import streamlit as st
import pandas as pd
import altair as alt
import json
import urllib.request

# ── Page config ───────────────────────────────────────────────────────────────
st.set_page_config(
    page_title="Crimes in Chicago 2026",
    page_icon="🔍",
    layout="wide",
)

# ── Header ────────────────────────────────────────────────────────────────────
st.title("🔍 Crimes in Chicago — 2026")
st.markdown("**Authors: Xinyi Chen, Zhongyin Wang** · Group 6")
st.markdown("---")

# ── Introduction ──────────────────────────────────────────────────────────────
st.markdown(
    """
    ## What Is This About?

    Every day, hundreds of crime incidents are reported across Chicago's 77 community areas.
    But where do they happen? At what time? And does poverty play a role?

    This interactive article walks you through 2026 Chicago crime data — drawn directly from
    the [Chicago Data Portal](https://data.cityofchicago.org/) — to help you explore the
    geography, timing, and social context of crime in one of America's largest cities.

    The dataset records every reported crime incident in 2026, including the exact location,
    date and time, crime type, and the police district that handled it. Each row is one
    reported incident. We also include community-level socioeconomic data to examine the
    relationship between poverty and crime rates across Chicago's neighborhoods.
    """
)

# ── Data loading ──────────────────────────────────────────────────────────────
@st.cache_data(show_spinner="Loading Chicago crime data…")
def load_crime_data():
    url = (
        "https://data.cityofchicago.org/resource/ijzp-q8t2.json"
        "?$where=year=2026"
        "&$limit=10000"
        "&$order=date%20DESC"
    )
    try:
        df = pd.read_json(url)
    except Exception as e:
        st.error(f"Failed to load crime data: {e}")
        return pd.DataFrame()

    df["date"] = pd.to_datetime(df["date"], errors="coerce")
    for col in ["latitude", "longitude"]:
        df[col] = pd.to_numeric(df.get(col, pd.Series(dtype=float)), errors="coerce")
    df = df.dropna(subset=["latitude", "longitude", "date"])

    df["Date_Only"] = df["date"].dt.floor("d")
    df["Hour"]      = df["date"].dt.hour
    df["weekday"]   = df["date"].dt.day_name().str[:3]

    if "primary_type" in df.columns:
        df["Primary Type"] = df["primary_type"].str.upper()
    else:
        df["Primary Type"] = "UNKNOWN"

    if "district" in df.columns:
        df["District_Str"] = (
            pd.to_numeric(df["district"], errors="coerce")
            .fillna(-1).astype(int).astype(str)
        )
        df["District"] = df["District_Str"]
    else:
        df["District_Str"] = "-1"
        df["District"]     = "-1"

    if "community_area" not in df.columns:
        df["community_area"] = None

    # Period column
    def get_period(hour):
        if 6 < hour <= 12:
            return "Morning (6am-12pm)"
        elif 12 < hour <= 18:
            return "Afternoon (12pm-6pm)"
        elif 18 < hour <= 24:
            return "Evening (6pm-12am)"
        else:
            return "Late Night (12am-6am)"

    df["Period"] = df["Hour"].apply(get_period)
    return df


@st.cache_data(show_spinner="Loading socioeconomic data…")
def load_socio():
    url = "https://data.cityofchicago.org/resource/kn9c-c2s2.json"
    try:
        df = pd.read_json(url)
        df = df.dropna(subset=["ca"])
        df["ca"] = df["ca"].astype(float).astype(int).astype(str)
        df["poverty_rate"] = pd.to_numeric(df["percent_households_below_poverty"], errors="coerce")
        return df
    except Exception as e:
        st.warning(f"Could not load socioeconomic data: {e}")
        return pd.DataFrame(columns=["ca", "community_area_name", "poverty_rate"])


@st.cache_data(show_spinner="Loading boundaries…")
def load_geojson(url):
    try:
        with urllib.request.urlopen(url) as r:
            return json.loads(r.read())
    except Exception as e:
        st.warning(f"Could not load GeoJSON: {e}")
        return {"features": []}


district_geojson  = load_geojson("https://data.cityofchicago.org/resource/24zt-jpfn.geojson")
community_geojson = load_geojson("https://data.cityofchicago.org/resource/igwz-8jzy.geojson")

df       = load_crime_data()
df_socio = load_socio()

districts   = alt.Data(values=district_geojson["features"])
communities = alt.Data(values=community_geojson["features"])

if df.empty:
    st.error("⚠️ Crime data could not be loaded. Please check the Chicago Data Portal.")
    st.stop()

# ═══════════════════════════════════════════════════════════════════════════════
# SECTION 1 — Linked dashboard (map | bar chart) & time-of-day line chart
# ═══════════════════════════════════════════════════════════════════════════════
st.markdown("---")
st.header("🗺️ Interactive Crime Dashboard")
st.markdown(
    """
    This dashboard lets you explore Chicago crime data across three linked views.
    **Drag a box on the map** to select a geographic area, or **click a district boundary**
    to highlight it — both actions filter the bar chart on the right and the timeline below.
    You can also **click a crime category** in the bar chart to drill into its temporal trend.

    The bottom line chart breaks daily incident counts into four time-of-day periods
    (plus a total), so you can see not just *where* crime happens but *when* it peaks.
    """
)

# Altair selections (note: cross-chart filtering via selections only works when
# the entire compound chart is rendered as one Altair object, which st.altair_chart supports)
brush      = alt.selection_interval(name="brush")
click_type = alt.selection_point(fields=["Primary Type"], name="click_type")
click_dist = alt.selection_point(fields=["District_Str"], name="click_dist")

# ── Map layer ─────────────────────────────────────────────────────────────────
background = (
    alt.Chart(districts)
    .mark_geoshape(stroke="black", strokeWidth=0.6)
    .transform_calculate(District_Str="datum.properties.dist_num")
    .encode(
        color=alt.condition(click_dist, alt.value("white"), alt.value("grey")),
        opacity=alt.condition(click_dist, alt.value(0.5), alt.value(0.8)),
        tooltip=[alt.Tooltip("properties.dist_num:N", title="District")],
    )
    .add_params(click_dist)
)

geo_points = (
    alt.Chart(df)
    .mark_circle(size=5)
    .encode(
        longitude="longitude:Q",
        latitude="latitude:Q",
        color=alt.condition(
            click_dist,
            alt.Color(
                "District:N",
                scale=alt.Scale(scheme="tableau10"),
                legend=alt.Legend(title="District", orient="right"),
            ),
            alt.value("#e0dbd6"),
        ),
        opacity=alt.condition(click_dist, alt.value(0.6), alt.value(0.05)),
        tooltip=[
            alt.Tooltip("Primary Type:N", title="Crime Type"),
            alt.Tooltip("District:N", title="District"),
            alt.Tooltip("date:T", title="Date"),
        ],
    )
    .add_params(brush)
)

map_layer = (background + geo_points).project(type="mercator").properties(
    width=420, height=450,
    title="Chicago Crime Map (Brush to select area / Click district)",
)

# ── Crime-type bar chart ──────────────────────────────────────────────────────
type_chart = (
    alt.Chart(df)
    .mark_bar()
    .encode(
        x=alt.X("count():Q", title="Number of Crimes"),
        y=alt.Y("Primary Type:N", sort="-x", title="Crime Type"),
        color=alt.condition(click_type, alt.value("steelblue"), alt.value("lightgray")),
        tooltip=["Primary Type:N", "count():Q"],
    )
    .properties(width=300, height=450, title="Crime Types")
    .add_params(click_type)
    .transform_filter(brush)
    .transform_filter(click_dist)
)

# ── Time-of-day line chart ────────────────────────────────────────────────────
period_order = [
    "Morning (6am-12pm)",
    "Afternoon (12pm-6pm)",
    "Evening (6pm-12am)",
    "Late Night (12am-6am)",
    "Total Daily",
]
period_range = ["#f4a261", "#e9c46a", "#e76f51", "#264653", "grey"]

period_lines = (
    alt.Chart(df)
    .mark_line(point=False, strokeWidth=1.5)
    .encode(
        x=alt.X("Date_Only:T", title="Timeline"),
        y=alt.Y("count:Q", title="Number of Incidents", scale=alt.Scale(zero=True)),
        color=alt.Color(
            "Period:N",
            scale=alt.Scale(domain=period_order, range=period_range),
            legend=alt.Legend(title="Time of Day", orient="right"),
        ),
        tooltip=[
            alt.Tooltip("Date_Only:T", title="Date"),
            alt.Tooltip("Period:N", title="Period"),
            alt.Tooltip("count:Q", title="Incidents"),
        ],
    )
    .transform_filter(brush)
    .transform_filter(click_type)
    .transform_filter(click_dist)
    .transform_aggregate(count="count()", groupby=["Date_Only", "Period"])
    .transform_impute(impute="count", key="Date_Only", groupby=["Period"], value=0)
)

total_line = (
    alt.Chart(df)
    .mark_line(opacity=0.5)
    .encode(
        x=alt.X("Date_Only:T"),
        y=alt.Y("count():Q"),
        color=alt.datum("Total Daily"),
        tooltip=[
            alt.Tooltip("Date_Only:T", title="Date"),
            alt.Tooltip("count():Q", title="Total Incidents"),
        ],
    )
    .transform_filter(brush)
    .transform_filter(click_type)
    .transform_filter(click_dist)
)

line_chart = (total_line + period_lines).properties(
    width=760, height=220,
    title="Daily Crime Trend by Time of Day",
).resolve_scale(color="shared")

# ── Compose full dashboard ────────────────────────────────────────────────────
dashboard = ((map_layer | type_chart) & line_chart).resolve_scale(color="independent")
st.altair_chart(dashboard, use_container_width=True)

# ═══════════════════════════════════════════════════════════════════════════════
# SECTION 2 — When do crimes happen? (standalone heatmap + dropdown)
# ═══════════════════════════════════════════════════════════════════════════════
st.markdown("---")
st.header("🕐 When Do Crimes Happen in Chicago?")
st.markdown(
    """
    Different crimes follow different schedules. Use the **dropdown below** to filter
    the heatmap to a specific crime category — or leave it on *All* to see the
    overall pattern. Each cell shows the total number of incidents at that
    day-of-week × hour-of-day combination; darker red means more incidents.

    Across nearly every category, Friday and Saturday evenings (6 pm – midnight)
    stand out as the most active windows, while the early morning hours (2–5 am)
    are quietest — except for a few crime types that peak overnight.
    """
)

top_types_hm = df["Primary Type"].value_counts().head(10).index.tolist()
selected_hm  = st.selectbox(
    "Select Crime Type",
    options=["All"] + top_types_hm,
    index=0,
)

hm_df = df if selected_hm == "All" else df[df["Primary Type"] == selected_hm]
weekday_order = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]

heatmap = (
    alt.Chart(hm_df)
    .mark_rect()
    .encode(
        x=alt.X("weekday:N", sort=weekday_order, title="Day of Week"),
        y=alt.Y("Hour:O", title="Hour of Day (0–23)", sort="ascending"),
        color=alt.Color(
            "count():Q",
            scale=alt.Scale(scheme="reds"),
            title="Number of Crimes",
        ),
        tooltip=[
            alt.Tooltip("weekday:N", title="Day"),
            alt.Tooltip("Hour:O", title="Hour"),
            alt.Tooltip("count():Q", title="Total Crimes"),
        ],
    )
    .properties(
        width=700, height=380,
        title=alt.TitleParams(
            text=f"Crime Heatmap — {selected_hm}",
            subtitle="Select a crime type above to filter · Darker = more incidents",
            fontSize=14,
        ),
    )
)
st.altair_chart(heatmap, use_container_width=True)

# ═══════════════════════════════════════════════════════════════════════════════
# SECTION 3 — Poverty vs. Crime
# ═══════════════════════════════════════════════════════════════════════════════
st.markdown("---")
st.header("💸 Does Poverty Predict Crime?")
st.markdown(
    """
    Socioeconomic inequality is one of the most studied predictors of crime at the
    neighborhood level. The choropleth map on the left shades each of Chicago's 77
    community areas by their poverty rate — darker orange means higher poverty —
    with crime incident dots overlaid in blue.

    A visual comparison suggests that some of the highest-crime community areas,
    particularly on the South and West sides, also carry the heaviest poverty burden.
    The scatter plot on the right makes this relationship explicit: each dot is one
    community area, and the dashed line is a statistical trend. There is a moderate
    positive correlation, though it is far from deterministic — policy, policing
    patterns, and reporting rates all play a role.

    **Socioeconomic data source:** [Census Data — Chicago Data Portal](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-Socioeconomic-Indicators-in-C/kn9c-c2s2)
    """
)

col3, col4 = st.columns(2)

with col3:
    if not df_socio.empty and community_geojson["features"]:
        poverty_map = (
            alt.Chart(communities)
            .mark_geoshape(stroke="white", strokeWidth=0.4)
            .transform_lookup(
                lookup="properties.area_num_1",
                from_=alt.LookupData(df_socio, "ca", ["poverty_rate", "community_area_name"]),
            )
            .encode(
                color=alt.Color(
                    "poverty_rate:Q",
                    scale=alt.Scale(scheme="orangered"),
                    title="Poverty Rate (%)",
                ),
                tooltip=[
                    alt.Tooltip("properties.community:N", title="Community"),
                    alt.Tooltip("poverty_rate:Q", title="Poverty Rate (%)", format=".1f"),
                ],
            )
            .project(type="mercator")
            .properties(width=360, height=440, title="Chicago Poverty Rate by Community Area")
        )
        crime_overlay = (
            alt.Chart(df.sample(min(5000, len(df)), random_state=42))
            .mark_circle(size=3, color="steelblue", opacity=0.3)
            .encode(longitude="longitude:Q", latitude="latitude:Q")
        )
        st.altair_chart(poverty_map + crime_overlay, use_container_width=True)
    else:
        st.info("Socioeconomic or boundary data unavailable.")

with col4:
    if not df_socio.empty and df["community_area"].notna().any():
        df_crime_count = (
            df.dropna(subset=["community_area"])
            .groupby("community_area").size()
            .reset_index(name="crime_count")
        )
        df_crime_count["ca"] = (
            df_crime_count["community_area"].astype(float).astype(int).astype(str)
        )
        df_scatter = pd.merge(
            df_socio[["ca", "community_area_name", "poverty_rate"]],
            df_crime_count[["ca", "crime_count"]],
            on="ca", how="inner",
        )
        if len(df_scatter) > 5:
            sc = (
                alt.Chart(df_scatter)
                .mark_circle(size=80, opacity=0.75)
                .encode(
                    x=alt.X("poverty_rate:Q", title="Poverty Rate (%)"),
                    y=alt.Y("crime_count:Q", title="Crime Count (2026)"),
                    color=alt.Color(
                        "poverty_rate:Q",
                        scale=alt.Scale(scheme="orangered"),
                        legend=None,
                    ),
                    tooltip=[
                        alt.Tooltip("community_area_name:N", title="Community"),
                        alt.Tooltip("poverty_rate:Q", title="Poverty Rate (%)", format=".1f"),
                        alt.Tooltip("crime_count:Q", title="Crime Count"),
                    ],
                )
            )
            reg = sc.transform_regression("poverty_rate", "crime_count").mark_line(
                color="gray", strokeDash=[4, 4], strokeWidth=1.5
            )
            st.altair_chart(
                (sc + reg).properties(
                    width=360, height=440,
                    title=alt.TitleParams(
                        text="Higher Poverty → More Crimes?",
                        subtitle="Each dot = one community area  |  Dashed = trend",
                        fontSize=13,
                    ),
                ),
                use_container_width=True,
            )
        else:
            st.info("Not enough community-level overlap to render scatter plot.")
    else:
        st.info("Community area data not available in this dataset sample.")

# ── Citations ─────────────────────────────────────────────────────────────────
st.markdown("---")
st.header("📚 Data Sources & Citations")
st.markdown(
    """
    | Dataset | Source | Link |
    |---|---|---|
    | Chicago Crimes 2001–Present | City of Chicago Data Portal | [ijzp-q8t2](https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-Present/ijzp-q8t2) |
    | Socioeconomic Indicators by Community | City of Chicago Data Portal | [kn9c-c2s2](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-Socioeconomic-Indicators-in-C/kn9c-c2s2) |
    | Police District Boundaries (GeoJSON) | City of Chicago Data Portal | [24zt-jpfn](https://data.cityofchicago.org/Public-Safety/Boundaries-Police-Districts-current-/24zt-jpfn) |
    | Community Area Boundaries (GeoJSON) | City of Chicago Data Portal | [igwz-8jzy](https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Community-Areas-current-/cauq-8yn6) |

    All data accessed April 2026. Visualizations built with [Altair](https://altair-viz.github.io/) and [Streamlit](https://streamlit.io/).
    """
)