Update src/streamlit_app.py

src/streamlit_app.py

@@ -91,59 +91,53 @@ st.altair_chart(chart1, use_container_width=True)
 ###############################################################
 
 import plotly.express as px
+# ── 3. Parse year from date ──
+# Detect the column containing “date”
+date_col = next((c for c in df.columns if "date" in c.lower()), None)
+if date_col is None:
+    st.error("Could not find a date column in the dataset.")
+    st.stop()
 
-# 4. Pie Chart 1: Top 10 Crime Types
-st.header("Pie Chart 1: Top 10 Crime Types Distribution")
-
-st.sidebar.header("Filters")
-selected_year = st.sidebar.slider(
-    "Select Year",
-    int(df['Year'].min()),
-    int(df['Year'].max()),
-    int(df['Year'].max())
-)
+df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
+df["Year"] = df[date_col].dt.year
 
-# ── Filtered dataframe ──
-filtered = df[df['Year'] == selected_year]
+# ── 4. Year filter (above chart) ──
+years = sorted(df["Year"].dropna().unique())
+selected_year = st.selectbox("Select Year", years, index=len(years)-1)
 
-# ── Title with selected year ──
-st.title(f"Top 10 Crime Types in {selected_year}")
+filtered = df[df["Year"] == selected_year]
 
-# ── Compute top 10 crime types ──
+# ── 5. Compute top 10 crime types for that year ──
 top_crimes = (
-    filtered['crm_cd_desc']
-      .value_counts()
-      .nlargest(10)
-      .rename_axis('Crime Type')
-      .reset_index(name='Count')
+    filtered["crm_cd_desc"]
+    .value_counts()
+    .nlargest(10)
+    .rename_axis("Crime Type")
+    .reset_index(name="Count")
 )
-top_crimes["Count"] = pd.to_numeric(top_crimes["Count"], errors="coerce")
-top_crimes['Percentage'] = top_crimes['Count'] / top_crimes['Count'].sum()
+top_crimes["Percentage"] = top_crimes["Count"] / top_crimes["Count"].sum()
 
-# ── Plotly pie chart ──
+# ── 6. Plotly donut chart ──
 fig = px.pie(
     top_crimes,
-    names='Crime Type',
-    values='Count',
+    names="Crime Type",
+    values="Count",
     hole=0.4,
-    color_discrete_sequence=px.colors.qualitative.Safe,  # prettier palette
+    color_discrete_sequence=px.colors.qualitative.Safe,
+    title=f"Top 10 Crime Types in {selected_year}"
 )
 
-# ── Style the slices ──
 fig.update_traces(
-    textposition='outside',        # labels outside slices
-    textinfo='label+percent',      # show both name and percent
-    pull=[0.05]*len(top_crimes),   # slight “pull” for all slices
-    marker=dict(line=dict(color='white', width=2))  # white borders
+    textposition="outside",
+    textinfo="label+percent",
+    pull=[0.05] * len(top_crimes),
+    marker=dict(line=dict(color="white", width=2))
 )
 
-# ── Layout adjustments ──
 fig.update_layout(
-    showlegend=True,
-    legend_title_text='Crime Type',
+    legend_title_text="Crime Type",
     margin=dict(t=60, b=20, l=20, r=20),
-    title_x=0.5                    # center the main title
+    title_x=0.5
 )
 
-# ── Render in Streamlit ──
 st.plotly_chart(fig, use_container_width=True)