add webapp

webapp.py

@@ -0,0 +1,83 @@
+import altair as alt
+import pandas as pd
+import streamlit as st
+import hopsworks
+
+# Connect to Hopsworks
+project = hopsworks.login()
+fs = project.get_feature_store()
+dataset_api = project.get_dataset_api()
+
+# download the forecast to the local environment
+dataset_api.download('Resources/weather_forecast/forecast.csv', overwrite=True)
+
+# Read CSV file without setting any column as the index
+df = pd.read_csv('resources/forecast.csv', index_col=None)
+
+#df_mapping = pd.read_csv('resources/weather_code_mapping.csv', index_col=None)
+#df_mapping = df_mapping[['weather_code', 'weather_code_group']]
+# Merge DataFrames on the 'weather_code' column
+#df = pd.merge(df, df_mapping, left_on='weather_code_prediction', right_on='weather_code', how='left')
+#df['weather_code_group_label'] = df['weather_code'].astype(str) + '-' + df['weather_code_group']
+
+# Drop the index column if it exists
+df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
+
+df_print = df[['date', 'temperature_min', 'precipitation_sum', 'wind_gusts_max', 'weather_code_prediction','weather_code_group']]
+df_print.temperature_min = df_print.temperature_min.round()
+df_print.precipitation_sum = df_print.precipitation_sum.round()
+df_print.wind_gusts_max = df_print.wind_gusts_max.round()
+df_print.rename(columns = {'date':'Day',
+                           'temperature_min':'Temperature Min [°C]',
+                           'precipitation_sum':'Precipitation Sum [mm]',
+                           'wind_gusts_max':'Wind Gusts Max [km/h]',
+                           'weather_code_prediction':'Weather code',
+                           'weather_code_group':'Weather'
+                          }, inplace = True)
+
+
+st.title('Weather Code Forecast for Stockholm')
+
+st.dataframe(df_print, hide_index=True,)
+
+# Define the base time-series chart.
+def get_chart(data):
+    hover = alt.selection_single(
+        fields=["date"],
+        nearest=True,
+        on="mouseover",
+        empty="none",
+    )
+
+    lines = (
+        alt.Chart(data, title="Evolution of Weather Code")
+        .mark_line()
+        .encode(
+            x=alt.X("date:T", title="Date", axis=alt.Axis(labelAngle=-60)),  # Adjust labelAngle as needed
+            y=alt.Y("weather_code_prediction:Q", title="Weather Code", scale=alt.Scale(domain=[1, 13]))
+        )
+    )
+
+    # Draw points on the line, and highlight based on selection
+    points = lines.transform_filter(hover).mark_circle(size=65)
+
+    # Draw a rule at the location of the selection
+    tooltips = (
+        alt.Chart(data)
+        .mark_rule()
+        .encode(
+            x=alt.X("yearmonthdate(date):T", title="Date"),
+            y=alt.Y("weather_code_prediction:Q", title="Weather Code", scale=alt.Scale(domain=[1, 13])),
+            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),
+            tooltip=[
+                alt.Tooltip("date:T", title="Date"),
+                alt.Tooltip("weather_code_label", title="Weather code"),
+                alt.Tooltip("weather_code_prediction", title="Weather code level"),
+            ],
+        )
+        .add_selection(hover)
+    )
+    return (lines + points + tooltips).interactive()
+
+st.altair_chart(get_chart(df).interactive(),
+    use_container_width=True)