Update src/streamlit_app.py

src/streamlit_app.py

@@ -41,7 +41,11 @@ DATA_PATH = Path(__file__).parent / "crime_data.csv"        # /app/src/crime_dat
 @st.cache_data
 def load_data():
     return pd.read_csv(DATA_PATH)
-
+    
+if st.button("🔄"):
+    st.cache_data.clear()  # Clear the cache
+    st.success("Data is refreshed")  # Reload the data
+    
 # 2. Load and early‐exit if missing
 df = load_data()
 if df.empty:
@@ -52,48 +56,8 @@ st.header("Data Preview")
 st.write(f"Total records: {df.shape[0]}  |  Total columns: {df.shape[1]}")
 st.dataframe(df.head())
 
-# # 4. Pie Chart 1: Top 10 Crime Types
-# st.header("Pie Chart 1: Top 10 Crime Types Distribution")
-
-# # Prepare Top 10 crime types
-# top_crimes = (
-#     df["crm_cd_desc"]
-#       .value_counts()                # Series indexed by crime type
-#       .nlargest(10)                  # keep only the top 10
-#       .rename_axis("Crime Type")     # name that index “Crime Type”
-#       .reset_index(name="Count")     # turn index into a column “Crime Type” and values into “Count”
-# )
-
-# # Verify your columns are exactly what you expect
-# st.dataframe(top_crimes)  # should show “Crime Type” and “Count” with correct numbers
-
-# # Now compute percentage
-# total = top_crimes["Count"].sum()
-# top_crimes["Percentage"] = top_crimes["Count"] / total
-# top_crimes["Count"] = pd.to_numeric(top_crimes["Count"], errors="coerce")
-
-# # And finally your pie‐chart…
-# chart1 = (
-#     alt.Chart(top_crimes)
-#     .mark_arc(innerRadius=50)
-#     .encode(
-#         theta=alt.Theta("Count:Q"),
-#         color=alt.Color("Crime Type:N", legend=alt.Legend(title="Crime Type")),
-#         tooltip=[
-#             alt.Tooltip("Crime Type:N"),
-#             alt.Tooltip("Count:Q"),
-#             alt.Tooltip("Percentage:Q", format=".1%")
-#         ]
-#     )
-#     .properties(width=400, height=400, title="Top 10 Crime Types")
-# )
-# st.altair_chart(chart1, use_container_width=True)
-
-###############################################################
-
 # Pie Chart 1: Top 10 Crime Types
 years = sorted(df["year"].dropna().astype(int).unique())
-# selected_year = st.selectbox("Select Year", years, index=len(years)-1)
 
 # Year filter (shorter, above chart)
 col_empty, col_filter = st.columns([3,1])
@@ -108,7 +72,6 @@ with col_filter:
 # Filter data for that year
 filtered = df[df["year"] == selected_year]
 
-
 # Compute top 10 crime types for that year ──
 top_crimes = (
     filtered["crm_cd_desc"]