app.py

import gradio as gr
from datetime import datetime
from typing import Any, Dict, Iterable, List, Optional, Tuple
from collections import Counter
import json
import os
import html as html_lib
import base64
from pathlib import Path

from huggingface_hub import HfApi, InferenceClient
import requests


def _created_year(obj):
    if hasattr(obj, "created_at"):
        dt = getattr(obj, "created_at")
        return dt.year

def _year_from_iso(value: Any) -> Optional[int]:
    if not value or not isinstance(value, str):
        return None
    try:
        # e.g. 2025-12-12T18:40:13.000Z
        dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
        return dt.year
    except Exception:
        return None

_ASSET_CACHE: Dict[str, str] = {}

def _asset_data_uri(filename: str) -> str:
    """
    Returns a data URI (base64) for a local asset in this repo.
    Cached in-memory to avoid re-reading files every render.
    """
    if filename in _ASSET_CACHE:
        return _ASSET_CACHE[filename]
    path = Path(__file__).resolve().parent / filename
    try:
        raw = path.read_bytes()
        b64 = base64.b64encode(raw).decode("ascii")
        ext = path.suffix.lower()
        mime = "image/png"
        if ext == ".gif":
            mime = "image/gif"
        elif ext in (".jpg", ".jpeg"):
            mime = "image/jpeg"
        elif ext == ".webp":
            mime = "image/webp"
        uri = f"data:{mime};base64,{b64}"
        _ASSET_CACHE[filename] = uri
        return uri
    except Exception:
        # If missing, return empty string to avoid breaking HTML
        return ""

def _http_get_json(url: str, *, token: Optional[str] = None, params: Optional[Dict[str, Any]] = None) -> Any:
    headers: Dict[str, str] = {}
    if token:
        headers["Authorization"] = f"Bearer {token}"
    r = requests.get(url, headers=headers, params=params, timeout=25)
    r.raise_for_status()
    return r.json()

def fetch_likes_left_2025(username: str, token: Optional[str] = None) -> int:
    """
    Count likes the user left in 2025 via /api/users/{username}/likes.
    Endpoint returns a list with `createdAt` descending.
    """
    url = f"https://huggingface.co/api/users/{username}/likes"
    try:
        data = _http_get_json(url, token=token)
    except Exception:
        return 0
    if not isinstance(data, list):
        return 0
    total = 0
    for item in data:
        if not isinstance(item, dict):
            continue
        yr = _year_from_iso(item.get("createdAt"))
        if yr is None:
            continue
        if yr < 2025:
            break
        if yr == 2025:
            total += 1
    return total

def _repo_id(obj: Any) -> str:
    if isinstance(obj, dict):
        return obj.get("id") or obj.get("modelId") or obj.get("repoId") or "N/A"
    return (
        getattr(obj, "id", None)
        or getattr(obj, "modelId", None)
        or getattr(obj, "repoId", None)
        or getattr(obj, "repo_id", None)
        or "N/A"
    )

def _repo_likes(obj: Any) -> int:
    return int(getattr(obj, "likes", 0) or 0)

def _repo_tags(obj: Any) -> List[str]:
    tags = getattr(obj, "tags", None) or []
    return [t for t in tags if isinstance(t, str)]

def _repo_pipeline_tag(obj: Any) -> Optional[str]:
    val = getattr(obj, "pipeline_tag", None)
    return val 

def _repo_library_name(obj: Any) -> Optional[str]:
    val = getattr(obj, "library_name", None)
    if isinstance(val, str) and val.strip():
        return val.strip()
    val = getattr(obj, "libraryName", None)
    if isinstance(val, str) and val.strip():
        return val.strip()
    return None

def _collect_2025_sorted_desc(items: Iterable[Any]) -> List[Any]:
    """
    We rely on API-side sorting (createdAt desc) + early-stop once we hit < 2025.
    This avoids pulling a user's entire history.
    """
    out: List[Any] = []
    for item in items:
        yr = _created_year(item)
        if yr is None:
            continue
        if yr < 2025:
            break
        if yr == 2025:
            out.append(item)
    return out

def fetch_user_data_2025(username: str, token: Optional[str] = None) -> Dict[str, List[Any]]:
    """Fetch user's models/datasets/spaces created in 2025 (API-side sort + paginated early-stop)."""
    api = HfApi(token=token)
    data: Dict[str, List[Any]] = {"models": [], "datasets": [], "spaces": []}

    try:
        data["models"] = _collect_2025_sorted_desc(
            api.list_models(author=username, full=True, sort="createdAt", direction=-1)
        )
    except Exception:
        data["models"] = []

    try:
        data["datasets"] = _collect_2025_sorted_desc(
            api.list_datasets(author=username, full=True, sort="createdAt", direction=-1)
        )
    except Exception:
        data["datasets"] = []

    # list_spaces full=True isn't supported in some versions; fall back if needed
    try:
        data["spaces"] = _collect_2025_sorted_desc(
            api.list_spaces(author=username, full=True, sort="createdAt", direction=-1)
        )
    except Exception:
        try:
            data["spaces"] = _collect_2025_sorted_desc(
                api.list_spaces(author=username, sort="createdAt", direction=-1)
            )
        except Exception:
            data["spaces"] = []

    return data

def _normalize_task_tag(tag: str) -> Optional[str]:
    t = (tag or "").strip()
    if not t:
        return None
    for prefix in ("task_categories:", "task_ids:", "pipeline_tag:"):
        if t.startswith(prefix):
            t = t[len(prefix):].strip()
    t = t.strip().lower()
    return t or None

def _suggested_nickname_for_task(task: Optional[str]) -> Optional[str]:
    if not task:
        return None
    t = task.strip().lower()
    mapping = {
        "text-generation": "LLM Whisperer 🗣️",
        "image-text-to-text": "VLM Nerd 🤓",
        "text-to-speech": "Full‑time Yapper 🗣️",
        "automatic-speech-recognition": "Subtitle Goblin 🎧",
        "text-to-image": "Diffusion Gremlin 🎨",
        "image-classification": "Pixel Judge 👁️",
        "token-classification": "NERd Lord 🤓",
        "text-classification": "Opinion Machine 🧠",
        "translation": "Language Juggler 🗺️",
        "summarization": "TL;DR Dealer ✍️",
        "image-to-text": "Caption Connoisseur 🖼️",
        "zero-shot-classification": "Label Wizard 🪄",
    }
    return mapping.get(t)

def infer_task_and_modality(models: List[Any], datasets: List[Any], spaces: List[Any]) -> Tuple[Optional[str], Counter]:
    """
    Returns: (most_common_task, task_counter)
    - Task is primarily inferred from model `pipeline_tag`, then from task-ish tags on all artifacts.
    """
    model_tasks: List[str] = []
    for m in models:
        pt = _repo_pipeline_tag(m)
        if pt:
            model_tasks.append(pt.strip().lower())

    tag_tasks: List[str] = []
    for obj in (models + datasets + spaces):
        for tag in _repo_tags(obj):
            nt = _normalize_task_tag(tag)
            if nt:
                tag_tasks.append(nt)

    counts = Counter(model_tasks if model_tasks else tag_tasks)
    top_task = counts.most_common(1)[0][0] if counts else None

    return top_task, counts

def infer_most_common_library(models: List[Any]) -> Optional[str]:
    libs: List[str] = []
    for m in models:
        ln = _repo_library_name(m)
        if ln:
            libs.append(ln)
    if not libs:
        return None
    return Counter(libs).most_common(1)[0][0]

def _k2_model_candidates() -> List[str]:
    """
    Kimi K2 repo IDs can vary; allow override via env and try a small list.
    """
    env_model = (os.getenv("KIMI_K2_MODEL") or "moonshotai/Kimi-K2-Instruct").strip()
    candidates = [env_model]
    # de-dupe while preserving order
    seen = set()
    out = []
    for c in candidates:
        if c and c not in seen:
            out.append(c)
            seen.add(c)
    return out

def _esc(value: Any) -> str:
    if value is None:
        return ""
    return html_lib.escape(str(value), quote=True)

def _profile_username(profile: Any) -> Optional[str]:
    if profile is None:
        return None
    for key in ("username", "preferred_username", "name", "user", "handle"):
        val = getattr(profile, key, None)
        if isinstance(val, str) and val.strip():
            return val.strip().lstrip("@")
    data = getattr(profile, "data", None)
    if isinstance(data, dict):
        for key in ("username", "preferred_username", "name"):
            val = data.get(key)
            if isinstance(val, str) and val.strip():
                return val.strip().lstrip("@")
        for container in ("profile", "user"):
            blob = data.get(container)
            if isinstance(blob, dict):
                val = blob.get("username") or blob.get("preferred_username") or blob.get("name")
                if isinstance(val, str) and val.strip():
                    return val.strip().lstrip("@")
    if isinstance(profile, dict):
        val = profile.get("username") or profile.get("preferred_username") or profile.get("name")
        if isinstance(val, str) and val.strip():
            return val.strip().lstrip("@")
    return None

def _profile_token(profile: Any) -> Optional[str]:
    """
    Gradio's OAuth payload varies by version.
    We try common attribute names and `.data` shapes.
    """
    if profile is None:
        return None
    for key in ("token", "access_token", "hf_token", "oauth_token", "oauth_access_token"):
        val = getattr(profile, key, None)
        if isinstance(val, str) and val.strip():
            return val.strip()
    data = getattr(profile, "data", None)
    if isinstance(data, dict):
        for key in ("token", "access_token", "hf_token", "oauth_token", "oauth_access_token"):
            val = data.get(key)
            if isinstance(val, str) and val.strip():
                return val.strip()
        # Common nested objects
        oauth_info = data.get("oauth_info") or data.get("oauth") or data.get("oauthInfo") or {}
        if isinstance(oauth_info, dict):
            val = oauth_info.get("access_token") or oauth_info.get("token")
            if isinstance(val, str) and val.strip():
                return val.strip()
    if isinstance(profile, dict):
        val = profile.get("token") or profile.get("access_token")
        if isinstance(val, str) and val.strip():
            return val.strip()
    return None

def generate_roast_and_nickname_with_k2(
    *,
    username: str,
    total_artifacts_2025: int,
    models_2025: int,
    datasets_2025: int,
    spaces_2025: int,
    top_task: Optional[str],
) -> Tuple[Optional[str], Optional[str]]:
    """
    Calls Kimi K2 via Hugging Face Inference Providers (via huggingface_hub InferenceClient).
    Returns (nickname, roast). If call fails, returns (None, None).
    """
    token = (os.getenv("HF_TOKEN") or "").strip()
    if not token:
        return None, None

    vibe = top_task or "mysterious vibes"
    above_below = "above" if total_artifacts_2025 > 20 else "below"
    suggested = _suggested_nickname_for_task(top_task)

    system = (
        "You are a witty, playful roast-comedian. Keep it fun, not cruel. "
        "No slurs, no hate, no harassment. Avoid profanity. Keep it short."
    )
    user = f"""
Create TWO things about this Hugging Face user, based on their 2025 activity stats.

User: @{username}
Artifacts created in 2025: {total_artifacts_2025} (models={models_2025}, datasets={datasets_2025}, spaces={spaces_2025}) which is {above_below} 20.
Top task (pipeline_tag): {top_task or "unknown"}

Nickname guidance (examples you SHOULD follow when applicable):
- text-generation -> LLM Whisperer 🗣️
- image-text-to-text -> VLM Nerd 🤓
- text-to-speech -> Full‑time Yapper 🗣️

If top task is known and you have a strong matching idea, pick a nickname like the examples. {f'If unsure, you may use this suggested nickname: {suggested}' if suggested else ''}
Roast should reference the task and whether they are above/below 20 artifacts.
Most common vibe: {vibe}

Return ONLY valid JSON with exactly these keys:
{{
  "nickname": "...",   // short, funny, can include 1 emoji
  "roast": "..."       // 1-2 sentences max, playful, no bullying
}}
""".strip()

    client = InferenceClient(model="moonshotai/Kimi-K2-Instruct", token=token)
    resp = client.chat.completions.create(
        model="moonshotai/Kimi-K2-Instruct",
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": user},
        ],
        max_tokens=180,
        temperature=0.8,
    )
    content = (resp.choices[0].message.content or "").strip()

    payload = json.loads(content)
    nickname = payload.get("nickname")
    roast = payload.get("roast")
    nickname_out = nickname.strip() if isinstance(nickname, str) else None
    roast_out = roast.strip() if isinstance(roast, str) else None
    return nickname_out, roast_out

def generate_wrapped_report(profile: gr.OAuthProfile) -> str:
    """Generate the HF Wrapped 2025 report"""
    username = _profile_username(profile) or "unknown"
    token = _profile_token(profile)

    # Fetch 2025 data (API-side sort + early stop)
    user_data_2025 = fetch_user_data_2025(username, token)
    models_2025 = user_data_2025["models"]
    datasets_2025 = user_data_2025["datasets"]
    spaces_2025 = user_data_2025["spaces"]

    most_liked_model = max(models_2025, key=_repo_likes) if models_2025 else None
    most_liked_dataset = max(datasets_2025, key=_repo_likes) if datasets_2025 else None
    most_liked_space = max(spaces_2025, key=_repo_likes) if spaces_2025 else None

    total_likes = sum(_repo_likes(x) for x in (models_2025 + datasets_2025 + spaces_2025))

    top_task, _task_counts = infer_task_and_modality(models_2025, datasets_2025, spaces_2025)
    top_library = infer_most_common_library(models_2025)

    total_artifacts_2025 = len(models_2025) + len(datasets_2025) + len(spaces_2025)
    nickname, roast = generate_roast_and_nickname_with_k2(
        username=username,
        total_artifacts_2025=total_artifacts_2025,
        models_2025=len(models_2025),
        datasets_2025=len(datasets_2025),
        spaces_2025=len(spaces_2025),
        top_task=top_task,
    )

    # New 2025 engagement stats
    likes_left_2025 = fetch_likes_left_2025(username, token)

    # Inline icons (local assets)
    like_icon = _asset_data_uri("like_logo.png")
    likes_received_icon = _asset_data_uri("likes_received.png")
    model_icon = _asset_data_uri("model_logo.png")
    dataset_icon = _asset_data_uri("dataset_logo.png")
    spaces_icon = _asset_data_uri("spaces_logo.png")
    vibe_icon = _asset_data_uri("vibe_logo.gif")

    # Create HTML report
    html = f"""
    <!DOCTYPE html>
    <html>
    <head>
        <style>
            @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700;800&display=swap');

            :root {{
                /* Keep the report readable even if the HF host page is in dark mode */
                color-scheme: light;
            }}

            body {{
                font-family: 'Poppins', sans-serif;
                background: linear-gradient(135deg, #FFF4D6 0%, #FFE6B8 50%, #FFF9E6 100%);
                margin: 0;
                padding: 20px;
                min-height: 100vh;
                color: #374151;
            }}

            .container {{
                max-width: 800px;
                margin: 0 auto;
                background: rgba(255, 255, 255, 0.95);
                border-radius: 26px;
                padding: 40px;
                box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
                animation: fadeIn 0.8s ease-in;
            }}

            @keyframes fadeIn {{
                from {{ opacity: 0; transform: translateY(20px); }}
                to {{ opacity: 1; transform: translateY(0); }}
            }}

            .header {{
                text-align: center;
                margin-bottom: 40px;
            }}

            .header h1 {{
                font-size: 3em;
                background: linear-gradient(135deg, #FF9D00 0%, #FFD21E 100%);
                -webkit-background-clip: text;
                -webkit-text-fill-color: transparent;
                margin: 0;
                font-weight: 800;
                animation: slideDown 0.6s ease-out;
            }}

            @keyframes slideDown {{
                from {{ transform: translateY(-30px); opacity: 0; }}
                to {{ transform: translateY(0); opacity: 1; }}
            }}

            .username {{
                font-size: 1.5em;
                color: #8a4b00 !important;
                margin-top: 10px;
                font-weight: 600;
            }}

            .nickname {{
                font-size: 1.1em;
                color: #111 !important;
                margin-top: 8px;
                font-weight: 700;
                background: #ffffff !important;
                display: inline-block;
                padding: 6px 12px;
                border-radius: 999px;
                border: 1px solid rgba(245, 87, 108, 0.25);
                box-shadow: 0 8px 18px rgba(0, 0, 0, 0.08);
            }}

            /* Removed the animated year badge ("beating 2025") */

            .stats-grid {{
                display: grid;
                grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
                gap: 20px;
                margin: 30px 0;
            }}

            .stat-card {{
                /* Solid pastel yellow (no gradient) */
                background: rgba(255, 210, 30, 0.55);
                color: #1f2937;
                padding: 25px;
                border-radius: 18px;
                text-align: center;
                box-shadow: 0 10px 25px rgba(255, 210, 30, 0.22);
                border: 1px solid rgba(17, 17, 17, 0.06);
                transition: transform 0.3s ease, box-shadow 0.3s ease;
                animation: popIn 0.5s ease-out backwards;
                display: flex;
                flex-direction: column;
                align-items: center;
                justify-content: center;
                gap: 10px;
                min-height: 170px;
            }}

            /* Force fixed readable colors (HF dark mode can override otherwise) */
            .stat-card, .stat-card * {{
                color: #111 !important;
            }}
            .stat-number {{
                color: #111 !important;
            }}
            .stat-label {{
                color: #111 !important;
            }}

            .stat-card:nth-child(1) {{ animation-delay: 0.1s; }}
            .stat-card:nth-child(2) {{ animation-delay: 0.2s; }}
            .stat-card:nth-child(3) {{ animation-delay: 0.3s; }}

            @keyframes popIn {{
                from {{ transform: scale(0.8); opacity: 0; }}
                to {{ transform: scale(1); opacity: 1; }}
            }}

            .stat-card:hover {{
                transform: translateY(-5px) scale(1.05);
                box-shadow: 0 15px 35px rgba(255, 210, 30, 0.30);
            }}

            .stat-number {{
                font-size: clamp(2.3rem, 3.6vw, 3.2rem);
                font-weight: 800;
                margin: 0;
                line-height: 1.05;
            }}

            .stat-label {{
                font-size: clamp(0.95rem, 1.2vw, 1.05rem);
                font-weight: 600;
                /* Avoid locale-sensitive uppercasing (e.g. "Likes" -> "LİKES" in Turkish locale) */
                text-transform: none;
                letter-spacing: 0.06em;
                display: inline-flex;
                align-items: center;
                justify-content: center;
                gap: 10px;
                color: #374151;
            }}

            .stat-icon {{
                width: clamp(54px, 6vw, 76px);
                height: clamp(54px, 6vw, 76px);
                object-fit: contain;
                filter: drop-shadow(0 2px 6px rgba(0,0,0,0.15));
            }}

            .stat-top {{
                display: flex;
                flex-direction: column;
                align-items: center;
                justify-content: center;
                gap: 10px;
            }}

            .likes-grid .stat-card {{
                min-height: 190px;
            }}

            .likes-grid {{
                display: grid;
                grid-template-columns: repeat(2, minmax(240px, 1fr));
                gap: 20px;
                margin: 30px 0;
            }}

            .section-icon {{
                width: 34px;
                height: 34px;
                object-fit: contain;
                vertical-align: middle;
                margin-right: 8px;
                filter: drop-shadow(0 2px 6px rgba(0,0,0,0.12));
            }}

            @media (max-width: 640px) {{
                .likes-grid {{
                    grid-template-columns: 1fr;
                }}
            }}

            .section {{
                margin: 40px 0;
                padding: 25px;
                background: #ffffff !important;
                border-radius: 18px;
                animation: slideIn 0.6s ease-out;
                color: #111 !important;
                border: 1px solid rgba(17, 17, 17, 0.08);
                box-shadow: 0 12px 30px rgba(0, 0, 0, 0.10);
                border-top: 6px solid rgba(255, 157, 0, 0.75);
            }}

            @keyframes slideIn {{
                from {{ transform: translateX(-30px); opacity: 0; }}
                to {{ transform: translateX(0); opacity: 1; }}
            }}

            .section h2 {{
                color: #8a4b00 !important;
                font-size: 1.8em;
                margin-top: 0;
                font-weight: 700;
                display: flex;
                align-items: center;
                gap: 10px;
            }}

            .trophy {{
                font-size: 1.5em;
            }}

            .item {{
                background: #ffffff !important;
                padding: 20px;
                margin: 15px 0;
                border-radius: 13px;
                box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
                transition: transform 0.2s ease;
                border: 1px solid rgba(17, 17, 17, 0.08);
            }}

            .item:hover {{
                transform: translateX(10px);
            }}

            .item-name {{
                font-weight: 600;
                font-size: 1.2em;
                color: #111 !important;
                margin-bottom: 5px;
            }}

            .item-likes {{
                color: #d92d20 !important;
                font-weight: 600;
                font-size: 1.1em;
            }}

            .item-sub {{
                color: #1f2937 !important;
                font-weight: 600;
                font-size: 1.05em;
            }}

            .emoji {{
                font-size: 1.5em;
                margin-right: 10px;
            }}

            .footer {{
                text-align: center;
                margin-top: 40px;
                color: #111 !important;
                font-weight: 600;
                background: #ffffff !important;
                border: 1px solid rgba(17, 17, 17, 0.08);
                border-radius: 14px;
                padding: 16px 18px;
                box-shadow: 0 10px 24px rgba(0, 0, 0, 0.08);
            }}

            .footer p {{
                margin: 8px 0;
                color: #111 !important;
                opacity: 1 !important;
                font-size: 1.05em;
                line-height: 1.35;
            }}

            .no-data {{
                text-align: center;
                color: #111 !important;
                font-style: italic;
                padding: 20px;
            }}

            .roast {{
                font-size: 1.15em;
                line-height: 1.5;
                color: #111 !important;
                background: #fff0f3 !important;
                border-left: 6px solid #f5576c;
                padding: 18px 18px;
                border-radius: 12px;
                margin-top: 10px;
                border: 1px solid rgba(245, 87, 108, 0.25);
                font-family: inherit !important;
            }}

            /* Ensure roast never switches to monospace because of inline code blocks */
            .roast, .roast * {{
                font-family: 'Poppins', sans-serif !important;
            }}

            .roast code, .roast pre {{
                font-family: 'Poppins', sans-serif !important;
            }}
        </style>
    </head>
    <body>
        <div class="container">
            <div class="header">
                <h1>Your 2025 Hugging Face Wrapped</h1>
                <div class="username">@{username}</div>
            </div>

            <div class="section">
                <h2><img class="section-icon" src="{vibe_icon}" alt="Vibe" /> Your Signature Vibe</h2>
                <div class="item">
                    {f'<div class="item-name">You are a {_esc(nickname)}</div>' if nickname else ''}
                    {f'<div class="item-name">You nailed this task: {_esc(top_task)}</div>' if top_task else ''}
                    <div class="item-name">You shipped {total_artifacts_2025} artifacts this year!</div>
                    {f'<div class="item-name">You loved {_esc(top_library)} library the most 💛</div>' if top_library else ''}
                </div>
                {f'<div class="roast" style="margin-top: 14px;">{_esc(roast)}</div>' if roast else '<div class="no-data" style="margin-top: 14px;">Couldn’t generate a roast (missing token or Kimi K2 not reachable).</div>'}
            </div>

            <div class="stats-grid">
                <div class="stat-card">
                    <div class="stat-top">
                        <img class="stat-icon" src="{model_icon}" alt="Models" />
                        <div class="stat-number">{len(models_2025)}</div>
                    </div>
                    <div class="stat-label">Models</div>
                </div>
                <div class="stat-card">
                    <div class="stat-top">
                        <img class="stat-icon" src="{dataset_icon}" alt="Datasets" />
                        <div class="stat-number">{len(datasets_2025)}</div>
                    </div>
                    <div class="stat-label">Datasets</div>
                </div>
                <div class="stat-card">
                    <div class="stat-top">
                        <img class="stat-icon" src="{spaces_icon}" alt="Spaces" />
                        <div class="stat-number">{len(spaces_2025)}</div>
                    </div>
                    <div class="stat-label">Spaces</div>
                </div>
            </div>

            <div class="likes-grid">
                <div class="stat-card">
                    <div class="stat-top">
                        <img class="stat-icon" src="{like_icon}" alt="Likes given" />
                        <div class="stat-number">{likes_left_2025}</div>
                    </div>
                    <div class="stat-label">Likes Given</div>
                </div>
                <div class="stat-card">
                    <div class="stat-top">
                        <img class="stat-icon" src="{likes_received_icon}" alt="Likes received" />
                        <div class="stat-number">{total_likes}</div>
                    </div>
                    <div class="stat-label">Likes Received</div>
                </div>
            </div>

            <div class="section">
                <h2>Most Liked Model</h2>
                {f'''
                <div class="item">
                    <div class="item-name"><span class="emoji">🤖</span>{_repo_id(most_liked_model)}</div>
                    <div class="item-likes">❤️ {_repo_likes(most_liked_model)} likes</div>
                </div>
                ''' if most_liked_model else '<div class="no-data">No models yet</div>'}
            </div>

            <div class="section">
                <h2>Most Liked Dataset</h2>
                {f'''
                <div class="item">
                    <div class="item-name"><span class="emoji">📊</span>{_repo_id(most_liked_dataset)}</div>
                    <div class="item-likes">❤️ {_repo_likes(most_liked_dataset)} likes</div>
                </div>
                ''' if most_liked_dataset else '<div class="no-data">No datasets yet</div>'}
            </div>

            <div class="section">
                <h2>Most Liked Space</h2>
                {f'''
                <div class="item">
                    <div class="item-name"><span class="emoji">🚀</span>{_repo_id(most_liked_space)}</div>
                    <div class="item-likes">❤️ {_repo_likes(most_liked_space)} likes</div>
                </div>
                ''' if most_liked_space else '<div class="no-data">No spaces yet</div>'}
            </div>

            <div class="footer">
                <p>🎊 Thank you for being part of the Hugging Face community! 🎊</p>
                <p>Keep building amazing things in 2026!</p>
                <p>Built with Inference Providers with 💛</p>
            </div>
        </div>
    </body>
    </html>
    """

    return html

def show_login_message():
    """Show message for non-logged-in users"""
    return """
    <div style="text-align: center; padding: 50px; font-family: 'Poppins', sans-serif;">
        <h1 style="color: #8a4b00; font-size: 3em;">🎉 Welcome to HF Wrapped! 🎉</h1>
        <p style="font-size: 1.5em; color: #374151;">
            Please log in with your Hugging Face account to see your personalized report!
        </p>
        <p style="font-size: 1.2em; color: #4b5563;">
            Click the "Sign in with Hugging Face" button above 👆
        </p>
    </div>
    """

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft(), css="""
    .gradio-container {
        background: linear-gradient(135deg, #FFF4D6 0%, #FFE6B8 50%, #FFF9E6 100%);
    }

    /* Force readable hero text even when HF host page is in dark mode */
    .hf-hero, .hf-hero * {
        color: #111 !important;
    }
""") as demo:
    gr.HTML("""
        <div class="hf-hero" style="text-align: center; padding: 20px;">
            <h1 style="font-size: 3em; margin: 0;">🎉 HF Wrapped 2025 🎉</h1>
            <p style="font-size: 1.2em; margin: 8px 0 0 0;">Discover your Hugging Face journey this year!</p>
        </div>
    """)

    with gr.Row():
        with gr.Column():
            login_button = gr.LoginButton()
            output = gr.HTML(value=show_login_message())

    def _render(profile_obj: Optional[gr.OAuthProfile] = None):
        # In Gradio versions that support OAuth, `profile_obj` is injected after login.
        return generate_wrapped_report(profile_obj) if profile_obj is not None else show_login_message()

    # On load show the login message (and in some Gradio versions, this also receives the injected profile)
    demo.load(fn=_render, inputs=None, outputs=output)

    # After login completes, clicking the login button will trigger a rerender.
    # Older Gradio treats LoginButton as a button (click event), not a value component (change event).
    if hasattr(login_button, "click"):
        login_button.click(fn=_render, inputs=None, outputs=output)

if __name__ == "__main__":
    demo.launch()