app.py

import streamlit as st
import pandas as pd

# ── Page Config ──
st.set_page_config(
    page_title="Race Results Explorer",
    page_icon="🏃",
    layout="wide",
    initial_sidebar_state="expanded",
)

# ── Custom CSS ──
st.markdown("""
<style>
    @import url('https://fonts.googleapis.com/css2?family=DM+Sans:wght@400;500;700&family=Space+Mono:wght@700&display=swap');

    .stApp {
        background: linear-gradient(145deg, #0f172a 0%, #1e293b 50%, #0f172a 100%);
    }

    [data-testid="stSidebar"] {
        background: linear-gradient(180deg, #1a1a2e 0%, #16213e 100%);
    }

    .main-title {
        font-family: 'Space Mono', monospace;
        font-size: 2.4rem;
        font-weight: 700;
        background: linear-gradient(90deg, #f97316, #fb923c, #fbbf24);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        text-align: center;
        margin-bottom: 0;
        letter-spacing: -1px;
    }

    .sub-title {
        text-align: center;
        color: #94a3b8;
        font-size: 0.95rem;
        margin-top: 0;
    }

    .metric-card {
        background: rgba(255,255,255,0.04);
        border: 1px solid rgba(255,255,255,0.08);
        border-radius: 12px;
        padding: 16px 20px;
        text-align: center;
    }

    .metric-val {
        font-family: 'Space Mono', monospace;
        font-size: 1.8rem;
        font-weight: 700;
    }

    .metric-label {
        font-size: 0.75rem;
        color: #64748b;
        text-transform: uppercase;
        letter-spacing: 2px;
        margin-top: 2px;
    }

    .sidebar-header {
        font-family: 'Space Mono', monospace;
        font-size: 1.1rem;
        color: #f97316;
        letter-spacing: 1px;
        margin-bottom: 8px;
    }

    div[data-testid="stDataFrame"] {
        border-radius: 12px;
        overflow: hidden;
    }
</style>
""", unsafe_allow_html=True)


# ── Data Loading ──
@st.cache_data
def load_data():
    df = pd.read_csv("new_data_01__2_.csv")

    # Drop the junk row at the bottom (non-numeric Place)
    df = df[pd.to_numeric(df["Place"], errors="coerce").notna()].copy()

    # Clean columns
    df["Place"] = df["Place"].astype(int)
    df["Bib"] = pd.to_numeric(df["Bib"], errors="coerce").astype("Int64")
    df["Gender"] = df["Gender"].str.strip().str.upper()
    df["City"] = df["City"].fillna("NOT SPECIFIED").str.strip().str.upper()
    df["State"] = df["State"].fillna("NOT SPECIFIED").str.strip().str.upper()
    df["Team"] = df["Team"].fillna("").str.strip()
    df["Time"] = df["Time"].astype(str).str.strip()
    df["Gun Time"] = df["Gun Time"].astype(str).str.strip()

    # Keep only valid genders
    df = df[df["Gender"].isin(["M", "F"])].copy()

    # Drop unnamed index column if present
    if "Unnamed: 0" in df.columns:
        df.drop(columns=["Unnamed: 0"], inplace=True)

    return df


df = load_data()

# ── Helper: parse time string to total seconds for filtering ──
def time_to_seconds(t):
    try:
        parts = str(t).split(":")
        if len(parts) == 3:
            h, m, s = parts
            return int(h) * 3600 + int(m) * 60 + int(s)
        elif len(parts) == 2:
            m, s = parts
            return int(m) * 60 + int(s)
    except Exception:
        return None
    return None


df["_time_sec"] = df["Time"].apply(time_to_seconds)
df["_gun_sec"] = df["Gun Time"].apply(time_to_seconds)

min_time = int(df["_time_sec"].min()) if df["_time_sec"].notna().any() else 0
max_time = int(df["_time_sec"].max()) if df["_time_sec"].notna().any() else 7200
min_gun = int(df["_gun_sec"].min()) if df["_gun_sec"].notna().any() else 0
max_gun = int(df["_gun_sec"].max()) if df["_gun_sec"].notna().any() else 7200


def seconds_to_label(s):
    h = s // 3600
    m = (s % 3600) // 60
    sec = s % 60
    if h > 0:
        return f"{h}:{m:02d}:{sec:02d}"
    return f"{m}:{sec:02d}"


# ══════════════════════════════════════
#  SIDEBAR – FILTER CHAMBER
# ══════════════════════════════════════
with st.sidebar:
    st.markdown('<div class="sidebar-header">🎯 FILTER CHAMBER</div>', unsafe_allow_html=True)
    st.markdown("---")

    # ── 1. Gender ──
    st.markdown("##### 👤 Gender")
    gender_opts = ["All"] + sorted(df["Gender"].unique().tolist())
    gender_sel = st.selectbox("Select Gender", gender_opts, index=0, label_visibility="collapsed")

    # ── 2. City ──
    st.markdown("##### 🏙️ City")
    city_list = sorted(df["City"].unique().tolist())
    city_sel = st.multiselect("Select City", city_list, default=[], placeholder="All cities")

    # ── 3. State ──
    st.markdown("##### 📍 State")
    state_list = sorted(df["State"].unique().tolist())
    state_sel = st.multiselect("Select State", state_list, default=[], placeholder="All states")

    # ── 4. Team ──
    st.markdown("##### 🏅 Team")
    team_mode = st.radio("Team filter mode", ["All Runners", "Team Members Only", "Independent Only", "Specific Team"], index=0, label_visibility="collapsed")
    specific_team = None
    if team_mode == "Specific Team":
        team_list = sorted([t for t in df["Team"].unique() if t])
        specific_team = st.selectbox("Choose team", team_list, label_visibility="collapsed")

    st.markdown("---")

    # ── 5. Chip Time Range ──
    st.markdown("##### ⏱️ Chip Time Range")
    time_range = st.slider(
        "Chip Time",
        min_value=min_time,
        max_value=max_time,
        value=(min_time, max_time),
        step=60,
        format="%d sec",
        label_visibility="collapsed",
    )
    st.caption(f"From **{seconds_to_label(time_range[0])}** to **{seconds_to_label(time_range[1])}**")

    # ── 6. Gun Time Range ──
    st.markdown("##### 🔫 Gun Time Range")
    gun_range = st.slider(
        "Gun Time",
        min_value=min_gun,
        max_value=max_gun,
        value=(min_gun, max_gun),
        step=60,
        format="%d sec",
        label_visibility="collapsed",
    )
    st.caption(f"From **{seconds_to_label(gun_range[0])}** to **{seconds_to_label(gun_range[1])}**")

    st.markdown("---")

    # ── 7. Name Search ──
    st.markdown("##### 🔍 Search by Name")
    name_search = st.text_input("Name", placeholder="e.g. JARED WILSON", label_visibility="collapsed")

    # ── Reset ──
    if st.button("🔄  Reset All Filters", use_container_width=True):
        st.rerun()


# ══════════════════════════════════════
#  APPLY FILTERS
# ══════════════════════════════════════
filtered = df.copy()

# Gender
if gender_sel != "All":
    filtered = filtered[filtered["Gender"] == gender_sel]

# City
if city_sel:
    filtered = filtered[filtered["City"].isin(city_sel)]

# State
if state_sel:
    filtered = filtered[filtered["State"].isin(state_sel)]

# Team
if team_mode == "Team Members Only":
    filtered = filtered[filtered["Team"] != ""]
elif team_mode == "Independent Only":
    filtered = filtered[filtered["Team"] == ""]
elif team_mode == "Specific Team" and specific_team:
    filtered = filtered[filtered["Team"] == specific_team]

# Chip Time
filtered = filtered[
    (filtered["_time_sec"] >= time_range[0]) & (filtered["_time_sec"] <= time_range[1])
    | filtered["_time_sec"].isna()
]

# Gun Time
filtered = filtered[
    (filtered["_gun_sec"] >= gun_range[0]) & (filtered["_gun_sec"] <= gun_range[1])
    | filtered["_gun_sec"].isna()
]

# Name
if name_search:
    filtered = filtered[filtered["Name"].str.contains(name_search.upper(), case=False, na=False)]


# ══════════════════════════════════════
#  MAIN CONTENT
# ══════════════════════════════════════
st.markdown('<h1 class="main-title">🏃 RACE RESULTS EXPLORER</h1>', unsafe_allow_html=True)
st.markdown(f'<p class="sub-title">Showing <strong>{len(filtered)}</strong> of <strong>{len(df)}</strong> runners</p>', unsafe_allow_html=True)

# ── Metric Cards ──
c1, c2, c3, c4, c5 = st.columns(5)

with c1:
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-val" style="color:#f97316">{len(filtered)}</div>
        <div class="metric-label">Total</div>
    </div>""", unsafe_allow_html=True)

with c2:
    m_count = len(filtered[filtered["Gender"] == "M"])
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-val" style="color:#3b82f6">{m_count}</div>
        <div class="metric-label">Male</div>
    </div>""", unsafe_allow_html=True)

with c3:
    f_count = len(filtered[filtered["Gender"] == "F"])
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-val" style="color:#ec4899">{f_count}</div>
        <div class="metric-label">Female</div>
    </div>""", unsafe_allow_html=True)

with c4:
    team_count = filtered[filtered["Team"] != ""]["Team"].nunique()
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-val" style="color:#10b981">{team_count}</div>
        <div class="metric-label">Teams</div>
    </div>""", unsafe_allow_html=True)

with c5:
    state_count = filtered["State"].nunique()
    st.markdown(f"""
    <div class="metric-card">
        <div class="metric-val" style="color:#a78bfa">{state_count}</div>
        <div class="metric-label">States</div>
    </div>""", unsafe_allow_html=True)

st.markdown("")

# ── Data Table ──
display_cols = ["Place", "Bib", "Name", "Gender", "City", "State", "Time", "Gun Time", "Team"]
display_df = filtered[display_cols].reset_index(drop=True)

st.dataframe(
    display_df,
    use_container_width=True,
    height=520,
    column_config={
        "Place": st.column_config.NumberColumn("🏆 Place", width="small"),
        "Bib": st.column_config.NumberColumn("🔢 Bib", width="small"),
        "Name": st.column_config.TextColumn("👤 Name", width="medium"),
        "Gender": st.column_config.TextColumn("⚧ Gender", width="small"),
        "City": st.column_config.TextColumn("🏙️ City", width="medium"),
        "State": st.column_config.TextColumn("📍 State", width="small"),
        "Time": st.column_config.TextColumn("⏱️ Chip Time", width="small"),
        "Gun Time": st.column_config.TextColumn("🔫 Gun Time", width="small"),
        "Team": st.column_config.TextColumn("🏅 Team", width="medium"),
    },
)

# ── Charts Row ──
st.markdown("")
st.markdown("### 📊 Distribution Insights")

tab1, tab2, tab3, tab4 = st.tabs(["Gender Split", "Top Cities", "Top Teams", "Time Distribution"])

with tab1:
    gender_counts = filtered["Gender"].value_counts().reset_index()
    gender_counts.columns = ["Gender", "Count"]
    gender_counts["Gender"] = gender_counts["Gender"].map({"M": "Male", "F": "Female"})
    st.bar_chart(gender_counts.set_index("Gender"), color="#f97316", horizontal=True)

with tab2:
    city_counts = filtered["City"].value_counts().head(10).reset_index()
    city_counts.columns = ["City", "Count"]
    st.bar_chart(city_counts.set_index("City"), color="#3b82f6")

with tab3:
    team_data = filtered[filtered["Team"] != ""]["Team"].value_counts().reset_index()
    team_data.columns = ["Team", "Count"]
    if not team_data.empty:
        st.bar_chart(team_data.set_index("Team"), color="#10b981")
    else:
        st.info("No team members in current filter.")

with tab4:
    time_data = filtered[filtered["_time_sec"].notna()].copy()
    if not time_data.empty:
        time_data["Minutes"] = time_data["_time_sec"] / 60
        st.bar_chart(
            time_data["Minutes"].value_counts(bins=20).sort_index().reset_index().rename(
                columns={"index": "Time Bin", "count": "Runners"}
            ).set_index("Time Bin"),
            color="#fbbf24",
        )
    else:
        st.info("No time data available for current filter.")

# ── Download ──
st.markdown("---")
col_dl1, col_dl2, _ = st.columns([1, 1, 3])
with col_dl1:
    csv_out = display_df.to_csv(index=False).encode("utf-8")
    st.download_button("📥 Download Filtered CSV", csv_out, "filtered_results.csv", "text/csv", use_container_width=True)
with col_dl2:
    st.download_button("📥 Download Full CSV", df[display_cols].to_csv(index=False).encode("utf-8"), "full_results.csv", "text/csv", use_container_width=True)

st.markdown("""
<div style="text-align:center; color:#475569; font-size:0.8rem; margin-top:24px;">
    Built with Streamlit • Race Results Explorer
</div>
""", unsafe_allow_html=True)