app.py

import os
import time
from collections import defaultdict
from functools import lru_cache

import argilla as rg
import gradio as gr
import pandas as pd
from dotenv import load_dotenv
from fastapi import FastAPI

load_dotenv()

try:
    client = rg.Argilla(
        api_url=os.getenv("ARGILLA_API_URL", ""),
        api_key=os.getenv("ARGILLA_API_KEY", ""),
    )
except Exception as e:
    print(f"Error initializing Argilla client: {e}")
    client = None


DATA_DIR = "data"
INCLUDE_CSV = os.path.join(DATA_DIR, "include.csv")
STEREOTYPES_CSV = os.path.join(DATA_DIR, "stereotypes.csv")
ARENA_JSON = os.path.join(DATA_DIR, "arena.json")
PARTICIPANTS_CSV = os.path.join(DATA_DIR, "participants.csv")
LEADERBOARD_CSV = os.path.join(".", "leaderboard.csv")

countries = {
    "Argentina": {"iso": "ARG", "emoji": "🇦🇷"},
    "Bolivia": {"iso": "BOL", "emoji": "🇧🇴"},
    "Chile": {"iso": "CHL", "emoji": "🇨🇱"},
    "Colombia": {"iso": "COL", "emoji": "🇨🇴"},
    "Costa Rica": {"iso": "CRI", "emoji": "🇨🇷"},
    "Cuba": {"iso": "CUB", "emoji": "🇨🇺"},
    "Ecuador": {"iso": "ECU", "emoji": "🇪🇨"},
    "El Salvador": {"iso": "SLV", "emoji": "🇸🇻"},
    "España": {"iso": "ESP", "emoji": "🇪🇸"},
    "Guatemala": {"iso": "GTM", "emoji": "🇬🇹"},
    "Honduras": {"iso": "HND", "emoji": "🇭🇳"},
    "México": {"iso": "MEX", "emoji": "🇲🇽"},
    "Nicaragua": {"iso": "NIC", "emoji": "🇳🇮"},
    "Panamá": {"iso": "PAN", "emoji": "🇵🇦"},
    "Paraguay": {"iso": "PRY", "emoji": "🇵🇾"},
    "Perú": {"iso": "PER", "emoji": "🇵🇪"},
    "Puerto Rico": {"iso": "PRI", "emoji": "🇵🇷"},
    "República Dominicana": {"iso": "DOM", "emoji": "🇩🇴"},
    "Uruguay": {"iso": "URY", "emoji": "🇺🇾"},
    "Venezuela": {"iso": "VEN", "emoji": "🇻🇪"},
}


def get_blend_es_data():
    data = []

    for country in countries.keys():
        iso = countries[country]["iso"]
        emoji = countries[country]["emoji"]

        dataset_name = f"{emoji} {country} - {iso} - Responder"

        try:
            print(f"Processing dataset: {dataset_name}")
            dataset = client.datasets(dataset_name)
            records = list(dataset.records(with_responses=True))

            dataset_contributions = defaultdict(int)
            user_mapping = {}

            for record in records:
                record_dict = record.to_dict()
                if "answer_1" in record_dict["responses"]:
                    for answer in record_dict["responses"]["answer_1"]:
                        if answer["user_id"]:
                            user_id = answer["user_id"]
                            dataset_contributions[user_id] += 1

                            if user_id not in user_mapping:
                                try:
                                    user = client.users(id=user_id)
                                    user_mapping[user_id] = user.username
                                except Exception as e:
                                    print(f"Error getting username for {user_id}: {e}")
                                    user_mapping[user_id] = f"User-{user_id[:8]}"

            for user_id, count in dataset_contributions.items():
                username = user_mapping.get(user_id, f"User-{user_id[:8]}")
                data.append(
                    {"source": "blend-es", "username": username, "count": count}
                )

        except Exception as e:
            print(f"Error processing dataset {dataset_name}: {e}")

    return data


def get_include_data():
    data = []
    try:
        if os.path.exists(INCLUDE_CSV):
            include_df = pd.read_csv(INCLUDE_CSV)
            username_column = "Nombre en Discord / username"
            questions_column = "Total preguntas hackathon"
            if (
                username_column in include_df.columns
                and questions_column in include_df.columns
            ):
                discord_users = defaultdict(int)
                for _, row in include_df.iterrows():
                    username = row[username_column][1:]  # Remove the @ symbol
                    questions = row[questions_column]
                    if pd.notna(username) and pd.notna(questions):
                        discord_users[username.lower()] += int(questions)

                for username, count in discord_users.items():
                    data.append(
                        {"source": "include", "username": username, "count": count}
                    )

    except Exception as e:
        print(f"Error loading {INCLUDE_CSV}: {e}")

    return data


def get_mail_to_username_mapping():
    mail_to_discord = {}
    try:
        if os.path.exists(PARTICIPANTS_CSV):
            mapping_df = pd.read_csv(PARTICIPANTS_CSV)
            if "gmail" in mapping_df.columns and "discord" in mapping_df.columns:
                for _, row in mapping_df.iterrows():
                    mail = row["gmail"]
                    discord = row["discord"]
                    if pd.notna(mail) and pd.notna(discord):
                        mail_to_discord[mail.lower()] = discord.lower()
    except Exception as e:
        print(f"Error loading {PARTICIPANTS_CSV}: {e}")

    return mail_to_discord


def get_estereotipos_data():
    data = []
    mail_to_discord = get_mail_to_username_mapping()

    try:
        if os.path.exists(STEREOTYPES_CSV):
            counts_df = pd.read_csv(STEREOTYPES_CSV)
            if "token_id" in counts_df.columns and "count" in counts_df.columns:
                mail_counts = defaultdict(int)
                for _, row in counts_df.iterrows():
                    mail = row["token_id"]
                    count = row["count"]
                    if pd.notna(mail) and pd.notna(count):
                        mail_counts[mail.lower()] += int(count)

                for mail, count in mail_counts.items():
                    username = mail_to_discord.get(mail.lower(), "")
                    if not username:
                        username = mail.split("@")[0] if "@" in mail else mail

                    data.append(
                        {"source": "estereotipos", "username": username, "count": count}
                    )
    except Exception as e:
        print(f"Error loading {STEREOTYPES_CSV}: {e}")

    return data


def get_arena_data():
    data = []
    mail_to_discord = get_mail_to_username_mapping()

    try:
        if os.path.exists(ARENA_JSON):
            import json

            with open(ARENA_JSON, "r", encoding="utf-8") as f:
                arena_data = json.load(f)

            mail_counts = defaultdict(int)

            for country, conversations in arena_data.items():
                for conversation in conversations:
                    if "username" in conversation:
                        mail = conversation["username"]
                        if mail:
                            mail_counts[mail.lower()] += 1

            for mail, count in mail_counts.items():
                username = mail_to_discord.get(mail.lower(), "")
                if not username:
                    username = mail.split("@")[0] if "@" in mail else mail

                data.append({"source": "arena", "username": username, "count": count})
    except Exception as e:
        print(f"Error loading {ARENA_JSON}: {e}")

    return data


@lru_cache(maxsize=32)
def get_user_contributions_cached(cache_buster: int):
    return consolidate_all_data()


def consolidate_all_data():
    all_data = []
    all_data.extend(get_blend_es_data())
    all_data.extend(get_include_data())
    all_data.extend(get_estereotipos_data())
    all_data.extend(get_arena_data())

    user_contributions = defaultdict(
        lambda: {
            "username": "",
            "blend_es": 0,
            "include": 0,
            "estereotipos": 0,
            "arena": 0,
        }
    )

    for item in all_data:
        source = item["source"]
        username = item["username"]
        count = item["count"]

        user_key = username.lower()

        if not user_contributions[user_key]["username"]:
            user_contributions[user_key]["username"] = username

        if source == "blend-es":
            user_contributions[user_key]["blend_es"] += count
        elif source == "include":
            user_contributions[user_key]["include"] += count
        elif source == "estereotipos":
            user_contributions[user_key]["estereotipos"] += count
        elif source == "arena":
            user_contributions[user_key]["arena"] += count

    rows = []
    for _, data in user_contributions.items():
        total = (
            data["blend_es"] + data["include"] + data["estereotipos"] + data["arena"]
        )
        row = {
            "Username": data["username"],
            "Total": total,
            "Blend-es": data["blend_es"],
            "INCLUDE": data["include"],
            "Estereotipos": data["estereotipos"],
            "Arena": data["arena"],
        }
        rows.append(row)

    df = pd.DataFrame(rows)

    if not df.empty:
        df = df.sort_values("Total", ascending=False)

    with open(LEADERBOARD_CSV, "w", encoding="utf-8") as f:
        df.to_csv(f, index=False)

    return df


app = FastAPI()

last_update_time = 0
cached_data = None


def create_leaderboard_ui():
    global cached_data, last_update_time
    current_time = time.time()

    if cached_data is not None and current_time - last_update_time < 300:
        df = cached_data
    else:
        cache_buster = int(current_time)
        df = get_user_contributions_cached(cache_buster)
        cached_data = df
        last_update_time = current_time

    if not df.empty:
        df = df.reset_index(drop=True)
        df.index = df.index + 1
        df = df.rename_axis("Rank")
        df = df.reset_index()

    df_html = df.to_html(classes="leaderboard-table", border=0, index=False)

    styled_html = f"""
    <div style="margin: 20px 0;">
        <p>Última Actualización: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(last_update_time))}</p>
        <style>
            .leaderboard-table {{
                width: 100%;
                border-collapse: collapse;
                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
                box-shadow: 0 4px 8px rgba(0,0,0,0.1);
                border-radius: 8px;
                overflow: hidden;
            }}
            .leaderboard-table th {{
                background-color: #1a1a2e;
                color: white;
                font-weight: bold;
                text-align: left;
                padding: 14px;
                border-bottom: 2px solid #16213e;
            }}
            .leaderboard-table td {{
                padding: 12px 14px;
                border-bottom: 1px solid #333;
                background-color: #222;
                color: #fff;
            }}
            .leaderboard-table tr:hover td {{
                background-color: #2a2a3a;
            }}
            .leaderboard-table tr:nth-child(1) td:first-child {{
                background-color: #ffd700;
                color: #333;
                font-weight: bold;
                text-align: center;
                border-right: 1px solid #333;
            }}
            .leaderboard-table tr:nth-child(2) td:first-child {{
                background-color: #c0c0c0;
                color: #333;
                font-weight: bold;
                text-align: center;
                border-right: 1px solid #333;
            }}
            .leaderboard-table tr:nth-child(3) td:first-child {{
                background-color: #cd7f32;
                color: #333;
                font-weight: bold;
                text-align: center;
                border-right: 1px solid #333;
            }}
            .leaderboard-table tr:nth-child(1) td:nth-child(2) {{
                font-weight: bold;
                color: #ffd700;
            }}
            .leaderboard-table tr:nth-child(2) td:nth-child(2) {{
                font-weight: bold;
                color: #c0c0c0;
            }}
            .leaderboard-table tr:nth-child(3) td:nth-child(2) {{
                font-weight: bold;
                color: #cd7f32;
            }}
        </style>
        {df_html}
    </div>
    """
    return styled_html


def refresh_data():
    global cached_data, last_update_time
    cached_data = None
    last_update_time = 0
    return create_leaderboard_ui()


with gr.Blocks(theme=gr.themes.Default()) as demo:
    with gr.Column(scale=1):
        gr.Markdown("""# 🏆 Hackaton Leaderboard""")

        leaderboard_html = gr.HTML(create_leaderboard_ui)

        refresh_btn = gr.Button("🔄 Actualizar Datos", variant="primary")
        refresh_btn.click(fn=refresh_data, outputs=leaderboard_html)

gr.mount_gradio_app(app, demo, path="/")

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=7860)