
+h5py==3.12.1
+huggingface-hub==0.26.2
+idna==3.10
+ipykernel==6.29.5
+ipython==8.29.0
+itsdangerous==2.2.0
+jedi==0.19.1
+Jinja2==3.1.4
+joblib==1.4.2
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+keras==3.6.0
+kiwisolver==1.4.7
+libclang==18.1.1
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.9.2
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+ml-dtypes==0.4.1
+mpmath==1.3.0
+multidict==6.1.0
+multiprocess==0.70.16
+namex==0.0.8
+nest-asyncio==1.6.0
+networkx==3.4.2
+numpy==2.0.2
+opt_einsum==3.4.0
+optree==0.13.0
+packaging==24.1
+pandas==2.2.3
+parso==0.8.4
+pillow==11.0.0
+platformdirs==4.3.6
+prompt_toolkit==3.0.48
+propcache==0.2.0
+protobuf==5.28.3
+psutil==6.1.0
+pure_eval==0.2.3
+pyarrow==18.0.0
+Pygments==2.18.0
+pyparsing==3.2.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
+pywin32==308
+PyYAML==6.0.2
+pyzmq==26.2.0
+regex==2024.11.6
+requests==2.32.3
+rich==13.9.4
+safetensors==0.4.5
+scikit-learn==1.5.2
+scipy==1.14.1
+seaborn==0.13.2
+sentencepiece==0.2.0
+six==1.16.0
+stack-data==0.6.3
+sympy==1.13.1
+tensorboard==2.18.0
+tensorboard-data-server==0.7.2
+tensorflow==2.18.0
+termcolor==2.5.0
+tf_keras==2.18.0
+threadpoolctl==3.5.0
+tokenizers==0.20.3
+torch==2.5.1
+torchaudio==2.5.1
+torchvision==0.20.1
+tornado==6.4.1
+tqdm==4.67.0
+traitlets==5.14.3
+transformers==4.46.2
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+wcwidth==0.2.13
+Werkzeug==3.1.2
+wrapt==1.16.0
+xxhash==3.5.0
+xyzservices==2024.9.0
+yarl==1.17.1

scaler_X_cpcb_4.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:427ce69431d9fecc00f9f31668e87e575dff645ecfd188dc146d7a41728d1dd7
+size 1703

scaler_y_cpcb_4.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e2f48ec4bcaf215123873f416b60d9ffe080045913719a945e115131fe3b6eeb
+size 1023

static/aqi_forecast_map.html

@@ -0,0 +1,177 @@
+<!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"/>
+    
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_f96d7c068d70a2dfb86669b83d95543c {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+            </style>
+        
+</head>
+<body>
+    
+    
+            <div class="folium-map" id="map_f96d7c068d70a2dfb86669b83d95543c" ></div>
+        
+</body>
+<script>
+    
+    
+            var map_f96d7c068d70a2dfb86669b83d95543c = L.map(
+                "map_f96d7c068d70a2dfb86669b83d95543c",
+                {
+                    center: [21.2, 72.85],
+                    crs: L.CRS.EPSG3857,
+                    zoom: 10,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
+    
+            var tile_layer_2fced53561d97c46a3851d3ffbd06d32 = L.tileLayer(
+                "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
+                {"attribution": "\u0026copy; \u003ca href=\"https://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+            );
+        
+    
+            tile_layer_2fced53561d97c46a3851d3ffbd06d32.addTo(map_f96d7c068d70a2dfb86669b83d95543c);
+        
+    
+            var marker_6e96a811704e3a9dcaf0853acb663dee = L.marker(
+                [21.195069775800516, 72.79324648126439],
+                {}
+            ).addTo(map_f96d7c068d70a2dfb86669b83d95543c);
+        
+    
+            var icon_9d8e2b2ea6b79f929f60c2483341d300 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_6e96a811704e3a9dcaf0853acb663dee.setIcon(icon_9d8e2b2ea6b79f929f60c2483341d300);
+        
+    
+        var popup_d299bc7f89956c3e85741cba3f9cf026 = L.popup({"maxWidth": 500});
+
+        
+            
+                var html_9c6b4cf4128fd1a18f384a46847adc48 = $(`<div id="html_9c6b4cf4128fd1a18f384a46847adc48" style="width: 100.0%; height: 100.0%;"><img 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"></div>`)[0];
+                popup_d299bc7f89956c3e85741cba3f9cf026.setContent(html_9c6b4cf4128fd1a18f384a46847adc48);
+            
+        
+
+        marker_6e96a811704e3a9dcaf0853acb663dee.bindPopup(popup_d299bc7f89956c3e85741cba3f9cf026)
+        ;
+
+        
+    
+    
+            marker_6e96a811704e3a9dcaf0853acb663dee.bindTooltip(
+                `<div>
+                     Location 1
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_d41a1d89753128a0d648fe9a95670476 = L.marker(
+                [21.3, 72.9],
+                {}
+            ).addTo(map_f96d7c068d70a2dfb86669b83d95543c);
+        
+    
+            var icon_49ac172dbb970324b9bc82293c12de6c = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_d41a1d89753128a0d648fe9a95670476.setIcon(icon_49ac172dbb970324b9bc82293c12de6c);
+        
+    
+        var popup_d0739edffb2fb3b36cd822eab4c31669 = L.popup({"maxWidth": 500});
+
+        
+            
+                var html_1d63111637e0a5d02c02ff5848c3a1b1 = $(`<div id="html_1d63111637e0a5d02c02ff5848c3a1b1" style="width: 100.0%; height: 100.0%;"><img 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"></div>`)[0];
+                popup_d0739edffb2fb3b36cd822eab4c31669.setContent(html_1d63111637e0a5d02c02ff5848c3a1b1);
+            
+        
+
+        marker_d41a1d89753128a0d648fe9a95670476.bindPopup(popup_d0739edffb2fb3b36cd822eab4c31669)
+        ;
+
+        
+    
+    
+            marker_d41a1d89753128a0d648fe9a95670476.bindTooltip(
+                `<div>
+                     Location 2
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_2b4dd178b28f8dc64a6b58d3b2195568 = L.marker(
+                [21.1, 72.8],
+                {}
+            ).addTo(map_f96d7c068d70a2dfb86669b83d95543c);
+        
+    
+            var icon_904d715c3fdcf1bd3e2085c62379e5c8 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_2b4dd178b28f8dc64a6b58d3b2195568.setIcon(icon_904d715c3fdcf1bd3e2085c62379e5c8);
+        
+    
+        var popup_17f841170c403bf21955f085ac0694c3 = L.popup({"maxWidth": 500});
+
+        
+            
+                var html_ac0992d15caf4cab8b21de240372b018 = $(`<div id="html_ac0992d15caf4cab8b21de240372b018" style="width: 100.0%; height: 100.0%;"><img 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"></div>`)[0];
+                popup_17f841170c403bf21955f085ac0694c3.setContent(html_ac0992d15caf4cab8b21de240372b018);
+            
+        
+
+        marker_2b4dd178b28f8dc64a6b58d3b2195568.bindPopup(popup_17f841170c403bf21955f085ac0694c3)
+        ;
+
+        
+    
+    
+            marker_2b4dd178b28f8dc64a6b58d3b2195568.bindTooltip(
+                `<div>
+                     Location 3
+                 </div>`,
+                {"sticky": true}
+            );
+        
+</script>
+</html>

static/aqi_forecast_with_legend.html

@@ -0,0 +1,203 @@
+<!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"/>
+    
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_2c2025c360c3ed95ce6339b20a693bdd {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+            </style>
+        
+</head>
+<body>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AQI Forecast Map</title>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/css/bootstrap.min.css">
+    <style>
+        #legend {
+            position: fixed;
+            bottom: 20px;
+            left: 20px;
+            width: 300px;
+            background-color: rgba(255, 255, 255, 0.8);
+            z-index: 9999;
+            font-size: 12px;
+            border-radius: 8px;
+            padding: 10px;
+            box-shadow: 2px 2px 8px rgba(0, 0, 0, 0.3);
+        }
+    </style>
+       
+ <!-- Navigation Bar -->
+ <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
+    <div class="container-fluid">
+        <a class="navbar-brand" href="#">AQI Forecaster</a>
+        <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+            <span class="navbar-toggler-icon"></span>
+        </button>
+        <div class="collapse navbar-collapse" id="navbarNav">
+            <form class="d-flex ms-auto" method="POST" action="{{ url_for('forecast') }}">
+                <input class="form-control me-2" type="text" name="latitude" placeholder="Enter Latitude" required>
+                <input class="form-control me-2" type="text" name="longitude" placeholder="Enter Longitude" required>
+                <button class="btn btn-outline-success" type="submit">Update Map</button>
+            </form>         
+        </div>
+    </div>
+</nav>
+
+<!-- AQI Legend -->
+<div id="legend">
+    <b>AQI Color Legend</b>
+    <ul style="list-style: none; padding: 0; margin: 5px;">
+        <li><i style="background:green; width:15px; height:15px; display:inline-block; margin-right:5px;"></i> Good (0-50)</li>
+        <li><i style="background:yellow; width:15px; height:15px; display:inline-block; margin-right:5px;"></i> Moderate (51-100)</li>
+        <li><i style="background:orange; width:15px; height:15px; display:inline-block; margin-right:5px;"></i> Unhealthy for Sensitive Groups (101-150)</li>
+        <li><i style="background:red; width:15px; height:15px; display:inline-block; margin-right:5px;"></i> Unhealthy (151-200)</li>
+        <li><i style="background:purple; width:15px; height:15px; display:inline-block; margin-right:5px;"></i> Very Unhealthy (201-300)</li>
+        <li><i style="background:maroon; width:15px; height:15px; display:inline-block; margin-right:5px;"></i> Hazardous (301+)</li>
+    </ul>
+</div>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/js/bootstrap.bundle.min.js"></script>
+
+    
+            <div class="folium-map" id="map_2c2025c360c3ed95ce6339b20a693bdd" ></div>
+        
+</body>
+<script>
+    
+    
+            var map_2c2025c360c3ed95ce6339b20a693bdd = L.map(
+                "map_2c2025c360c3ed95ce6339b20a693bdd",
+                {
+                    center: [21.21745762262239, 72.83111804946397],
+                    crs: L.CRS.EPSG3857,
+                    zoom: 10,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
+    
+            var tile_layer_1ed9d8a583c9ed0cbc8410660e53a246 = L.tileLayer(
+                "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
+                {"attribution": "\u0026copy; \u003ca href=\"https://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+            );
+        
+    
+            tile_layer_1ed9d8a583c9ed0cbc8410660e53a246.addTo(map_2c2025c360c3ed95ce6339b20a693bdd);
+        
+    
+            var marker_38b108b3eb17f246af1847ce3fde7155 = L.marker(
+                [21.19506977580052, 72.79324648126439],
+                {}
+            ).addTo(map_2c2025c360c3ed95ce6339b20a693bdd);
+        
+    
+            var icon_48e797f3cf504d64ca1c73448a103ea4 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}
+            );
+            marker_38b108b3eb17f246af1847ce3fde7155.setIcon(icon_48e797f3cf504d64ca1c73448a103ea4);
+        
+    
+        var popup_0baf097a03f16cda6bcec6973beb4d7e = L.popup({"maxWidth": 500});
+
+        
+            
+                var html_7a3296080251cbbfd4b2ec8815f1ae4e = $(`<div id="html_7a3296080251cbbfd4b2ec8815f1ae4e" style="width: 100.0%; height: 100.0%;"><img 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"></div>`)[0];
+                popup_0baf097a03f16cda6bcec6973beb4d7e.setContent(html_7a3296080251cbbfd4b2ec8815f1ae4e);
+            
+        
+
+        marker_38b108b3eb17f246af1847ce3fde7155.bindPopup(popup_0baf097a03f16cda6bcec6973beb4d7e)
+        ;
+
+        
+    
+    
+            var marker_408f797d9baf56a8137906ff78f3eff5 = L.marker(
+                [21.22050672027795, 72.83355967457062],
+                {}
+            ).addTo(map_2c2025c360c3ed95ce6339b20a693bdd);
+        
+    
+            var icon_331cf9c1763cef84374ab13767deef94 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}
+            );
+            marker_408f797d9baf56a8137906ff78f3eff5.setIcon(icon_331cf9c1763cef84374ab13767deef94);
+        
+    
+        var popup_3e38d54f4f14f205f64d254fc7ff5ec2 = L.popup({"maxWidth": 500});
+
+        
+            
+                var html_19ea0d1ccb60191cfa575a17ae6bc14a = $(`<div id="html_19ea0d1ccb60191cfa575a17ae6bc14a" style="width: 100.0%; height: 100.0%;"><img 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"></div>`)[0];
+                popup_3e38d54f4f14f205f64d254fc7ff5ec2.setContent(html_19ea0d1ccb60191cfa575a17ae6bc14a);
+            
+        
+
+        marker_408f797d9baf56a8137906ff78f3eff5.bindPopup(popup_3e38d54f4f14f205f64d254fc7ff5ec2)
+        ;
+
+        
+    
+    
+            var marker_93a2124fa21d15be6000355f1e3d3307 = L.marker(
+                [21.236796371788703, 72.8665479925569],
+                {}
+            ).addTo(map_2c2025c360c3ed95ce6339b20a693bdd);
+        
+    
+            var icon_2a83ba72c46f59895db0b7de760b0a07 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}
+            );
+            marker_93a2124fa21d15be6000355f1e3d3307.setIcon(icon_2a83ba72c46f59895db0b7de760b0a07);
+        
+    
+        var popup_0f78979bbd5a3a2c30a209b0b346f20b = L.popup({"maxWidth": 500});
+
+        
+            
+                var html_7a8f516c8442020b7cbc117f95c0b7b6 = $(`<div id="html_7a8f516c8442020b7cbc117f95c0b7b6" style="width: 100.0%; height: 100.0%;"><img 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+                popup_0f78979bbd5a3a2c30a209b0b346f20b.setContent(html_7a8f516c8442020b7cbc117f95c0b7b6);
+            
+        
+
+        marker_93a2124fa21d15be6000355f1e3d3307.bindPopup(popup_0f78979bbd5a3a2c30a209b0b346f20b)
+        ;
+
+        
+    
+</script>
+</html>

static/aqi_map_with_legend.html

@@ -0,0 +1,222 @@
+<!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"/>
+    
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_a150e0fb4cc7276810a35f632619f3a1 {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+            </style>
+        
+</head>
+<body>
+    
+    
+<div style="
+    position: fixed; 
+    bottom: 50px; left: 50px; width: 300px; height: 200px; 
+    background-color: white; 
+    z-index:9999; font-size:14px; border:2px solid grey; 
+    padding: 10px; overflow-y: auto;">
+    <b>AQI Color Legend</b>
+    <table style="width: 100%; border-collapse: collapse; text-align: left;">
+        <thead>
+            <tr style="border-bottom: 2px solid grey;">
+                <th style="padding: 5px;">Color</th>
+                <th style="padding: 5px;">Remark</th>
+                <th style="padding: 5px;">Range</th>
+            </tr>
+        </thead>
+        <tbody>
+            <tr>
+                <td style="padding: 5px;"><i style="background:green; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td style="padding: 5px;">Good</td>
+                <td style="padding: 5px;">0-50</td>
+            </tr>
+            <tr>
+                <td style="padding: 5px;"><i style="background:yellow; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td style="padding: 5px;">Moderate</td>
+                <td style="padding: 5px;">51-100</td>
+            </tr>
+            <tr>
+                <td style="padding: 5px;"><i style="background:orange; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td style="padding: 5px;">Unhealthy for Sensitive Groups</td>
+                <td style="padding: 5px;">101-150</td>
+            </tr>
+            <tr>
+                <td style="padding: 5px;"><i style="background:red; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td style="padding: 5px;">Unhealthy</td>
+                <td style="padding: 5px;">151-200</td>
+            </tr>
+            <tr>
+                <td style="padding: 5px;"><i style="background:purple; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td style="padding: 5px;">Very Unhealthy</td>
+                <td style="padding: 5px;">201-300</td>
+            </tr>
+            <tr>
+                <td style="padding: 5px;"><i style="background:maroon; width:15px; height:15px; display:inline-block; border:1px solid black;"></i></td>
+                <td style="padding: 5px;">Hazardous</td>
+                <td style="padding: 5px;">301+</td>
+            </tr>
+        </tbody>
+    </table>
+</div>
+    
+            <div class="folium-map" id="map_a150e0fb4cc7276810a35f632619f3a1" ></div>
+        
+</body>
+<script>
+    
+    
+            var map_a150e0fb4cc7276810a35f632619f3a1 = L.map(
+                "map_a150e0fb4cc7276810a35f632619f3a1",
+                {
+                    center: [21.2130139551601, 72.81864929625287],
+                    crs: L.CRS.EPSG3857,
+                    zoom: 10,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
+    
+            var tile_layer_63bd45a9cd619b6645de023d33db15a9 = L.tileLayer(
+                "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
+                {"attribution": "\u0026copy; \u003ca href=\"https://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+            );
+        
+    
+            tile_layer_63bd45a9cd619b6645de023d33db15a9.addTo(map_a150e0fb4cc7276810a35f632619f3a1);
+        
+    
+            var marker_2429860b4cbaf5615ea1017c44b19f22 = L.marker(
+                [21.195069775800516, 72.79324648126439],
+                {}
+            ).addTo(map_a150e0fb4cc7276810a35f632619f3a1);
+        
+    
+            var icon_67d84c6204b0790d039646f6c1e2da39 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_2429860b4cbaf5615ea1017c44b19f22.setIcon(icon_67d84c6204b0790d039646f6c1e2da39);
+        
+    
+            marker_2429860b4cbaf5615ea1017c44b19f22.bindTooltip(
+                `<div>
+                     Location 1
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_b7a6e3483466a57f4c675d8f1855957d = L.marker(
+                [21.3, 72.9],
+                {}
+            ).addTo(map_a150e0fb4cc7276810a35f632619f3a1);
+        
+    
+            var icon_312c040d6b5b62945c0a9095b367df86 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_b7a6e3483466a57f4c675d8f1855957d.setIcon(icon_312c040d6b5b62945c0a9095b367df86);
+        
+    
+            marker_b7a6e3483466a57f4c675d8f1855957d.bindTooltip(
+                `<div>
+                     Location 2
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_7ae55386c3f54f1e32b3a75aa8a3c475 = L.marker(
+                [21.1, 72.8],
+                {}
+            ).addTo(map_a150e0fb4cc7276810a35f632619f3a1);
+        
+    
+            var icon_3dbc85d72a24050ac1f5c0e977d415fe = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_7ae55386c3f54f1e32b3a75aa8a3c475.setIcon(icon_3dbc85d72a24050ac1f5c0e977d415fe);
+        
+    
+            marker_7ae55386c3f54f1e32b3a75aa8a3c475.bindTooltip(
+                `<div>
+                     Location 3
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_2d8020c07cc33b3b6500310ab4224ec2 = L.marker(
+                [21.25, 72.85],
+                {}
+            ).addTo(map_a150e0fb4cc7276810a35f632619f3a1);
+        
+    
+            var icon_a4e9c615cf66ea065d533cfb5956086b = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_2d8020c07cc33b3b6500310ab4224ec2.setIcon(icon_a4e9c615cf66ea065d533cfb5956086b);
+        
+    
+            marker_2d8020c07cc33b3b6500310ab4224ec2.bindTooltip(
+                `<div>
+                     Location 4
+                 </div>`,
+                {"sticky": true}
+            );
+        
+    
+            var marker_a32fd1d6158d26403dfdfd275aeee4fe = L.marker(
+                [21.22, 72.75],
+                {}
+            ).addTo(map_a150e0fb4cc7276810a35f632619f3a1);
+        
+    
+            var icon_903f2288884967fe826c17b2e3de0ae6 = L.AwesomeMarkers.icon(
+                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
+            );
+            marker_a32fd1d6158d26403dfdfd275aeee4fe.setIcon(icon_903f2288884967fe826c17b2e3de0ae6);
+        
+    
+            marker_a32fd1d6158d26403dfdfd275aeee4fe.bindTooltip(
+                `<div>
+                     Location 5
+                 </div>`,
+                {"sticky": true}
+            );
+        
+</script>
+</html>

static/multiple_wind_speed_map.html

@@ -0,0 +1,101 @@
+<!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"/>
+    
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_f07b2c63ab23eb37554ee9e8f9b994bd {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+            </style>
+        
+</head>
+<body>
+    
+    
+            <div class="folium-map" id="map_f07b2c63ab23eb37554ee9e8f9b994bd" ></div>
+        
+</body>
+<script>
+    
+    
+            var map_f07b2c63ab23eb37554ee9e8f9b994bd = L.map(
+                "map_f07b2c63ab23eb37554ee9e8f9b994bd",
+                {
+                    center: [21.172654967610296, 72.8284030862854],
+                    crs: L.CRS.EPSG3857,
+                    zoom: 10,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
+    
+            var tile_layer_450ea641254663ee4a9f020cc91927af = L.tileLayer(
+                "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
+                {"attribution": "\u0026copy; \u003ca href=\"https://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+            );
+        
+    
+            tile_layer_450ea641254663ee4a9f020cc91927af.addTo(map_f07b2c63ab23eb37554ee9e8f9b994bd);
+        
+    
+            var marker_2a7638b5cacc5305e893d4dba45911c5 = L.marker(
+                [21.172654967610296, 72.8284030862854],
+                {}
+            ).addTo(map_f07b2c63ab23eb37554ee9e8f9b994bd);
+        
+    
+        var popup_4d47557e8d279db04bb42be9092829a3 = L.popup({"maxWidth": 300});
+
+        
+            
+                var html_f9410af04516429d1f1c883381fe8eba = $(`<div id="html_f9410af04516429d1f1c883381fe8eba" style="width: 100.0%; height: 100.0%;"><img 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uzsbKxatQo2NjaYOnVqtdp8F0VFRfDz88PgwYORnp6OdevWoWvXrujTpw+Ast8dIyMjyOVyTJo0CSKRCFu2bKn1ZVb37t1hYWGBLl26wNzcHFevXsWaNWvQq1cv7kOKRYsW4fjx4+jatSvGjx8PDQ0NrF+/HkqlstI5gQ2uXj6LfE+EhYUxAMzDw6PCuj179jAATF9fn7169UplXVXTGir7yFkulzNra2uVsrt377I+ffowXV1dZmpqyiZPnsyOHDlSrWkNhw8fZqNHj2atW7dmenp6TEtLi9nb27OJEyey7OxslboAWEhICPvpp5+Yg4MDE4vFrEOHDpW2kZ2dzUJCQpiVlRXT1NRkFhYWzM/Pj23YsEGlXlFREVuyZAlzdHRkYrGYGRkZMVdXVzZ//nyWm5vL1Xvx4gWbNGkSMzExYRKJhPXu3ZtlZGRUa1pDuZycHDZ37lzm7OzMdHV1mba2NnNycmJhYWEsKytLpe7OnTtZhw4dmFgsZsbGxmz48OHs/v37KnXkcjmTSCQV2qnqvbO2tma9evXilsunApw4cYKNHTuWGRkZMT09PTZ8+HD29OlTlW2TkpLYxx9/zHR0dJhMJuOmn1T396Z83evTGtavX888PT2ZiYkJE4vFzM7Ojs2cOVPl584YYykpKSwgIIDp6ekxXV1d5uPjw06fPq1Sp/xYzp07p1Je2e92XRIxVk+jZaTRE4lECAkJwZo1axq6K++FmJgYBAUF4dy5c9yYIakdGsMihAgGBRYhRDAosAghgkFjWIQQwaAzLEKIYFBgEUIE44OcOFpaWooHDx5AX1//jV8ZIYTwizGGvLw8yGQyqKm9+/nSBxlYDx48aHTfkSLkQ5KRkYFmzZq983YfZGCVfy0hIyOj0X1XipD3mUKhgJWVVbXvX/Z3H2RglV8GGhgYUGAR0gBqOhRDg+6EEMGgwCKECAYFFiFEMCiwCCGCQYFFCBEMCixCiGBQYBFCBIMCixAiGB/kxFHyYRHNp++L1gcWXvd3qmp0Z1gnT55E7969IZPJIBKJsG/fPm5dcXExQkND4ezsDIlEAplMhpEjR+LBgwcN12FCSL1pdIFVUFCA9u3bY+3atRXWFRYWIiUlBXPmzEFKSgr27NmD9PR07nFJhJD3W6O7JOzRowf3gMm/k0qlOH78uErZmjVr0LlzZ9y7dw/Nmzevjy4SQhpIozvDele5ubkQiUQwNDRs6K4QQupYozvDehcvX75EaGgohg4d+sa7LiiVSpXHkisUivroHiGEZ4I9wyouLsbgwYPBGENkZOQb60ZEREAqlXIvunkfIcIkyMAqD6u7d+/i+PHjb72nVVhYGHJzc7lXRkZGPfWUEMInwV0SlofVjRs3EB8fDxMTk7duIxaLIRaL66F3hJC61OgCKz8/Hzdv3uSW79y5g0uXLsHY2BiWlpYYOHAgUlJScPDgQZSUlODhw4cAAGNjY2hpaTVUtwkh9aDRBdb58+fh4+PDLU+bNg0AIJfLMW/ePBw4cAAA4OLiorJdfHw8vL2966ubhJAG0OgCy9vbG296GDU9qJqQD5cgB90JIR8mCixCiGBQYBFCBIMCixAiGBRYhBDBoMAihAgGBRYhRDAosAghgtHoJo42NiK6HXi9oPnApDroDIsQIhgUWIQQwaDAIoQIBgUWIUQwKLAIIYJBgUUIEQwKLEKIYFBgEUIEgwKLECIYFFiEEMGgwCKECAYFFiFEMCiwCCGCQYFFCBEMCixCiGA0usA6efIkevfuDZlMBpFIhH379qmsZ4xh7ty5sLS0hI6ODvz9/XHjxo2G6SwhpF7VOrCuXr2K8PBw+Pr6ws7ODpaWlmjXrh3kcjm2bdsGpVL5TvsrKChA+/btsXbt2krXL126FN9//z2ioqJw9uxZSCQSBAQE4OXLl7U9FEJIIydiNXz2e0pKCmbNmoXExER06dIFnTt3hkwmg46ODp49e4a0tDScOnUKCoUCs2bNwpQpUyAWi9+tcyIR9u7di8DAQABlZ1cymQzTp0/HjBkzAAC5ubkwNzdHTEwMPvvss2rtV6FQQCqVIjc3FwYGBm/pwzt1mdRQXd5xVDSf3sT6wMLf/ia+y99eZWp8i+QBAwZg5syZ2L17NwwNDausl5ycjFWrVmHZsmX45z//WdPmAAB37tzBw4cP4e/vz5VJpVJ89NFHSE5OrnZgEUKEqcaBdf36dWhqar61nru7O9zd3VFcXFzTpjgPHz4EAJibm6uUm5ubc+sqo1QqVS5NFQpFrftCCKl/NR7Dqk5Y1aY+nyIiIiCVSrmXlZVVg/WFEFJzvD01Jy4uDnFxcXj06BFKS0tV1kVHR/PShoWFBQAgOzsblpaWXHl2djZcXFyq3C4sLAzTpk3jlhUKBYUWIQLEy7SG+fPno3v37oiLi8OTJ0+Qk5Oj8uKLra0tLCwsEBcXx5UpFAqcPXsW7u7uVW4nFothYGCg8iKECA8vZ1hRUVGIiYnB559/Xut95efn4+bNm9zynTt3cOnSJRgbG6N58+aYMmUKFi1aBAcHB9ja2mLOnDmQyWTcJ4mEkPcXL4FVVFQEDw8PPnaF8+fPw8fHh1suv5STy+WIiYnBrFmzUFBQgLFjx+L58+fo2rUrjhw5Am1tbV7aJ4Q0XjWeh/W60NBQ6OnpYc6cOXz0qc7RPKzGh+ZhCV+jnof1+iB2aWkpNmzYgN9++w3t2rWr8Ing8uXLa9oMIYRwahxYFy9eVFku/5QuLS1NpVxEpyiEEJ7UOLDi4+P57AchhLxVraY1NG/eHBMnTsTx48fx6tUrvvpECCGVqlVgbdmyBVpaWhg/fjxMTU0xZMgQbN26Fc+fP+epe4QQ8pdaBZaXlxeWLVuGGzduICkpCS4uLli9ejUsLCzg6+uLlStX4vbt23z1lRDygePtBn6Ojo4ICwvDmTNncOfOHQwdOhRxcXFwcnKCk5MTfv31V76aIoR8oHj7LuHrLC0tERwcjODgYBQWFuLo0aPvfC8sQgj5O14D69GjR5V++blfv358NkMI+UDxElgXLlyAXC7H1atX8feJ8yKRCCUlJXw0Qwj5wPESWKNHj0bLli2xceNGmJub02RRQkid4CWwbt++jdjYWNjb2/OxO0IIqRQvnxL6+fkhNTWVj10RQkiVeDnD+s9//gO5XI60tDQ4OTlV+PJznz59+GiGEPKB4yWwkpOTkZSUhMOHD1dYR4PuhBC+8HJJOHHiRIwYMQJZWVkoLS1VeVFYEUL4wktgPX36FFOnTq3w+C1CCOETL4HVv39/ut0MIaTO8TKG1bJlS4SFhSExMRHOzs4VBt0nTZrERzOEkA8cL/d0t7W1rboBkajR3bGB7une+NA93YWvUd/T/XV37tzhYzeEEPJGvIxh/f0+7q/bt28fH00QQgg/gRUQEFDpWVZsbCyGDx/ORxOEEMJPYH3xxRfw9/fHw4cPubKdO3di5MiRiImJ4aMJQgjhJ7Dmz5+Pnj17wt/fH8+ePcO2bdsQFBSEzZs3Y9CgQXw0wSkpKcGcOXNga2sLHR0d2NnZYeHChRVua0MIef/wdgO/1atXY/jw4fj444+RmZmJ7du3o2/fvnztnrNkyRJERkZi06ZNcHR0xPnz5xEUFASpVErTJwh5z9U4sA4cOFChrH///jh16hSGDh0KkUjE1eHzy8+nT59G37590atXLwCAjY0Ntm/fjj/++IO3NgghjVONAyswMLDKddHR0YiOjgbA/5efPTw8sGHDBly/fh0tW7ZEamoqEhMTsXz58iq3USqVUCqV3LJCoeCtP4SQ+lPjwPr7fdvry+zZs6FQKNC6dWuoq6ujpKQEixcvfuOnkREREZg/f3499pIQUhd4e8xXfdm1axe2bt2Kbdu2ISUlBZs2bcK///1vbNq0qcptwsLCkJuby70yMjLqsceEEL7U+Axrx44d+Oyzz6pVNyMjA/fu3UOXLl1q2hxn5syZmD17Nte2s7Mz7t69i4iICMjl8kq3EYvF9JgxQt4DNT7DioyMRJs2bbB06VJcvXq1wvrc3FwcOnQIw4YNQ8eOHfH06dNadbRcYWEh1NRUu62urt5gl6iEkPpT4zOsEydO4MCBA1i9ejXCwsIgkUhgbm4ObW1t5OTk4OHDhzA1NcWoUaOQlpbG272yevfujcWLF6N58+ZwdHTExYsXsXz5cowePZqX/RNCGi9e7tbw5MkTJCYm4u7du3jx4gVMTU3RoUMHdOjQocLZUG3l5eVhzpw52Lt3Lx49egSZTIahQ4di7ty50NLSqtY+6G4NjQ/drUH46uNuDbwEltBQYDU+FFjCVx+BJbhPCQkhHy4KLEKIYFBgEUIEgwKLECIYFFiEEMGo8TysadOmVbvum76YTAgh1VXjwLp48aLKckpKCl69eoVWrVoBAK5fvw51dXW4urrWroeEEPL/ahxYrz84dfny5dDX18emTZtgZGQEAMjJyUFQUBC6detW+14SQgh4mjjatGlTHDt2DI6OjirlaWlp6N69Ox48eFDbJnhFE0cbH5o4KnyCmTiqUCjw+PHjCuWPHz9GXl4eH00QQgg/gdWvXz8EBQVhz549uH//Pu7fv4/Y2FiMGTMG/fv356MJQgjh5yEUUVFRmDFjBoYNG4bi4uKyHWtoYMyYMfjuu+/4aIIQQvj98nNBQQFu3boFALCzs4NEIuFr17yiMazGh8awhE8wY1jlsrKykJWVBQcHB0gkEnpWICGEV7wE1tOnT+Hn54eWLVuiZ8+eyMrKAgCMGTMG06dP56MJQgjhJ7CmTp0KTU1N3Lt3D7q6ulz5kCFDcOTIET6aIIQQfgbdjx07hqNHj6JZs2Yq5Q4ODrh79y4fTRBCCD9nWAUFBSpnVuWePXtGT6shhPCGl8Dq1q0bNm/ezC2LRCKUlpZi6dKl8PHx4aMJQgjh55Jw6dKl8PPzw/nz51FUVIRZs2bhypUrePbsGZKSkvhoghBB6Na8G2Z6zISrzBUyfRkCdwRif/p+AICGmgYW+S5CT/ueaGHUArnKXPx2+zfM/m02svKzqtznV25fYZzbONgY2gAArjy6ggUnF+DIzb/Gh+Pl8fC28VbZLup8FMb9Oo73Y2xIvASWk5MTrl+/jtWrV0NfXx/5+fno378/QkJCYGlpyUcThAiCREuC1OxURF+Kxt4he1XW6WrqoqNFRyw8uRCp2akw0jbCqn+swoGhB9Dph05V7vO+4j5m/zYbN57dgAgiyF3k2P/ZfnRY3wH/e/w/rt6GCxswN34ut1xYXMj/ATYwXgILAKRSKb7++mu+dkeIIB25eUTlzOd1CqUC3X/qrlI24fAEnAs+BysDK2QoMird7uD1gyrLX//+Nca5jcPHzT5WCazC4kJkF2TX8ggaN94mjp46dQojRoyAh4cHMjMzAQBbtmxBYmIiX00Q8t6RiqUoZaV4/vJ5teqridQwxHEIJJoSJGckq6wb7jwcj2c+xuVxl/GN3zfQ0dCpgx43LF4CKzY2FgEBAdDR0UFKSgqUSiWAssfVf/PNN3w0oSIzMxMjRoyAiYkJdHR04OzsjPPnz/PeDiF1SawuxhL/Jdh+eTvyit58VxMnMyfkheVB+bUSUZ9God/Ofrj65Cq3ftvlbRixdwR8NvkgIjECn7f7HD/1/6muD6He8XJJuGjRIkRFRWHkyJHYsWMHV96lSxcsWrSIjyY4OTk56NKlC3x8fHD48GE0adIEN27c4G4cSIgQaKhpYNegXRCJRNUaGE9/kg6XKBdItaUY2HYgNgVugleMFxdaP6T8wNVNe5SGrLws/C7/HS2MWuB2zu06O476xktgpaenw9PTs0K5VCrF8+fP+WiCs2TJElhZWeHHH3/kymxtbXltg5C6pKGmgV0Dd8Faag3fzb5vPbsCgOLSYtzKKbuxQEpWCjrJOmHyx5Px1cGvKq1/NvMsAMDe2P69CixeLgktLCxw8+bNCuWJiYlo0aIFH01wDhw4ADc3NwwaNAhmZmbo0KEDfvjhh7dvSEgjUB5WDiYO8N/ij2cvntVoP2oiNYjVq56U7WLhAgDIyqt6uoQQ8RJYwcHBmDx5Ms6ePQuRSIQHDx5g69atmDFjBsaN43ceyO3btxEZGQkHBwccPXoU48aNw6RJk7Bp06Yqt1EqlVAoFCovQuqCRFOC9ubt0d68PQDA1sgW7c3bw8rAChpqGtg9aDfcZG4Yvmc41EXqMJeYw1xiDk01TW4fv33+G0I6hXDL3/h9g27Nu8Faag0nMyd84/cNvG28sfXyVgBAC6MW+Nrza3S07AhrqTV6t+yNzYGbceLPE7j86HL9/gDqGC+XhLNnz0ZpaSn8/PxQWFgIT09PiMVizJgxAxMnTuSjCU5paSnc3Ny4wfwOHTogLS0NUVFRkMvllW4TERGB+fPn89oPQirjJnNDwqgEbnlFwAoAQMylGMxLmIe+rfsCAFK/SlXZzjvGGyfungAA2BnbwVTXlFtnJjHD5n6bYalniVxlLv6b/V8E/BSA327/BgAoKimCv60/pnw0BRItCTJyMxB7NRaLTvI7ftwY8HoDv6KiIty8eRP5+flo27Yt9PT0+No1x9raGp988gn+85//cGWRkZFYtGgRN53i75RKJffJJVB2EzErK6tGfwO/bt2AmTMBV1dAJgMCA4H9+/9a368f8NVXZetNTAAXFyA1taq9lfniC2DkSMDJqWz5wgXgn/8Ezp2rq6OoHrqBn/AJ7gZ+Wlpa0NfXh6WlZZ2EFVD2yWN6erpK2fXr12FtbV3lNmKxGAYGBiovIZBIygIoJKTq9YmJQGho9ffp7Q1s3w74+ADu7kBGBnDsWFkgEtLY8XJJ+OrVK8yfPx/ff/898vPzAQB6enqYOHEiwsPDoamp+ZY9VN/UqVPh4eGBb775BoMHD8Yff/yBDRs2YMOGDby10VgcOVL2qspP/z/N5g1ZXcGIEarLX3wBDBgA+PkBW7a8ex8JqU+8BNbEiROxZ88eLF26FO7u7gCA5ORkzJs3D0+fPkVkZCQfzQAAOnXqhL179yIsLAwLFiyAra0tVq5cieHDh/PWxodEVxfQ1ASe1ezDKkLqFS+BtW3bNuzYsQM9evTgytq1awcrKysMHTqU18ACgE8//RSffvopr/v8UC1ZAjx4APz2W0P3hJC34yWwxGIxbGxsKpTb2tpCS0uLjyZIHQgNBT77rGxc67XPJAhptHgZdJ8wYQIWLlyo8kmcUqnE4sWLMWHCBD6aIDybPh2YPRvo3h24/H5N1SHvMV7OsC5evIi4uDg0a9YM7duXTZhLTU1FUVER/Pz8VJ7+vGfPHj6aJLUwcybwr38BAQFl0xoIEQpeAsvQ0BADBgxQKbOysuJj1x80iQSwt/9r2dYWaN++bIA8IwMwMgKaN/9rSkKrVmX/ffgQyP7/2yJt2gRkZpbNtQKAWbOABQuAYcOAP/8EzM3LyvPzgYKCejksQmqMl8B6/YvIhD9ubkBCwl/LK8omTSMmBggKAvr0Kfv/cjt3lv133jygfGJ/8+ZAaelfdcaNA8RiIDZWta3XtyGkseJ1pnu5EydOoKCgAO7u7o3yti/0qPrGh2a6C199zHSv1RnWkiVLkJ+fj4ULFwIAGGPo0aMHjh07BgAwMzNDXFwcHB0da9MMIYQAqOWnhDt37oRT+ZfSAOzevRsnT57EqVOn8OTJE7i5udGXjgkhvKlVYN25cwft2rXjlg8dOoSBAweiS5cuMDY2xtdff43k5OQ37IEQQqqvVoH16tUrlSc7Jycnw8PDg1uWyWR48uRJbZoghBBOrQLLzs4OJ0+eBADcu3cP169fV7lV8v3792FiYlK7HhJCyP+r1aB7SEgIJkyYgFOnTuHMmTNwd3dH27ZtufW///47OnToUOtOEkIIUMvACg4Ohrq6On755Rd4enoiPDxcZf2DBw8wevToWnWQEELK1ck8rMaO5mE1PjQPS/gEd8dRQgipSxRYhBDBoMAihAgGBRYhRDAosAghglHjaQ2v35TvbeimfYQQPtT4DEsqlXIvAwMDxMXF4fz589z6CxcuIC4uDlKplJeOEkJIjc+wXr9pX2hoKAYPHoyoqCioq6sDAEpKSjB+/HjBPLSUENL48TKGFR0djRkzZnBhBQDq6uqYNm0aoqOj+WiCEEL4CaxXr17h2rVrFcqvXbuG0tfvz0sIIbXAS2AFBQVhzJgxWL58ORITE5GYmIhly5bhiy++QFBQEB9NVOnbb7+FSCTClClT6rQdQkjD4+UhFP/+979hYWGBZcuWISsrCwBgaWmJmTNnYvr06Xw0Ualz585h/fr1KjcRJIS8v3g5w1JTU8OsWbOQmZmJ58+f4/nz58jMzMSsWbNUxrX4lJ+fj+HDh+OHH35olA+6IITwj/eJowYGBvXyyWBISAh69eoFf3//t9ZVKpVQKBQqL0KI8PASWNnZ2fj8888hk8mgoaEBdXV1lRffduzYgZSUFERERFSrfkREhMq8MXrIKyHCxMsY1qhRo3Dv3j3MmTMHlpaWENXhTaQyMjIwefJkHD9+HNra2tXaJiwsDNOmTeOWFQoFhRYhAsRLYCUmJuLUqVNwcXHhY3dvdOHCBTx69AgdO3bkykpKSnDy5EmsWbMGSqWywlmdWCxWeVgGIUSYeAksKysr1NeNS/38/HD58mWVsqCgILRu3RqhoaF1NshPCGl4vATWypUrMXv2bKxfvx42NjZ87LJK+vr6Kg9vBQCJRAITE5MK5YSQ9wsvgTVkyBAUFhbCzs4Ourq60NTUVFn/7NkzPpohhHzgeDvDakgJCQkN2j4hpH7wElhyuZyP3RBCyBvVOLAUCgU3QfRtEzHpFjOEED7UOLCMjIyQlZUFMzMzGBoaVjr3ijEGkUiEkpKSWnWSEEKAWgTW77//DmNjY+7/63KyKCGEALUILC8vL9y5cwe2trbw9vbmsUuEEFK5Wn2X0M7ODra2thg9ejR++ukn3L9/n69+EUJIBbX6lPD3339HQkICEhISsH37dhQVFaFFixbw9fWFj48PfHx8YG5uzldfCSEfuFoFlre3N3c5+PLlS5w+fZoLsE2bNqG4uBitW7fGlStX+OgrIeQDx8s8LADQ1taGr68vunbtCh8fHxw+fBjr16+v9F7vhBBSE7UOrKKiIpw5cwbx8fFISEjA2bNnYWVlBU9PT6xZswZeXl589JMQQmoXWL6+vjh79ixsbW3h5eWFL7/8Etu2bYOlpSVf/SOEEE6tAuvUqVOwtLSEr68vvL294eXlBRMTE776RgghKmo1reH58+fYsGEDdHV1sWTJEshkMjg7O2PChAnYvXs3Hj9+zFc/CSEEIsbjnffy8vKQmJjIjWelpqbCwcEBaWlpfDXBC4VCAalUitzc3Ld+z5Em8NePurz/o2g+vYn1gYW//U18l7+9yvD61ByJRAJjY2MYGxvDyMgIGhoauHr1Kp9NEEI+YLUawyotLcX58+eRkJCA+Ph4JCUloaCgAE2bNoWPjw/Wrl0LHx8fvvpKCPnA1SqwDA0NUVBQAAsLC/j4+GDFihXw9vaGnZ0dX/0jhBBOrQLru+++g4+PD1q2bMlXfwghpEq1Cqwvv/ySr34QQshb8f6oekIIqSsUWIQQwaDAIoQIBgUWIUQwBBdYERER6NSpE/T19WFmZobAwECkp6c3dLcIIfVAcIF14sQJhISE4MyZMzh+/DiKi4vRvXt3FBQUNHTXCCF1jLcb+NWXI0eOqCzHxMTAzMwMFy5cgKenZwP1ihBSHwR3hvV3ubm5AMA9cowQ8v4S3BnW60pLSzFlyhR06dIFTk5OVdZTKpVQKpXc8tueVE0IaZwEfYYVEhKCtLQ07Nix4431IiIiIJVKuZeVlVU99ZAQwifBBtaECRNw8OBBxMfHo1mzZm+sGxYWhtzcXO6VkZFRT70khPBJcJeEjDFMnDgRe/fuRUJCAmxtbd+6jVgshlgsrofeEULqkuACKyQkBNu2bcP+/fuhr6+Phw8fAgCkUil0dHQauHeEkLokuEvCyMhI5ObmwtvbG5aWltxr586dDd01QkgdE9wZFo+3oCeECIzgzrAIIR8uCixCiGBQYBFCBIMCixAiGBRYhBDBoMAihAgGBRYhRDAosAghgkGBRQgRDAosQohgUGARQgSDAosQIhgUWIQQwaDAIoQIBgUWIUQwKLAIIYJBgUUIEQwKLEKIYFBgEUIEgwKLECIYFFiEEMGgwCKECAYFFiFEMCiwCCGCIdjAWrt2LWxsbKCtrY2PPvoIf/zxR0N3iRBSxwQZWDt37sS0adMQHh6OlJQUtG/fHgEBAXj06FFDd40QUocEGVjLly9HcHAwgoKC0LZtW0RFRUFXVxfR0dEN3TVCSB3SaOgOvKuioiJcuHABYWFhXJmamhr8/f2RnJxc6TZKpRJKpZJbzs3NBQAoFIq67Syptjp9K17W4b4Jpzp/T+V1GGM1akNwgfXkyROUlJTA3Nxcpdzc3BzXrl2rdJuIiAjMnz+/QrmVlVWd9JG8O6m0oXtAakv6bfXfxLy8PEhr8KYLLrBqIiwsDNOmTeOWS0tL8ezZM5iYmEAkEjVgz/inUChgZWWFjIwMGBgYNHR3SA28z+8hYwx5eXmQyWQ12l5wgWVqagp1dXVkZ2erlGdnZ8PCwqLSbcRiMcRisUqZoaFhXXWxUTAwMHjvftk/NO/re1iTM6tyght019LSgqurK+Li4riy0tJSxMXFwd3dvQF7Rgipa4I7wwKAadOmQS6Xw83NDZ07d8bKlStRUFCAoKCghu4aIaQOCTKwhgwZgsePH2Pu3Ll4+PAhXFxccOTIkQoD8R8isViM8PDwCpfARDjoPayaiNX080VCCKlnghvDIoR8uCiwCCGCQYFFCBEMCixSJZFIhH379jV0N95ro0aNQmBgILfs7e2NKVOm1Hs/EhISIBKJ8Pz583pv+11QYDUSycnJUFdXR69evd5pOxsbG6xcubJuOvUBGzVqFEQiEUQiEbS0tGBvb48FCxbg1atXddrunj17sHDhwmrVFUrI8IkCq5HYuHEjJk6ciJMnT+LBgwcN3R0C4B//+AeysrJw48YNTJ8+HfPmzcN3331XoV5RURFvbRobG0NfX5+3/b1vKLAagfz8fOzcuRPjxo1Dr169EBMTo7L+l19+QadOnaCtrQ1TU1P069cPQNnlw927dzF16lTubAAA5s2bBxcXF5V9rFy5EjY2NtzyuXPn8Mknn8DU1BRSqRReXl5ISUmpy8MUHLFYDAsLC1hbW2PcuHHw9/fHgQMHuMu4xYsXQyaToVWrVgCAjIwMDB48GIaGhjA2Nkbfvn3x559/cvsrKSnBtGnTYGhoCBMTE8yaNavCXQv+fkmoVCoRGhoKKysriMVi2NvbY+PGjfjzzz/h4+MDADAyMoJIJMKoUaMAlH3zIyIiAra2ttDR0UH79u2xe/dulXYOHTqEli1bQkdHBz4+Pir9bMwosBqBXbt2oXXr1mjVqhVGjBiB6Oho7hf5119/Rb9+/dCzZ09cvHgRcXFx6Ny5M4Cyy4dmzZphwYIFyMrKQlZWVrXbzMvLg1wuR2JiIs6cOQMHBwf07NkTeXl5dXKM7wMdHR3ubCouLg7p6ek4fvw4Dh48iOLiYgQEBEBfXx+nTp1CUlIS9PT08I9//IPbZtmyZYiJiUF0dDQSExPx7Nkz7N27941tjhw5Etu3b8f333+Pq1evYv369dDT04OVlRViY2MBAOnp6cjKysKqVasAlN2dZPPmzYiKisKVK1cwdepUjBgxAidOnABQFqz9+/dH7969cenSJXzxxReYPXt2Xf3Y+MVIg/Pw8GArV65kjDFWXFzMTE1NWXx8PGOMMXd3dzZ8+PAqt7W2tmYrVqxQKQsPD2ft27dXKVuxYgWztraucj8lJSVMX1+f/fLLL1wZALZ37953OZT3hlwuZ3379mWMMVZaWsqOHz/OxGIxmzFjBpPL5czc3JwplUqu/pYtW1irVq1YaWkpV6ZUKpmOjg47evQoY4wxS0tLtnTpUm59cXExa9asGdcOY4x5eXmxyZMnM8YYS09PZwDY8ePHK+1jfHw8A8BycnK4spcvXzJdXV12+vRplbpjxoxhQ4cOZYwxFhYWxtq2bauyPjQ0tMK+GiM6w2pg6enp+OOPPzB06FAAgIaGBoYMGYKNGzcCAC5dugQ/Pz/e283OzkZwcDAcHBwglUphYGCA/Px83Lt3j/e2hOrgwYPQ09ODtrY2evTogSFDhmDevHkAAGdnZ2hpaXF1U1NTcfPmTejr60NPTw96enowNjbGy5cvcevWLeTm5iIrKwsfffQRt42Ghgbc3NyqbP/SpUtQV1eHl5dXtft88+ZNFBYW4pNPPuH6oaenh82bN+PWrVsAgKtXr6r0A4BgbhwgyO8Svk82btyIV69eqdwfiDEGsViMNWvWQEdH5533qaamVmFspLi4WGVZLpfj6dOnWLVqFaytrSEWi+Hu7s7rALLQ+fj4IDIyElpaWpDJZNDQ+OvPRSKRqNTNz8+Hq6srtm7dWmE/TZo0qVH7NXnv8/PzAZQNJTRt2lRl3fvw3UQKrAb06tUrbN68GcuWLUP37t1V1gUGBmL79u1o164d4uLiqrwThZaWFkpKSlTKmjRpgocPH4Ixxg3EX7p0SaVOUlIS1q1bh549ewIoG9d48uQJT0f2fpBIJLC3t69W3Y4dO2Lnzp0wMzOr8h5WlpaWOHv2LDw9PQGUvf8XLlxAx44dK63v7OyM0tJSnDhxAv7+/hXWl5/hvf7+t23bFmKxGPfu3avyzKxNmzY4cOCAStmZM2fefpCNAF0SNqCDBw8iJycHY8aMgZOTk8prwIAB2LhxI8LDw7F9+3aEh4fj6tWruHz5MpYsWcLtw8bGBidPnkRmZiYXON7e3nj8+DGWLl2KW7duYe3atTh8+LBK2w4ODtiyZQuuXr2Ks2fPYvjw4TX6F52UGT58OExNTdG3b1+cOnUKd+7cQUJCAiZNmoT79+8DACZPnoxvv/0W+/btw7Vr1zB+/Pg3zqGysbGBXC7H6NGjsW/fPm6fu3btAgBYW1tDJBLh4MGDePz4MfLz86Gvr48ZM2Zg6tSp2LRpE27duoWUlBSsXr0amzZtAgB89dVXuHHjBmbOnIn09HRs27atwifTjVYDj6F90D799FPWs2fPStedPXuWAWCpqaksNjaWubi4MC0tLWZqasr69+/P1UtOTmbt2rVjYrGYvf52RkZGMisrKyaRSNjIkSPZ4sWLVQbdU1JSmJubG9PW1mYODg7s559/rjCADxp0f6d1WVlZbOTIkczU1JSJxWLWokULFhwczHJzcxljZYPskydPZgYGBszQ0JBNmzaNjRw5sspBd8YYe/HiBZs6dSqztLRkWlpazN7enkVHR3PrFyxYwCwsLJhIJGJyuZwxVvYhwcqVK1mrVq2YpqYma9KkCQsICGAnTpzgtvvll1+Yvb09E4vFrFu3biw6OloQg+50exlCiGDQJSEhRDAosAghgkGBRQgRDAosQohgUGARQgSDAosQIhgUWIQQwaDAIoQIBgUWIUQwKLAIIYJBgUUIEQwKLEKIYFBgEUIEgwKLECIYFFiEEMGgwCKECAYFFiFEMP4P4ll/zNeckY8AAAAASUVORK5CYII=" alt="Wind Speed Comparison"></div>`)[0];
+                popup_4d47557e8d279db04bb42be9092829a3.setContent(html_f9410af04516429d1f1c883381fe8eba);
+            
+        
+
+        marker_2a7638b5cacc5305e893d4dba45911c5.bindPopup(popup_4d47557e8d279db04bb42be9092829a3)
+        ;
+
+        
+    
+    
+            marker_2a7638b5cacc5305e893d4dba45911c5.bindTooltip(
+                `<div>
+                     Utran
+                 </div>`,
+                {"sticky": true}
+            );
+        
+</script>
+</html>

surat_sample_test.csv

@@ -0,0 +1,204 @@
+PM2.5,PM10,NO,NO2,NOx,NH3,SO2,CO,Ozone,Benzene,Toluene,Xylene,Day,Month,Hour,AQI,Hour sin,Hour cos,Day sin,Day cos,Month sin,Month cos,AQI_step_1,AQI_step_2,AQI_step_3
+69.95,549.41,64.17,97.97,34.76,0.0,35.78,1.26,0.0,0.0,0.0,0.0,18,11,1,549.2624999999999,0.25881904510252074,0.9659258262890683,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,1111.1625,158.48,639.825
+67.56,998.93,66.08,101.35,35.02,0.0,36.04,1.23,0.0,0.0,0.0,0.0,18,11,2,1111.1625,0.49999999999999994,0.8660254037844387,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,158.48,639.825,210.0
+50.98,187.72,66.48,101.38,34.94,0.0,36.09,1.18,0.0,0.0,0.0,0.0,18,11,3,158.48,0.7071067811865476,0.7071067811865476,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,639.825,210.0,150.39999999999998
+50.59,621.86,66.34,101.0,35.05,0.0,36.14,1.12,0.0,0.0,0.0,0.0,18,11,4,639.825,0.8660254037844386,0.5000000000000001,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,210.0,150.39999999999998,662.3874999999999
+72.78,260.0,66.92,102.82,35.2,0.0,36.17,1.07,0.0,0.0,0.0,0.0,18,11,5,210.0,0.9659258262890683,0.25881904510252074,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,150.39999999999998,662.3874999999999,111.03333333333335
+63.9,175.6,62.66,94.63,34.56,0.0,35.95,0.98,0.0,0.0,0.0,0.0,18,11,6,150.39999999999998,1.0,6.123233995736766e-17,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,662.3874999999999,111.03333333333335,327.08750000000003
+63.0,639.91,57.92,88.44,33.91,0.0,35.72,0.92,0.0,0.0,0.0,0.0,18,11,7,662.3874999999999,0.9659258262890683,-0.25881904510252063,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,111.03333333333335,327.08750000000003,142.98
+63.31,91.14,56.63,86.67,33.45,0.0,36.41,0.9,0.0,0.0,0.0,0.0,18,11,8,111.03333333333335,0.8660254037844387,-0.4999999999999998,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,327.08750000000003,142.98,202.29999999999998
+66.03,371.67,51.25,77.13,32.68,0.0,37.09,0.91,0.0,0.0,0.0,0.0,18,11,9,327.08750000000003,0.7071067811865476,-0.7071067811865475,-0.4853019625310808,-0.8743466161445822,-0.5000000000000004,0.8660254037844384,142.98,202.29999999999998,302.1625
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templates/aqi_forecast_with_legend.html

@@ -0,0 +1,187 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AQI Forecast Map</title>
+    <!-- Add Bootstrap 5 CSS -->
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/css/bootstrap.min.css">
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background-color: #f8f9fa;
+            margin: 0;
+            padding: 0;
+        }
+
+        /* Thinner Navigation Bar */
+        .navbar {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            z-index: 1000;
+            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
+            padding: 3px 10px;
+            /* Reduced padding for a thinner navbar */
+            font-size: 0.85rem;
+            /* Slightly smaller font size */
+            height: 50px;
+            /* Explicit height for consistent thinness */
+        }
+
+        /* Reduced brand name size */
+        .navbar-brand {
+            font-size: 1rem;
+            /* Smaller font size for brand */
+            padding: 0;
+            /* Remove extra padding */
+            line-height: 50px;
+            /* Center-align text vertically */
+        }
+
+        /* Adjust the navbar toggler button */
+        .navbar-toggler {
+            padding: 2px 8px;
+            /* Smaller padding for toggler */
+            line-height: 1;
+            /* Adjust spacing */
+        }
+
+        /* Adjust form-container inside navbar */
+        .form-container {
+            margin-bottom: 0;
+            display: flex;
+            align-items: center; /* Vertically align form fields and button */
+        }
+
+        /* Reduce size of form inputs and button */
+        .form-control {
+            font-size: 0.85rem;
+            padding: 4px 6px;
+            height: auto;
+            flex-grow: 1; /* Ensure input fields expand proportionally */
+            min-width: 50px; /* Prevent inputs from shrinking too much */
+            margin-right: 5px; /* Spacing between input fields */
+        }
+
+        .btn-outline-success {
+            font-size: 0.85rem;
+            padding: 4px 8px;
+            height: auto;
+            white-space: nowrap; /* Prevent button text from wrapping */
+        }
+
+        /* Content area */
+        .content {
+            display: flex;
+            flex-direction: column;
+            height: 80vh;
+            /* Full height */
+            padding-top: 50px;
+            /* Reduced space for the smaller navbar */
+        }
+
+        /* Map Container to fill the remaining space */
+        .map-container {
+            flex: 1;
+            margin-bottom: 20px;
+            background-color: #e9ecef;
+            /* Optional: set a background color for the map container */
+        }
+
+        /* AQI Legend box */
+        #legend {
+            position: fixed;
+            bottom: 20px;
+            left: 20px;
+            width: 220px;
+            /* Reduced the width */
+            background-color: rgba(255, 255, 255, 0.6);
+            z-index: 9999;
+            font-size: 12px;
+            /* Reduced font size */
+            border-radius: 8px;
+            padding: 10px;
+            /* Reduced padding */
+            box-shadow: 2px 2px 8px rgba(0, 0, 0, 0.3);
+        }
+
+        .form-control {
+            margin-right: 10px;
+        }
+
+        /* List styling for AQI legend */
+        .legend-list li {
+            margin-bottom: 6px;
+        }
+
+        .legend-list i {
+            width: 18px;
+            /* Reduced the size of the color box */
+            height: 18px;
+            display: inline-block;
+            margin-right: 8px;
+        }
+    </style>
+</head>
+
+<body>
+
+    <!-- Fixed Navigation Bar -->
+    <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
+        <div class="container-fluid">
+            <!-- Brand -->
+            <a class="navbar-brand" href="#">AQI Forecaster</a>
+    
+            <!-- Toggler for mobile view -->
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav"
+                aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+    
+            <!-- Navigation content -->
+            <div class="collapse navbar-collapse" id="navbarNav">
+                <div class="w-100 d-flex justify-content-between align-items-center">
+                    <!-- Form for latitude and longitude -->
+                    <form class="d-flex flex-grow-1 form-container me-2" method="POST" action="{{ url_for('forecast') }}">
+                        <input class="form-control me-2" type="text" name="latitude" placeholder="Enter Latitude" required>
+                        <input class="form-control me-2" type="text" name="longitude" placeholder="Enter Longitude" required>
+                        <button class="btn btn-outline-success" type="submit">Update Map</button>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </nav>
+    
+
+
+    <div class="content">
+        <div class="map-container">
+            <!-- AQI Forecast Map -->
+            <div>
+                <!-- Render the map -->
+                {{ map_html|safe }}
+            </div>
+        </div>
+
+        <!-- AQI Legend -->
+        <div id="legend">
+            <b>AQI Color Legend</b>
+            <ul class="legend-list" style="list-style: none; padding: 0; margin: 5px;">
+                <li><i style="background:green;"></i> Good (0-50)</li>
+                <li><i style="background:yellow;"></i> Moderate (51-100)</li>
+                <li><i style="background:orange;"></i> Unhealthy for Sensitive Groups (101-150)</li>
+                <li><i style="background:red;"></i> Unhealthy (151-200)</li>
+                <li><i style="background:purple;"></i> Very Unhealthy (201-300)</li>
+                <li><i style="background:maroon;"></i> Hazardous (301+)</li>
+            </ul>
+        </div>
+    </div>
+
+    <!-- Add Bootstrap 5 JS -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/js/bootstrap.bundle.min.js"></script>
+</body>
+
+</html>

templates/index.html

@@ -0,0 +1,134 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AQI Forecast Map</title>
+    <!-- Add Bootstrap 5 CSS -->
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/css/bootstrap.min.css">
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background-color: #f8f9fa;
+            margin: 0;
+            padding: 0;
+        }
+
+        /* Fixed Navigation Bar at the top */
+        .navbar {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            z-index: 1000;
+            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
+        }
+
+        /* Content area */
+        .content {
+            display: flex;
+            flex-direction: column;
+            height: 100vh; /* Full height */
+            padding-top: 80px; /* Space for fixed navbar */
+        }
+
+        /* Map Container to fill the remaining space */
+        .map-container {
+            flex: 1;
+            margin-bottom: 20px;
+            background-color: #e9ecef; /* Optional: set a background color for the map container */
+        }
+
+        /* AQI Legend box */
+        #legend {
+            position: fixed;
+            bottom: 20px;
+            left: 20px;
+            width: 220px; /* Reduced the width */
+            background-color: rgba(255, 255, 255, 0.8);
+            z-index: 9999;
+            font-size: 12px; /* Reduced font size */
+            border-radius: 8px;
+            padding: 10px; /* Reduced padding */
+            box-shadow: 2px 2px 8px rgba(0, 0, 0, 0.3);
+        }
+
+        /* Navbar style adjustments */
+        .navbar-brand {
+            font-size: 1.5rem;
+        }
+
+        /* Container for the form */
+        .form-container {
+            margin-bottom: 30px;
+        }
+
+        /* Form styling */
+        .form-control {
+            margin-right: 10px;
+        }
+
+        /* List styling for AQI legend */
+        .legend-list li {
+            margin-bottom: 6px;
+        }
+
+        .legend-list i {
+            width: 18px; /* Reduced the size of the color box */
+            height: 18px;
+            display: inline-block;
+            margin-right: 8px;
+        }
+    </style>
+</head>
+
+<body>
+
+    <!-- Fixed Navigation Bar -->
+    <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
+        <div class="container-fluid">
+            <a class="navbar-brand" href="#">AQI Forecaster</a>
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav"
+                aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+            <div class="collapse navbar-collapse" id="navbarNav">
+                <form class="d-flex ms-auto form-container" method="POST" action="{{ url_for('forecast') }}">
+                    <input class="form-control me-2" type="text" name="latitude" placeholder="Enter Latitude" required>
+                    <input class="form-control me-2" type="text" name="longitude" placeholder="Enter Longitude"
+                        required>
+                    <button class="btn btn-outline-success" type="submit">Update Map</button>
+                </form>
+            </div>
+        </div>
+    </nav>
+
+    <div class="content">
+        <div class="map-container">
+            <!-- AQI Forecast Map -->
+            <div>
+                <!-- Render the map -->
+                {{ map_html|safe }}
+            </div>
+        </div>
+        
+        <!-- AQI Legend -->
+        <div id="legend">
+            <b>AQI Color Legend</b>
+            <ul class="legend-list" style="list-style: none; padding: 0; margin: 5px;">
+                <li><i style="background:green;"></i> Good (0-50)</li>
+                <li><i style="background:yellow;"></i> Moderate (51-100)</li>
+                <li><i style="background:orange;"></i> Unhealthy for Sensitive Groups (101-150)</li>
+                <li><i style="background:red;"></i> Unhealthy (151-200)</li>
+                <li><i style="background:purple;"></i> Very Unhealthy (201-300)</li>
+                <li><i style="background:maroon;"></i> Hazardous (301+)</li>
+            </ul>
+        </div>
+    </div>
+
+    <!-- Add Bootstrap 5 JS -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/js/bootstrap.bundle.min.js"></script>
+</body>
+
+</html>

templates/multiple_wind_speed_map.html

@@ -0,0 +1,101 @@
+<!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"/>
+    
+            <meta name="viewport" content="width=device-width,
+                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+            <style>
+                #map_c61b342e70deea09a0734350221e1318 {
+                    position: relative;
+                    width: 100.0%;
+                    height: 100.0%;
+                    left: 0.0%;
+                    top: 0.0%;
+                }
+                .leaflet-container { font-size: 1rem; }
+            </style>
+        
+</head>
+<body>
+    
+    
+            <div class="folium-map" id="map_c61b342e70deea09a0734350221e1318" ></div>
+        
+</body>
+<script>
+    
+    
+            var map_c61b342e70deea09a0734350221e1318 = L.map(
+                "map_c61b342e70deea09a0734350221e1318",
+                {
+                    center: [21.174364030859888, 73.06735784024302],
+                    crs: L.CRS.EPSG3857,
+                    zoom: 10,
+                    zoomControl: true,
+                    preferCanvas: false,
+                }
+            );
+
+            
+
+        
+    
+            var tile_layer_9a692e000cfa98eab120be7ef771ef0c = L.tileLayer(
+                "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
+                {"attribution": "\u0026copy; \u003ca href=\"https://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors", "detectRetina": false, "maxNativeZoom": 19, "maxZoom": 19, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
+            );
+        
+    
+            tile_layer_9a692e000cfa98eab120be7ef771ef0c.addTo(map_c61b342e70deea09a0734350221e1318);
+        
+    
+            var marker_325f7aaaf407ede9431ef3e94b478da8 = L.marker(
+                [21.174364030859888, 73.06735784024302],
+                {}
+            ).addTo(map_c61b342e70deea09a0734350221e1318);
+        
+    
+        var popup_ba5ff7c693e7132352eab20dde701d9f = L.popup({"maxWidth": 300});
+
+        
+            
+                var html_504edff652c09db11dfbb4099d86edf5 = $(`<div id="html_504edff652c09db11dfbb4099d86edf5" style="width: 100.0%; height: 100.0%;"><img 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"></div>`)[0];
+                popup_ba5ff7c693e7132352eab20dde701d9f.setContent(html_504edff652c09db11dfbb4099d86edf5);
+            
+        
+
+        marker_325f7aaaf407ede9431ef3e94b478da8.bindPopup(popup_ba5ff7c693e7132352eab20dde701d9f)
+        ;
+
+        
+    
+    
+            marker_325f7aaaf407ede9431ef3e94b478da8.bindTooltip(
+                `<div>
+                     Bardoli
+                 </div>`,
+                {"sticky": true}
+            );
+        
+</script>
+</html>

templates/results.html

@@ -0,0 +1,45 @@
+<!-- templates/results.html -->
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Wind Speed Prediction Results</title>
+    <link href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" rel="stylesheet">
+    <style>
+        body {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            min-height: 100vh;
+            background-color: #f8f9fa;
+        }
+        .container {
+            background-color: #ffffff;
+            padding: 30px;
+            border-radius: 8px;
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
+        }
+        h1, h2 {
+            text-align: center;
+            color: #343a40;
+        }
+        p {
+            font-size: 1.2em;
+            color: #555;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Wind Speed Prediction Results</h1>
+        <p><strong>Actual Wind Speed:</strong> {{ actual }} km/h</p>
+        <p><strong>Predicted Wind Speed:</strong> {{ predicted }} km/h</p>
+        <h2>Location Map</h2>
+        <iframe src="{{ url_for('static', filename=map_file) }}" width="100%" height="500px" style="border: none; border-radius: 8px;"></iframe>
+    </div>
+
+    <script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@popperjs/[email protected]/dist/umd/popper.min.js"></script>
+    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
+</body>
+</html>