Upload 9 files

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/cleaned_weather_data.csv filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,20 @@
+# Use the official Python image as a parent image
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 80 available to the world outside this container
+EXPOSE 5006
+
+# Define environment variable
+ENV BOKEH_ALLOW_WS_ORIGIN=localhost:5006
+
+# Run app.py when the container launches
+CMD ["bokeh", "serve", "--show", "dashboard/app.py"]

dashboard/app.py

@@ -0,0 +1,57 @@
+import panel as pn
+from panel.template import FastListTemplate
+from data_manager import DataManager
+from visualizations import temp_chart, create_aqi_plot, condition_pie_chart, create_wind_speed_plot
+from dashboard_components import create_location_selector, create_temp_unit_selector
+
+data_manager = DataManager('data/cleaned_weather_data.csv', )
+location_selector = create_location_selector(data_manager.weather['location_name'].unique().tolist())
+temp_unit_selector = create_temp_unit_selector(options=['Celsius', 'Fahrenheit'])
+
+@pn.depends(location_selector.param.value, temp_unit_selector.param.value)
+def update_plots(country, temp_unit):
+    # Generate plot figures
+    temp_plot = temp_chart(country, temp_unit)
+    aqi_plot = create_aqi_plot(country)
+    condition_pie_plot = condition_pie_chart(country)
+    wind_speed_plot = create_wind_speed_plot(country)
+
+    # Create Panel objects for each plot directly here
+    temp_plot_panel = pn.pane.Plotly(temp_plot)
+    aqi_plot_panel = pn.pane.Plotly(aqi_plot)
+    condition_pie_plot_panel = pn.pane.Plotly(condition_pie_plot)
+    wind_speed_plot_panel = pn.pane.Plotly(wind_speed_plot)
+
+    # Arrange the Panel objects in rows and columns
+    row1 = pn.Row(pn.Column(temp_unit_selector, pn.Row(temp_plot_panel, aqi_plot_panel)))
+    row2 = pn.Row(pn.Column(pn.Row(condition_pie_plot_panel, wind_speed_plot_panel)))
+
+    main_column = pn.Column(row1, row2)
+
+    return main_column
+
+# Template for the dashboard
+template = FastListTemplate(
+    title='Global Weather Overview Dashboard', 
+    sidebar=[
+        pn.pane.Markdown("# WorldWide Weather Analytics"), 
+        pn.pane.Markdown(" Global weather encompasses temperature changes, weather conditions, wind patterns,\
+                          and air quality, each significantly impacting ecosystems, human health, and agriculture.\
+                          Understanding these elements is crucial for managing environmental risks and enhancing \
+                          resilience against climatic variations."), 
+
+        pn.pane.PNG('clouds.png', sizing_mode='scale_both'),
+        pn.pane.Markdown("#### Settings"),   
+        location_selector
+    ],
+    main=[
+        # Dynamically update the plots based on user selections 
+        update_plots]
+    ,
+    header_background="#9381ff",
+    accent_base_color="#b8b8ff",
+    # background_color='#bbd1ea', 
+    main_layout='card')
+
+
+template.servable()

dashboard/dashboard_components.py

@@ -0,0 +1,7 @@
+import panel as pn
+
+def create_location_selector(options):
+    return pn.widgets.Select(name='Select Location', options=options)
+
+def create_temp_unit_selector(options):
+    return pn.widgets.RadioButtonGroup(name='Temperature Unit', options=options, button_type='primary')

dashboard/data_manager.py

@@ -0,0 +1,9 @@
+import pandas as pd
+
+class DataManager:
+    def __init__(self, file_path):
+        self.weather = pd.read_csv(file_path)
+
+    def get_location_data(self, location):
+        return self.weather[self.weather['location_name'] == location]
+    

dashboard/visualizations.py

@@ -0,0 +1,167 @@
+import plotly.express as px
+import pandas as pd
+from data_manager import DataManager
+
+data_manager = DataManager('data/cleaned_weather_data.csv')
+
+
+def temp_chart(location, temp_unit):
+    df = data_manager.get_location_data(location)
+    y_axis = 'Temperature (°C)' if temp_unit == 'Celsius' else 'Temperature (°F)'
+    fig = px.line(df, x='last_updated', y=y_axis, color_discrete_sequence=['#7158e2'])
+    fig.update_layout(
+        title={
+            'text': f'Temperature Trends over Time',
+            'y': 0.96,
+            'x': 0.5,
+            'xanchor': 'center',
+            'yanchor': 'top',
+            'font': {'color': 'RebeccaPurple', 'size': 20}  
+        },
+        xaxis=dict(
+            title='Date',
+            titlefont=dict(
+                family="Arial, sans-serif",  
+                size=15,                      
+                color="RebeccaPurple"       
+            )
+        ),
+        yaxis=dict(
+            title=y_axis,
+            titlefont=dict(
+                family="Arial, sans-serif", 
+                size=15,                     
+                color="RebeccaPurple"        
+            )
+        )
+    )
+
+
+    return fig
+
+
+# Aqi and Pollutants Concentration
+
+def create_aqi_plot(location):
+    data = data_manager.get_location_data(location)
+    air_quality_columns = [
+        'air_quality_Carbon_Monoxide', 'air_quality_Ozone', 'air_quality_Nitrogen_dioxide', 
+        'air_quality_Sulphur_dioxide', 'air_quality_PM2.5', 'air_quality_PM10'
+    ]
+    avg_concentration = data[air_quality_columns].mean()
+    readable_labels = {
+        'air_quality_Carbon_Monoxide': 'CO',
+        'air_quality_Ozone': 'O3',
+        'air_quality_Nitrogen_dioxide': 'NO2',
+        'air_quality_Sulphur_dioxide': 'SO2',
+        'air_quality_PM2.5': 'PM2.5',
+        'air_quality_PM10': 'PM10'
+    }
+    avg_concentration.index = [readable_labels[col] for col in avg_concentration.index]
+    avg_concentration = avg_concentration.sort_values(ascending=False).reset_index()
+    avg_concentration.columns = ['Pollutants', 'Average Concentration']
+    
+    fig = px.bar(avg_concentration, x='Pollutants', y='Average Concentration',
+                 title=f'Average Air Quality Indexes for {location}',color='Pollutants', 
+                 color_discrete_sequence=px.colors.qualitative.Set3,
+                 template='plotly_white'
+                )
+    fig.update_layout(showlegend=False)
+    fig.update_layout(
+        title={
+            'text': f'Average Air Quality ({location})',
+            'y': 0.96,
+            'x': 0.5,
+            'xanchor': 'center',
+            'yanchor': 'top',
+            'font': {'color': 'RebeccaPurple', 'size': 20}  
+        },
+        xaxis=dict(
+            title='Pollutants',
+            titlefont=dict(
+                family="Arial, sans-serif",  
+                size=15,                     
+                color="RebeccaPurple"        
+            )
+        ),
+        yaxis=dict(
+            title='Average Concentration',
+            titlefont=dict(
+                family="Arial, sans-serif",  
+                size=15,                      
+                color="RebeccaPurple"    
+            )
+        )
+    )
+    return fig
+
+# Weather Conditions
+def condition_pie_chart(location):
+    data = data_manager.get_location_data(location)
+    weather_counts = data['condition_text'].value_counts().head(4)
+    fig = px.pie(weather_counts, values=weather_counts.values, names=weather_counts.index,
+                 title='Weather Condition Distribution', hole=0.6, 
+                 color_discrete_sequence=px.colors.qualitative.Set3
+                )
+    fig.update_traces(textinfo='percent')
+    fig.update_layout(showlegend=True)
+    fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide')
+    fig.update_layout(
+        title={
+            'text': f'Weather Condition Distribution ',
+            'y':0.96,
+            'x':0.5,
+            'xanchor': 'center',
+            'yanchor': 'top', 
+            'font': {'color': 'RebeccaPurple', 'size': 20}})
+    fig.update_layout(
+        font=dict(
+            family="Arial, sans-serif",
+            size=12,
+            color="RebeccaPurple"
+        )
+    )
+    fig.update_layout(
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.01,
+            xanchor="auto",
+            x=0.9
+        )
+    )
+    return fig
+
+
+# Wind speed
+def create_wind_speed_plot(location):
+    data = data_manager.get_location_data(location)
+    fig = px.line(data, x='last_updated', y='Wind Speed (kph)', color_discrete_sequence=['#7158e2'])
+    fig.update_layout(
+            title={
+                'text': f'Wind Speed Trends',
+                'y': 0.96,
+                'x': 0.5,
+                'xanchor': 'center',
+                'yanchor': 'top',
+                'font': {'color': 'RebeccaPurple', 'size': 20}  
+            },
+            xaxis=dict(
+                title='Date',
+                titlefont=dict(
+                    family="Arial, sans-serif",  
+                    size=15,                      
+                    color="RebeccaPurple"         
+                )
+            ),
+            yaxis=dict(
+                title=f'Wind Speed (kph)',
+                titlefont=dict(
+                    family="Arial, sans-serif",  
+                    size=15,                      
+                    color="RebeccaPurple"    
+                )
+            )
+        )
+    return fig
+

data/cleaned_weather_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd0181bda730c9d45d3efdc339c430f66e1c059d5fe8a306c8c599f9b3f17688
+size 10742154