src/streamlit_app.py

import streamlit as st
import pandas as pd
import altair as alt
from pathlib import Path
import plotly.express as px

# ── 0. Page configuration ──
st.set_page_config(
    page_title="Analyze Crime Distributions", 
    page_icon="📊", 
    layout="wide"
)
st.markdown("""
        <style>
        .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)
    
st.markdown("<div class='title'><h1> LAPD Crime Insights Dashboard </h1></div>", unsafe_allow_html=True)

# 1. Page title
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.markdown("<div class='sectionheader'> Dataset Information </div>", unsafe_allow_html=True)
st.markdown(
    """
    <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
)

# 1. Resolve the path to the CSV next to this script
DATA_PATH = Path(__file__).parent / "crime_data.csv"        # /app/src/crime_data.csv
REGION_DATA_PATH = Path(__file__).parent / "area_lookup.csv"        # /app/src/crime_data.csv
@st.cache_data
def load_data():
    return pd.read_csv(DATA_PATH)
def region_load_data():
    return pd.read_csv(REGION_DATA_PATH)    
    
if st.button("🔄 Refresh Data"):
    st.cache_data.clear()  # Clear the cache
    st.toast("Data is refreshed",icon="✅")  # Reload the data
    
# 2. Load and early‐exit if missing
df = load_data()
lookup = region_load_data()
map_region   = dict(zip(lookup["OBJECTID"], lookup["APREC"]))
map_precinct = dict(zip(lookup["OBJECTID"], lookup["PREC"]))

# Map into new columns
df["RegionName"]   = df["area"].map(map_region)
df["PrecinctCode"] = df["area"].map(map_precinct)

# Inspect
print(df[["area", "RegionName", "PrecinctCode"]].head())

if df.empty:
    st.stop()
    
# 3. Data preview
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='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)
selected_year = st.selectbox("Select Year", options, index=0)
# 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":
    filtered = df.copy()
else:
    filtered = df[df["year"] == selected_year]

# 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")
)
top_crimes["Percentage"] = top_crimes["Count"] / top_crimes["Count"].sum()

#Key Metrics
st.markdown("### Key Metrics", unsafe_allow_html=True)
col1, col2, col3 = st.columns(3)
col1.metric(
    label="Total Incidents",
    value=f"{len(filtered):,}"
)
col2.metric(
    label="Unique Crime Types",
    value=f"{filtered['crm_cd_desc'].nunique():,}"
)
# compute share of the top crime
top_share = top_crimes.iloc[0]["Percentage"]  
col3.metric(
    label=f"Share of Top Crime ({top_crimes.iloc[0]['Crime Type']})",
    value=f"{top_share:.1%}"
)

# Plotly donut chart ──
fig = px.pie(
    top_crimes,
    names="Crime Type",
    values="Count",
    hole=0.4,
    color_discrete_sequence=px.colors.sequential.Agsunset,
    title="  "
)

fig.update_traces(
    textposition="outside",
    textinfo="label+percent",
    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=40, b=40, l=20, r=20),
    height=600,
    width=450,
    title_x=0.5
)

st.plotly_chart(fig, use_container_width=True)
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)