Update src/streamlit_app.py

src/streamlit_app.py

@@ -44,24 +44,53 @@ st.markdown("""
         .title {
             text-align: center;
             padding: 25px;
+            color: #2c3e50;
+            font-family: 'Source Sans Pro', sans-serif;
+        }
+        /* Paragraph/write-up styling */
+      .description {
+        font-size: 18px;           /* comfortable reading size */
+        line-height: 1.6;          /* good spacing */
+        color: #4b4b4b;            /* dark grey text */
+        text-align: justify;       /* nice full-justified look */
+        padding: 0 10px 20px;      /* side & bottom padding */
+        font-family: 'Helvetica Neue', Arial, sans-serif;
+      }
+      .sectionheader {
+          font-family: 'Source Sans Pro', sans-serif;
+          font-size: 32px;
+          color: #2c3e50;
+          margin-top: 15px;
+          margin-bottom: 10px;
+          border-bottom: 3px solid #ccc;
+          padding-bottom: 8px;
         }
         </style>
         """, unsafe_allow_html=True)
-        
+
+# 1. Page title
 st.markdown("<div class='title'><h1> LAPD Crime Insights Dashboard </h1></div>", unsafe_allow_html=True)
     
-# 1. Page title
-st.markdown(""" This application provides a suite of interactive visualizations—pie charts, bar charts, scatter plots, and more—that let you explore crime patterns in the LAPD dataset from multiple angles. Quickly see which offense categories dominate, compare arrest rates against non-arrests, track how crime volumes change over time, and examine geographic hotspots. These insights can help police departments, community organizations, and policymakers allocate resources more effectively and design targeted strategies to improve public safety.""")
-                
+st.markdown("""<div class='description'> This application provides a suite of interactive visualizations—pie charts, 
+bar charts, scatter plots, and more—that let you explore crime patterns in the LAPD dataset from multiple angles. 
+Quickly see which offense categories dominate, compare arrest rates against non-arrests, track how crime volumes change over time, and examine geographic hotspots. 
+These insights can help police departments, community organizations, and policymakers allocate resources more effectively and 
+design targeted strategies to improve public safety.</div>""",unsafe_allow_html=True)
+
 # 2. Data info & load
-st.header("Dataset Information")
+st.markdown("<div class='sectionheader'> Dataset Information </div>", unsafe_allow_html=True)
 st.markdown(
-"""
-- **Source:** LAPD crime incidents dataset  
-- **Rows:** incidents (one per row)  
-- **Columns:** e.g. `crm_cd_desc` (crime type), `arrest` (boolean), `date`, `location_description`, etc.  
-- **Purpose:** Interactive exploration of top crime categories and arrest rates.
-"""
+    """
+    <div class="description">
+      <ul>
+        <li><strong>Source:</strong> LAPD crime incidents dataset</li>
+        <li><strong>Rows:</strong> one incident per row</li>
+        <li><strong>Columns:</strong> e.g. <code>crm_cd_desc</code> (crime type), <code>arrest</code> (boolean), <code>year</code>, <code>location_description</code>, etc.</li>
+        <li><strong>Purpose:</strong> Interactive exploration of top crime categories and arrest rates.</li>
+      </ul>
+    </div>
+    """,
+    unsafe_allow_html=True
 )
 
 # # Define paths.
@@ -82,7 +111,7 @@ st.markdown(
 def load_data():
     return pd.read_csv(DATA_PATH)
     
-if st.button("🔄"):
+if st.button("🔄 Refresh Data"):
         st.cache_data.clear()  # Clear the cache
         st.toast("Data is refreshed",icon="✅")  # Reload the data
         
@@ -92,26 +121,32 @@ if df.empty:
     st.stop()
         
 # 3. Data preview
-st.header("Data Preview")
-st.write(f"Total records: {df.shape[0]}  |  Total columns: {df.shape[1]}")
+st.markdown("<div class='sectionheader'> Data Preview </div>", unsafe_allow_html=True)
+st.markdown(
+    f"<div class='description'>"
+    f"Total records: <strong>{df.shape[0]:,}</strong> &nbsp;|&nbsp; "
+    f"Total columns: <strong>{df.shape[1]:,}</strong>"
+    f"</div>",
+    unsafe_allow_html=True
+)
 st.dataframe(df.head())
 
 # Pie Chart 1: Top 10 Crime Types
-st.markdown("<div class='title'><h1> Top 10 Crime Type </h1></div>", unsafe_allow_html=True)
+st.markdown("<div class='sectionheader'> Top 10 Crime Types by Year </div>", unsafe_allow_html=True)
     
 years = sorted(df["year"].dropna().astype(int).unique())
 # Prepend an “All” option
 options = ["All"] + years
-    
-# Year filter (shorter, above chart)
-col_empty, col_filter = st.columns([3,1])
-with col_filter:
-    selected_year = st.selectbox(
-        "Select Year",
-        options=options,
-        index=0,  # default to “All”
-        key="year_filter"
-    )
+selected_year = st.selectbox("Select Year", options, index=0)    
+# # Year filter (shorter, above chart)
+# col_empty, col_filter = st.columns([3,1])
+# with col_filter:
+#     selected_year = st.selectbox(
+#         "Select Year",
+#         options=options,
+#         index=0,  # default to “All”
+#         key="year_filter"
+#     )
     
 # Filter according to selection
 if selected_year == "All":
@@ -161,14 +196,15 @@ fig = px.pie(
 fig.update_traces(
     textposition="outside",
     textinfo="label+percent",
-    pull=[0.05] * len(top_crimes),
-    marker=dict(line=dict(color="white", width=2))
+    pull=[0.02] * len(top_crimes),
+    marker=dict(line=dict(color="white", width=1))
 )
     
 fig.update_layout(
     legend_title_text="Crime Type",
-    margin=dict(t=60, b=20, l=20, r=20),
-    height=700,
+    margin=dict(t=40, b=40, l=20, r=20),
+    height=600,
+    width=450,
     title_x=0.5
 )
     
@@ -176,7 +212,11 @@ fig.update_layout(
 st.plotly_chart(fig, use_container_width=True)
     
 # Description.
-st.markdown(""" The donut chart shows the share of the ten most frequent crime categories in the selected year. At the center, you can see that Vehicle ­– Stolen is the single largest slice, accounting for roughly 18.7% of all incidents, The remaining five categories each represent between 3%–5% of total incidents—these include miscellaneous crimes, criminal threats, assault with a deadly weapon, burglary, and minor vandalism. By displaying both slice size and percentage labels, the chart makes it easy to compare how dominant property‐related offenses are, versus violent or lesser‐common crimes, in that year’s LAPD data. """)
+st.markdown("""<div class="description"> The donut chart shows the share of the ten most frequent crime categories in the selected year. 
+At the center, you can see that Vehicle ­– Stolen is the single largest slice, accounting for roughly 18.7% of all incidents, 
+The remaining five categories each represent between 3%–5% of total incidents—these include miscellaneous crimes, criminal threats, 
+assault with a deadly weapon, burglary, and minor vandalism. By displaying both slice size and percentage labels, the chart makes it easy 
+to compare how dominant property‐related offenses are, versus violent or lesser‐common crimes, in that year’s LAPD data.</div>""",unsafe_allow_html=True)
 
 
 # -------------------------------- Plot 2: Heat Map --------------------------------