Update src/streamlit_app.py

src/streamlit_app.py

@@ -91,29 +91,21 @@ 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()
-
-df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
-df["Year"] = df[date_col].dt.year
 
-# ── 4. Year filter (above chart) ──
-years = sorted(df["Year"].dropna().unique())
+# ── 3. Year filter (above chart) ──
+years = sorted(df["year"].dropna().astype(int).unique())
 selected_year = st.selectbox("Select Year", years, index=len(years)-1)
 
-filtered = df[df["Year"] == selected_year]
+# ── 4. Filter data for the selected year ──
+filtered = df[df["year"] == selected_year]
 
 # ── 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")
+      .value_counts()
+      .nlargest(10)
+      .rename_axis("Crime Type")
+      .reset_index(name="Count")
 )
 top_crimes["Percentage"] = top_crimes["Count"] / top_crimes["Count"].sum()