Update app.py

app.py

@@ -1,73 +1,56 @@
-# INSTRUCTIONS:
-# 1. Open a "Terminal" by: View --> Terminal OR just the "Terminal" through the hamburger menu
-# 2. run in terminal with: streamlit run app.py
-# 3. click the "Open in Browser" link that pops up OR click on "Ports" and copy the URL
-# 4. Open a Simple Browswer with View --> Command Palette --> Simple Browser: Show
-# 5. use the URL from prior steps as intput into this simple browser
-
-
 import streamlit as st
+import pandas as pd
 import altair as alt
-from vega_datasets import data
- 
-st.title('My First Streamlit App')
 
-st.text("The URL for this app is: https://huggingface.co/spaces/jnaiman/is445_demo")
+# Load dataset
+DATA_URL = "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/building_inventory.csv"
 
-source = data.seattle_weather()
+@st.cache
+def load_data():
+    return pd.read_csv(DATA_URL)
 
-scale = alt.Scale(
-    domain=["sun", "fog", "drizzle", "rain", "snow"],
-    range=["#e7ba52", "#a7a7a7", "#aec7e8", "#1f77b4", "#9467bd"],
-)
-color = alt.Color("weather:N", scale=scale)
+df = load_data()
 
-# We create two selections:
-# - a brush that is active on the top panel
-# - a multi-click that is active on the bottom panel
-brush = alt.selection_interval(encodings=["x"])
-click = alt.selection_point(encodings=["color"])
+# Streamlit app title and description
+st.title("Building Inventory Visualization")
+st.markdown("""
+This app visualizes the building inventory dataset. 
+Use the controls in the sidebar to explore the data.
+""")
 
-# Top panel is scatter plot of temperature vs time
-points = (
-    alt.Chart()
-    .mark_point()
-    .encode(
-        alt.X("monthdate(date):T", title="Date"),
-        alt.Y(
-            "temp_max:Q",
-            title="Maximum Daily Temperature (C)",
-            scale=alt.Scale(domain=[-5, 40]),
-        ),
-        color=alt.condition(brush, color, alt.value("lightgray")),
-        size=alt.Size("precipitation:Q", scale=alt.Scale(range=[5, 200])),
-    )
-    .properties(width=550, height=300)
-    .add_params(brush)
-    .transform_filter(click)
+# Sidebar for user input
+st.sidebar.header("Filter Options")
+state_filter = st.sidebar.multiselect(
+    "Select State(s):",
+    options=df["State"].dropna().unique(),
+    default=df["State"].dropna().unique()
 )
 
-# Bottom panel is a bar chart of weather type
-bars = (
-    alt.Chart()
-    .mark_bar()
-    .encode(
-        x="count()",
-        y="weather:N",
-        color=alt.condition(click, color, alt.value("lightgray")),
-    )
-    .transform_filter(brush)
-    .properties(
-        width=550,
-    )
-    .add_params(click)
+agency_filter = st.sidebar.multiselect(
+    "Select Agency(ies):",
+    options=df["Agency Name"].dropna().unique(),
+    default=df["Agency Name"].dropna().unique()
 )
 
-chart = alt.vconcat(points, bars, data=source, title="Seattle Weather: 2012-2015")
-
-tab1, tab2 = st.tabs(["Streamlit theme (default)", "Altair native theme"])
+# Apply filters to the data
+filtered_data = df[df["State"].isin(state_filter) & df["Agency Name"].isin(agency_filter)]
+
+# Display filtered data table
+st.subheader("Filtered Data Table")
+st.write(filtered_data)
+
+# Bar chart of buildings by agency
+st.subheader("Buildings by Agency")
+agency_building_counts = filtered_data["Agency Name"].value_counts().reset_index()
+agency_building_counts.columns = ["Agency Name", "Building Count"]
+
+chart = alt.Chart(agency_building_counts).mark_bar().encode(
+    x=alt.X("Building Count", title="Number of Buildings"),
+    y=alt.Y("Agency Name", sort="-x", title="Agency Name"),
+    tooltip=["Agency Name", "Building Count"]
+).properties(
+    width=800,
+    height=400
+)
 
-with tab1:
-    st.altair_chart(chart, theme="streamlit", use_container_width=True)
-with tab2:
-    st.altair_chart(chart, theme=None, use_container_width=True)
+st.altair_chart(chart, use_container_width=True)