import os
import requests
import gradio as gr
import pandas as pd

BASE_URL = "https://sportsbook-nash.draftkings.com/sites/US-VA-SB/api/sportscontent/controldata/league/leagueSubcategory/v1/markets"

LEAGUE_ID = "42133"
SUBCATEGORY_ID = "18010"

# OT adjustment factor (1 goal vs ~1.5 goals)
ADJUSTMENT_FACTOR = 0.67

DEBUG = True


def log(msg):
    if DEBUG:
        print(msg)


# =========================
# Odds Conversion
# =========================
def american_to_prob(odds):
    odds = float(odds)
    if odds > 0:
        return 100 / (odds + 100)
    else:
        return abs(odds) / (abs(odds) + 100)


def prob_to_decimal(p):
    if p <= 0:
        return None
    return round(1 / p, 3)


def extract_decimal_adjusted(odds):
    try:
        if odds is None:
            return None

        if isinstance(odds, dict):
            odds = odds.get("american") or odds.get("value") or odds.get("price")

        odds = str(odds).strip().replace("−", "-")

        # already decimal
        if "." in odds and not odds.startswith("+") and not odds.startswith("-"):
            base_dec = float(odds)
            p = 1 / base_dec
        else:
            p = american_to_prob(float(odds))

        # apply OT adjustment
        p_adj = p * ADJUSTMENT_FACTOR

        return prob_to_decimal(p_adj)

    except:
        return None


# =========================
# Copy Format (TSV)
# =========================
def df_to_tsv(df):
    if df is None or df.empty:
        return ""
    return "\n".join(
        f"{row['Player']}\t{row['Decimal Odds']}"
        for _, row in df.iterrows()
    )


# =========================
# Fetch Data
# =========================
def fetch_data():
    headers = {
        "accept": "*/*",
        "content-type": "application/json; charset=utf-8",
        "origin": "https://sportsbook.draftkings.com",
        "referer": "https://sportsbook.draftkings.com/",
        "user-agent": "Mozilla/5.0",
    }

    params = {
        "isBatchable": "false",
        "templateVars": f"{LEAGUE_ID},{SUBCATEGORY_ID}",
        "eventsQuery": f"$filter=leagueId eq '{LEAGUE_ID}' AND clientMetadata/Subcategories/any(s: s/Id eq '{SUBCATEGORY_ID}')",
        "marketsQuery": f"$filter=clientMetadata/subCategoryId eq '{SUBCATEGORY_ID}'",
        "include": "Events",
        "entity": "events",
    }

    r = requests.get(BASE_URL, headers=headers, params=params, timeout=20)
    r.raise_for_status()
    data = r.json()

    log(f"Events: {len(data.get('events', []))}")
    log(f"Selections: {len(data.get('selections', []))}")

    return data


# =========================
# Extract Games
# =========================
def extract_games(data):
    games = []

    for e in data.get("events", []):
        event_id = e.get("id")
        name = e.get("name")

        if event_id and name:
            games.append((name, str(event_id)))

    return games


# =========================
# Match Markets
# =========================
def get_market_ids(data, event_id):
    market_ids = []

    for m in data.get("markets", []):
        mid = m.get("id")

        if str(m.get("eventId")) == str(event_id):
            market_ids.append(str(mid))

        if "eventIds" in m and str(event_id) in [str(x) for x in m["eventIds"]]:
            market_ids.append(str(mid))

    return set(market_ids)


# =========================
# Extract Players
# =========================
def extract_players(data, event_id):
    selections = data.get("selections", [])
    market_ids = get_market_ids(data, event_id)

    rows = []

    for s in selections:
        market_id = s.get("marketId")

        if str(market_id) not in market_ids:
            continue

        player = None

        if s.get("participants"):
            player = s["participants"][0].get("name")

        if not player:
            player = s.get("label") or s.get("outcomeName")

        odds = s.get("displayOdds") or s.get("oddsAmerican") or s.get("price")

        dec = extract_decimal_adjusted(odds)

        if player and dec is not None:
            rows.append((player, dec))

    df = pd.DataFrame(rows, columns=["Player", "Decimal Odds"])

    return df.drop_duplicates().sort_values("Player").reset_index(drop=True)


# =========================
# Initialize
# =========================
def initialize_app():
    data = fetch_data()
    games = extract_games(data)

    if not games:
        return {}, gr.Dropdown(choices=[], value=None)

    game_map = {name: eid for name, eid in games}

    return game_map, gr.Dropdown(
        choices=list(game_map.keys()),
        value=list(game_map.keys())[0]
    )


# =========================
# Run
# =========================
def run_selected_game(game_map, selected_game):
    if not selected_game:
        return pd.DataFrame(), ""

    data = fetch_data()
    event_id = game_map[selected_game]

    df = extract_players(data, event_id)

    return df, df_to_tsv(df)


# =========================
# UI
# =========================
with gr.Blocks(title="DK NHL OT Adjusted Points") as demo:
    gr.Markdown("# DK NHL OT Adjusted Points")

    game_map_state = gr.State({})

    with gr.Row():
        game_dropdown = gr.Dropdown(label="Select Game", scale=5)
        run_btn = gr.Button("Run", variant="primary")
        refresh_btn = gr.Button("Refresh")

    output_df = gr.Dataframe(
        headers=["Player", "Decimal Odds"],
        datatype=["str", "number"],
        interactive=False,
        label="Adjusted Player Table"
    )

    copy_box = gr.Textbox(
        label="Copy (paste into Excel/Sheets)",
        lines=12,
        interactive=False
    )

    demo.load(
        initialize_app,
        inputs=[],
        outputs=[game_map_state, game_dropdown]
    )

    run_btn.click(
        run_selected_game,
        inputs=[game_map_state, game_dropdown],
        outputs=[output_df, copy_box]
    )

    refresh_btn.click(
        initialize_app,
        inputs=[],
        outputs=[game_map_state, game_dropdown]
    )


# =========================
# Launch
# =========================
if __name__ == "__main__":
    demo.queue().launch(
        server_name="0.0.0.0",
        server_port=int(os.getenv("PORT", 7860))
    )