import streamlit as st
from huggingface_hub import HfApi
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed
from functools import lru_cache
import time
import requests
from collections import Counter
import numpy as np

st.set_page_config(page_title="HF Contributions", layout="wide", initial_sidebar_state="expanded")

# 향상된 UI 스타일링
st.markdown("""
<style>
    /* 사이드바 스타일링 */
    [data-testid="stSidebar"] {
        min-width: 35vw !important;
        max-width: 35vw !important;
        background-color: #f8f9fa;
        padding: 1rem;
        border-right: 1px solid #e9ecef;
    }
    
    /* 헤더 스타일링 */
    h1, h2, h3 {
        color: #1e88e5;
        font-weight: 700;
    }
    h1 {
        font-size: 2.5rem;
        margin-bottom: 1.5rem;
        border-bottom: 2px solid #e0e0e0;
        padding-bottom: 0.5rem;
    }
    h2 {
        font-size: 1.8rem;
        margin-top: 1.5rem;
    }
    h3 {
        font-size: 1.4rem;
        margin-top: 1rem;
    }
    
    /* 카드 스타일링 */
    div[data-testid="stMetric"] {
        background-color: #f1f8fe;
        border-radius: 10px;
        padding: 1rem;
        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
        margin-bottom: 1rem;
    }
    
    /* 차트 컨테이너 스타일링 */
    .chart-container {
        background-color: white;
        border-radius: 10px;
        padding: 1rem;
        box-shadow: 0 2px 10px rgba(0,0,0,0.1);
        margin: 1rem 0;
    }
    
    /* 테이블 스타일링 */
    div[data-testid="stDataFrame"] {
        background-color: white;
        border-radius: 10px;
        padding: 0.5rem;
        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
    }
    
    /* 탭 스타일링 */
    button[data-baseweb="tab"] {
        font-weight: 600;
    }
    
    /* 서브헤더 배경 */
    .subheader {
        background-color: #f1f8fe;
        padding: 0.5rem 1rem;
        border-radius: 5px;
        margin-bottom: 1rem;
    }
    
    /* 정보 뱃지 */
    .info-badge {
        background-color: #e3f2fd;
        color: #1976d2;
        padding: 0.3rem 0.7rem;
        border-radius: 20px;
        display: inline-block;
        font-weight: 500;
        margin-right: 0.5rem;
    }
    
    /* 프로그레스 바 */
    div[data-testid="stProgress"] {
        height: 0.5rem !important;
    }
    
    /* 버튼 스타일링 */
    .stButton button {
        background-color: #1e88e5;
        color: white;
        border: none;
        font-weight: 500;
    }
    
    /* 경고/성공 메시지 개선 */
    div[data-testid="stAlert"] {
        border-radius: 10px;
        margin: 1rem 0;
    }
    
    /* 카테고리 분석 섹션 */
    .category-section {
        background-color: white;
        border-radius: 10px;
        padding: 1rem;
        margin-bottom: 1.5rem;
        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
    }
</style>
""", unsafe_allow_html=True)

api = HfApi()

# Cache for API responses
@lru_cache(maxsize=1000)
def cached_repo_info(repo_id, repo_type):
    return api.repo_info(repo_id=repo_id, repo_type=repo_type)

@lru_cache(maxsize=1000)
def cached_list_commits(repo_id, repo_type):
    return list(api.list_repo_commits(repo_id=repo_id, repo_type=repo_type))

@lru_cache(maxsize=100)
def cached_list_items(username, kind):
    if kind == "model":
        return list(api.list_models(author=username))
    elif kind == "dataset":
        return list(api.list_datasets(author=username))
    elif kind == "space":
        return list(api.list_spaces(author=username))
    return []

# Function to fetch trending accounts and create stats
@lru_cache(maxsize=1)
def get_trending_accounts(limit=100):
    try:
        trending_data = {"spaces": [], "models": []}
        
        # Get spaces for stats calculation
        spaces_response = requests.get("https://huggingface.co/api/spaces", 
                                      params={"limit": 10000}, 
                                      timeout=30)
        
        # Get models for stats calculation
        models_response = requests.get("https://huggingface.co/api/models", 
                                      params={"limit": 10000}, 
                                      timeout=30)
        
        # Process spaces data
        spaces_owners = []
        if spaces_response.status_code == 200:
            spaces = spaces_response.json()
            
            # Count spaces by owner
            owner_counts_spaces = {}
            for space in spaces:
                if '/' in space.get('id', ''):
                    owner, _ = space.get('id', '').split('/', 1)
                else:
                    owner = space.get('owner', '')
                
                if owner != 'None':
                    owner_counts_spaces[owner] = owner_counts_spaces.get(owner, 0) + 1
            
            # Get top owners by count for spaces
            top_owners_spaces = sorted(owner_counts_spaces.items(), key=lambda x: x[1], reverse=True)[:limit]
            trending_data["spaces"] = top_owners_spaces
            spaces_owners = [owner for owner, _ in top_owners_spaces]
        
        # Process models data
        models_owners = []
        if models_response.status_code == 200:
            models = models_response.json()
            
            # Count models by owner
            owner_counts_models = {}
            for model in models:
                if '/' in model.get('id', ''):
                    owner, _ = model.get('id', '').split('/', 1)
                else:
                    owner = model.get('owner', '')
                
                if owner != 'None':
                    owner_counts_models[owner] = owner_counts_models.get(owner, 0) + 1
            
            # Get top owners by count for models
            top_owners_models = sorted(owner_counts_models.items(), key=lambda x: x[1], reverse=True)[:limit]
            trending_data["models"] = top_owners_models
            models_owners = [owner for owner, _ in top_owners_models]
        
        # Combine rankings for overall trending based on appearance in both lists
        combined_score = {}
        for i, owner in enumerate(spaces_owners):
            if owner not in combined_score:
                combined_score[owner] = 0
            combined_score[owner] += (limit - i)  # Higher rank gives more points
            
        for i, owner in enumerate(models_owners):
            if owner not in combined_score:
                combined_score[owner] = 0
            combined_score[owner] += (limit - i)  # Higher rank gives more points
        
        # Sort by combined score
        sorted_combined = sorted(combined_score.items(), key=lambda x: x[1], reverse=True)[:limit]
        trending_authors = [owner for owner, _ in sorted_combined]
        
        return trending_authors, trending_data["spaces"], trending_data["models"]
    except Exception as e:
        st.error(f"Error fetching trending accounts: {str(e)}")
        fallback_authors = ["ritvik77", "facebook", "google", "stabilityai", "Salesforce", "tiiuae", "bigscience"]
        return fallback_authors, [(author, 0) for author in fallback_authors], [(author, 0) for author in fallback_authors]

# Rate limiting
class RateLimiter:
    def __init__(self, calls_per_second=10):
        self.calls_per_second = calls_per_second
        self.last_call = 0

    def wait(self):
        current_time = time.time()
        time_since_last_call = current_time - self.last_call
        if time_since_last_call < (1.0 / self.calls_per_second):
            time.sleep((1.0 / self.calls_per_second) - time_since_last_call)
        self.last_call = time.time()

rate_limiter = RateLimiter()

# Function to fetch commits for a repository (optimized)
def fetch_commits_for_repo(repo_id, repo_type, username, selected_year):
    try:
        rate_limiter.wait()
        # Skip private/gated repos upfront
        repo_info = cached_repo_info(repo_id, repo_type)
        if repo_info.private or (hasattr(repo_info, 'gated') and repo_info.gated):
            return [], 0

        # Get initial commit date
        initial_commit_date = pd.to_datetime(repo_info.created_at).tz_localize(None).date()
        commit_dates = []
        commit_count = 0

        # Add initial commit if it's from the selected year
        if initial_commit_date.year == selected_year:
            commit_dates.append(initial_commit_date)
            commit_count += 1

        # Get all commits
        commits = cached_list_commits(repo_id, repo_type)
        for commit in commits:
            commit_date = pd.to_datetime(commit.created_at).tz_localize(None).date()
            if commit_date.year == selected_year:
                commit_dates.append(commit_date)
                commit_count += 1

        return commit_dates, commit_count
    except Exception as e:
        return [], 0

# Function to get commit events for a user (optimized)
def get_commit_events(username, kind=None, selected_year=None):
    commit_dates = []
    items_with_type = []
    kinds = [kind] if kind else ["model", "dataset", "space"]

    for k in kinds:
        try:
            items = cached_list_items(username, k)
            items_with_type.extend((item, k) for item in items)
            repo_ids = [item.id for item in items]

            # Optimized parallel fetch with chunking
            chunk_size = 5  # Process 5 repos at a time
            for i in range(0, len(repo_ids), chunk_size):
                chunk = repo_ids[i:i + chunk_size]
                with ThreadPoolExecutor(max_workers=min(5, len(chunk))) as executor:
                    future_to_repo = {
                        executor.submit(fetch_commits_for_repo, repo_id, k, username, selected_year): repo_id
                        for repo_id in chunk
                    }
                    for future in as_completed(future_to_repo):
                        repo_commits, repo_count = future.result()
                        if repo_commits:  # Only extend if we got commits
                            commit_dates.extend(repo_commits)
        except Exception as e:
            st.warning(f"Error fetching {k}s for {username}: {str(e)}")

    # Create DataFrame with all commits
    df = pd.DataFrame(commit_dates, columns=["date"])
    if not df.empty:
        df = df.drop_duplicates()  # Remove any duplicate dates
    return df, items_with_type

# Calendar heatmap function (optimized)
def make_calendar_heatmap(df, title, year):
    if df.empty:
        st.info(f"No {title.lower()} found for {year}.")
        return

    # Optimize DataFrame operations
    df["count"] = 1
    df = df.groupby("date", as_index=False).sum()
    df["date"] = pd.to_datetime(df["date"])

    # Create date range more efficiently
    start = pd.Timestamp(f"{year}-01-01")
    end = pd.Timestamp(f"{year}-12-31")
    all_days = pd.date_range(start=start, end=end)

    # Optimize DataFrame creation and merging
    heatmap_data = pd.DataFrame({"date": all_days, "count": 0})
    heatmap_data = heatmap_data.merge(df, on="date", how="left", suffixes=("", "_y"))
    heatmap_data["count"] = heatmap_data["count_y"].fillna(0)
    heatmap_data = heatmap_data.drop("count_y", axis=1)

    # Calculate week and day of week more efficiently
    heatmap_data["dow"] = heatmap_data["date"].dt.dayofweek
    heatmap_data["week"] = (heatmap_data["date"] - start).dt.days // 7

    # Create pivot table more efficiently
    pivot = heatmap_data.pivot(index="dow", columns="week", values="count").fillna(0)

    # Optimize month labels calculation
    month_labels = pd.date_range(start, end, freq="MS").strftime("%b")
    month_positions = pd.date_range(start, end, freq="MS").map(lambda x: (x - start).days // 7)

    # Create custom colormap with specific boundaries
    from matplotlib.colors import ListedColormap, BoundaryNorm
    colors = ['#ebedf0', '#9be9a8', '#40c463', '#30a14e', '#216e39']  # GitHub-style green colors
    bounds = [0, 1, 3, 11, 31, float('inf')]  # Boundaries for color transitions
    cmap = ListedColormap(colors)
    norm = BoundaryNorm(bounds, cmap.N)

    # Create plot more efficiently
    fig, ax = plt.subplots(figsize=(12, 1.5))

    # Convert pivot values to integers to ensure proper color mapping
    pivot_int = pivot.astype(int)

    # Create heatmap with explicit vmin and vmax
    sns.heatmap(pivot_int, ax=ax, cmap=cmap, norm=norm, linewidths=0.5, linecolor="white",
                square=True, cbar=False, yticklabels=["M", "T", "W", "T", "F", "S", "S"])

    ax.set_title(f"{title}", fontsize=14, pad=10)
    ax.set_xlabel("")
    ax.set_ylabel("")
    ax.set_xticks(month_positions)
    ax.set_xticklabels(month_labels, fontsize=10)
    ax.set_yticklabels(ax.get_yticklabels(), rotation=0, fontsize=10)
    
    # 시각적 향상을 위한 figure 스타일링
    fig.tight_layout()
    fig.patch.set_facecolor('#F8F9FA')
    
    st.pyplot(fig)

# Function to create a fancy contribution radar chart
def create_contribution_radar(username, models_count, spaces_count, datasets_count, commits_count):
    # Create radar chart for contribution metrics
    categories = ['Models', 'Spaces', 'Datasets', 'Activity']
    values = [models_count, spaces_count, datasets_count, commits_count]
    
    # Normalize values for better visualization
    max_vals = [100, 100, 50, 500]  # Reasonable max values for each category
    normalized = [min(v/m, 1.0) for v, m in zip(values, max_vals)]
    
    # Create radar chart
    angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False).tolist()
    angles += angles[:1]  # Close the loop
    
    normalized += normalized[:1]  # Close the loop
    
    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw={'polar': True}, facecolor='#F8F9FA')
    
    # Add background grid with improved styling
    ax.set_theta_offset(np.pi / 2)
    ax.set_theta_direction(-1)
    ax.set_thetagrids(np.degrees(angles[:-1]), categories, fontsize=12, fontweight='bold')
    
    # 그리드 스타일링 개선
    ax.grid(color='#CCCCCC', linestyle='-', linewidth=0.5, alpha=0.7)
    
    # Draw the chart with improved color scheme
    ax.fill(angles, normalized, color='#4CAF50', alpha=0.25)
    ax.plot(angles, normalized, color='#4CAF50', linewidth=3)
    
    # Add value labels with improved styling
    for i, val in enumerate(values):
        angle = angles[i]
        x = (normalized[i] + 0.1) * np.cos(angle)
        y = (normalized[i] + 0.1) * np.sin(angle)
        ax.text(angle, normalized[i] + 0.1, str(val), 
                ha='center', va='center', fontsize=12, 
                fontweight='bold', color='#1976D2')
    
    # Add highlight circles
    circles = [0.25, 0.5, 0.75, 1.0]
    for circle in circles:
        ax.plot(angles, [circle] * len(angles), color='gray', alpha=0.3, linewidth=0.5, linestyle='--')
    
    ax.set_title(f"{username}'s Contribution Profile", fontsize=16, pad=20, fontweight='bold')
    
    # 배경 원 없애기
    ax.set_facecolor('#F8F9FA')
    
    return fig

# Function to create contribution distribution pie chart
def create_contribution_pie(model_commits, dataset_commits, space_commits):
    labels = ['Models', 'Datasets', 'Spaces']
    sizes = [model_commits, dataset_commits, space_commits]
    
    # Filter out zero values
    filtered_labels = [label for label, size in zip(labels, sizes) if size > 0]
    filtered_sizes = [size for size in sizes if size > 0]
    
    if not filtered_sizes:
        return None  # No data to show
    
    # Use a more attractive color scheme
    colors = ['#FF9800', '#2196F3', '#4CAF50']
    filtered_colors = [color for color, size in zip(colors, sizes) if size > 0]
    
    fig, ax = plt.subplots(figsize=(7, 7), facecolor='#F8F9FA')
    
    # Create exploded pie chart with improved styling
    explode = [0.1] * len(filtered_sizes)  # Explode all slices for better visualization
    
    wedges, texts, autotexts = ax.pie(
        filtered_sizes, 
        labels=None,  # We'll add custom labels
        colors=filtered_colors,
        autopct='%1.1f%%', 
        startangle=90, 
        shadow=True, 
        explode=explode,
        textprops={'fontsize': 14, 'weight': 'bold'},
        wedgeprops={'edgecolor': 'white', 'linewidth': 2}
    )
    
    # Customize the percentage text
    for autotext in autotexts:
        autotext.set_color('white')
        autotext.set_fontsize(12)
        autotext.set_weight('bold')
    
    # Add legend with custom styling
    ax.legend(
        wedges, 
        [f"{label} ({size})" for label, size in zip(filtered_labels, filtered_sizes)],
        title="Contribution Types",
        loc="center left",
        bbox_to_anchor=(0.85, 0.5),
        fontsize=12
    )
    
    ax.set_title('Distribution of Contributions by Type', fontsize=16, pad=20, fontweight='bold')
    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle
    
    return fig

# Function to create monthly activity chart
def create_monthly_activity(df, year):
    if df.empty:
        return None
    
    # Aggregate by month
    df['date'] = pd.to_datetime(df['date'])
    df['month'] = df['date'].dt.month
    df['month_name'] = df['date'].dt.strftime('%b')
    
    # Count by month and ensure all months are present
    month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    counts_by_month = df.groupby('month_name')['date'].count()
    monthly_counts = pd.Series([counts_by_month.get(m, 0) for m in month_order], index=month_order)
    
    # Create bar chart with improved styling
    fig, ax = plt.subplots(figsize=(14, 6), facecolor='#F8F9FA')
    
    # Create bars with gradient colors based on activity level
    norm = plt.Normalize(0, monthly_counts.max() if monthly_counts.max() > 0 else 1)
    colors = plt.cm.viridis(norm(monthly_counts.values))
    
    bars = ax.bar(monthly_counts.index, monthly_counts.values, color=colors, width=0.7)
    
    # Highlight the month with most activity
    if monthly_counts.max() > 0:
        max_idx = monthly_counts.argmax()
        bars[max_idx].set_color('#FF5722')
        bars[max_idx].set_edgecolor('black')
        bars[max_idx].set_linewidth(1.5)
    
    # Add labels and styling with enhanced design
    ax.set_title(f'Monthly Activity in {year}', fontsize=18, pad=20, fontweight='bold')
    ax.set_xlabel('Month', fontsize=14, labelpad=10)
    ax.set_ylabel('Number of Contributions', fontsize=14, labelpad=10)
    
    # Add value labels on top of bars with improved styling
    for i, count in enumerate(monthly_counts.values):
        if count > 0:
            ax.text(i, count + 0.5, str(int(count)), ha='center', fontsize=12, fontweight='bold')
    
    # Add grid for better readability with improved styling
    ax.grid(axis='y', linestyle='--', alpha=0.7, color='#CCCCCC')
    ax.set_axisbelow(True)  # Grid lines behind bars
    
    # Style the chart borders and background
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_linewidth(0.5)
    ax.spines['bottom'].set_linewidth(0.5)
    
    # Adjust tick parameters for better look
    ax.tick_params(axis='x', labelsize=12, pad=5)
    ax.tick_params(axis='y', labelsize=12, pad=5)
    
    plt.tight_layout()
    
    return fig

# Function to render follower growth simulation
def simulate_follower_data(username, spaces_count, models_count, total_commits):
    # Simulate follower growth based on contribution metrics
    # This is just a simulation for visual purposes
    import numpy as np
    from datetime import timedelta
    
    # Start with a base number of followers proportional to contribution metrics
    base_followers = max(10, int((spaces_count * 2 + models_count * 3 + total_commits/10) / 6))
    
    # Generate timestamps for the past year
    end_date = datetime.now()
    start_date = end_date - timedelta(days=365)
    dates = pd.date_range(start=start_date, end=end_date, freq='W')  # Weekly data points
    
    # Generate follower growth with some randomness
    followers = []
    current = base_followers / 2  # Start from half the base
    
    for i in range(len(dates)):
        growth_factor = 1 + (np.random.random() * 0.1)  # Random growth between 0% and 10%
        current = current * growth_factor
        followers.append(int(current))
    
    # Ensure end value matches our base_followers estimate
    followers[-1] = base_followers
    
    # Create the chart with improved styling
    fig, ax = plt.subplots(figsize=(14, 6), facecolor='#F8F9FA')
    
    # Create gradient line for better visualization
    points = np.array([dates, followers]).T.reshape(-1, 1, 2)
    segments = np.concatenate([points[:-1], points[1:]], axis=1)
    
    from matplotlib.collections import LineCollection
    norm = plt.Normalize(0, len(segments))
    lc = LineCollection(segments, cmap='viridis', norm=norm, linewidth=3, alpha=0.8)
    lc.set_array(np.arange(len(segments)))
    line = ax.add_collection(lc)
    
    # Add markers
    ax.scatter(dates, followers, s=50, color='#9C27B0', alpha=0.8, zorder=10)
    
    # Add styling with enhanced design
    ax.set_title(f"Estimated Follower Growth for {username}", fontsize=18, pad=20, fontweight='bold')
    ax.set_xlabel("Date", fontsize=14, labelpad=10)
    ax.set_ylabel("Followers", fontsize=14, labelpad=10)
    
    # Format the axes limits
    ax.set_xlim(dates.min(), dates.max())
    ax.set_ylim(0, max(followers) * 1.1)
    
    # Add grid for better readability with improved styling
    ax.grid(True, linestyle='--', alpha=0.7, color='#CCCCCC')
    ax.set_axisbelow(True)  # Grid lines behind plot
    
    # Style the chart borders and background
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_linewidth(0.5)
    ax.spines['bottom'].set_linewidth(0.5)
    
    # Adjust tick parameters for better look
    ax.tick_params(axis='x', labelsize=12, rotation=45)
    ax.tick_params(axis='y', labelsize=12)
    
    # Add annotations for start and end points
    ax.annotate(f"Start: {followers[0]}", 
                xy=(dates[0], followers[0]),
                xytext=(10, 10),
                textcoords='offset points',
                fontsize=12,
                fontweight='bold',
                color='#9C27B0',
                bbox=dict(boxstyle="round,pad=0.3", fc="#F3E5F5", ec="#9C27B0", alpha=0.8))
    
    ax.annotate(f"Current: {followers[-1]}", 
                xy=(dates[-1], followers[-1]),
                xytext=(-10, 10),
                textcoords='offset points',
                fontsize=12,
                fontweight='bold',
                color='#9C27B0',
                ha='right',
                bbox=dict(boxstyle="round,pad=0.3", fc="#F3E5F5", ec="#9C27B0", alpha=0.8))
    
    plt.tight_layout()
    
    return fig

# Function to create ranking position visualization
def create_ranking_chart(username, overall_rank, spaces_rank, models_rank):
    if not (overall_rank or spaces_rank or models_rank):
        return None
    
    # Create a horizontal bar chart for rankings with improved styling
    fig, ax = plt.subplots(figsize=(12, 5), facecolor='#F8F9FA')
    
    categories = []
    positions = []
    colors = []
    rank_values = []
    
    if overall_rank:
        categories.append('Overall')
        positions.append(101 - overall_rank)  # Invert rank for visualization (higher is better)
        colors.append('#673AB7')
        rank_values.append(overall_rank)
    
    if spaces_rank:
        categories.append('Spaces')
        positions.append(101 - spaces_rank)
        colors.append('#2196F3')
        rank_values.append(spaces_rank)
    
    if models_rank:
        categories.append('Models')
        positions.append(101 - models_rank)
        colors.append('#FF9800')
        rank_values.append(models_rank)
    
    # Create horizontal bars with enhanced styling
    bars = ax.barh(categories, positions, color=colors, alpha=0.8, height=0.6, 
                  edgecolor='white', linewidth=1.5)
    
    # Add rank values as text with improved styling
    for i, bar in enumerate(bars):
        ax.text(bar.get_width() + 2, bar.get_y() + bar.get_height()/2, 
                f'Rank #{rank_values[i]}', va='center', fontsize=12, 
                fontweight='bold', color=colors[i])
    
    # Set chart properties with enhanced styling
    ax.set_xlim(0, 105)
    ax.set_title(f"Ranking Positions for {username} (Top 100)", fontsize=18, pad=20, fontweight='bold')
    ax.set_xlabel("Percentile (higher is better)", fontsize=14, labelpad=10)
    
    # Add explanatory text
    ax.text(50, -0.6, "← Lower rank (higher number) | Higher rank (lower number) →", 
            ha='center', va='center', fontsize=10, fontweight='bold', color='#666666')
    
    # Add a vertical line at 90th percentile to highlight top 10 with improved styling
    ax.axvline(x=90, color='#FF5252', linestyle='--', alpha=0.7, linewidth=2)
    ax.text(92, len(categories)/2, 'Top 10', color='#D32F2F', fontsize=12, 
           rotation=90, va='center', fontweight='bold')
    
    # Style the chart borders and background
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_linewidth(0.5)
    ax.spines['bottom'].set_linewidth(0.5)
    
    # Adjust tick parameters for better look
    ax.tick_params(axis='x', labelsize=12)
    ax.tick_params(axis='y', labelsize=14, pad=5)
    
    # Add grid for better readability
    ax.grid(axis='x', linestyle='--', alpha=0.5, color='#CCCCCC')
    ax.set_axisbelow(True)  # Grid lines behind bars
    
    # Invert x-axis to show ranking position more intuitively
    ax.invert_xaxis()
    
    plt.tight_layout()
    return fig

# Fetch trending accounts with a loading spinner (do this once at the beginning)
with st.spinner("Loading trending accounts..."):
    trending_accounts, top_owners_spaces, top_owners_models = get_trending_accounts(limit=100)

# Sidebar
with st.sidebar:
    st.markdown('<h1 style="text-align: center; color: #1E88E5;">👤 Contributor</h1>', unsafe_allow_html=True)
    
    # Create tabs for Spaces and Models rankings - ONLY SHOWING FIRST TWO TABS
    tab1, tab2 = st.tabs([
        "Top 100 Overall", 
        "Top Spaces & Models"
    ])
    
    with tab1:
        # Show combined trending accounts list
        st.markdown('<div class="subheader"><h3>🔥 Top 100 Contributors</h3></div>', unsafe_allow_html=True)
        
        # Create a data frame for the table
        if trending_accounts:
            # Create a mapping from username to Spaces and Models rankings
            spaces_rank = {owner: idx+1 for idx, (owner, _) in enumerate(top_owners_spaces)}
            models_rank = {owner: idx+1 for idx, (owner, _) in enumerate(top_owners_models)}
            
            # Create the overall ranking dataframe with trophies for top 3
            overall_data = []
            for idx, username in enumerate(trending_accounts[:100]):
                # Add trophy emojis for top 3
                rank_display = ""
                if idx == 0:
                    rank_display = "🏆 "  # Gold trophy for 1st place
                elif idx == 1:
                    rank_display = "🏆 "  # Silver trophy for 2nd place
                elif idx == 2:
                    rank_display = "🏆 "  # Bronze trophy for 3rd place
                
                # Use strings for all rankings to avoid type conversion issues
                spaces_position = str(spaces_rank.get(username, "-"))
                models_position = str(models_rank.get(username, "-"))
                overall_data.append([f"{rank_display}{username}", spaces_position, models_position])
            
            ranking_data_overall = pd.DataFrame(
                overall_data, 
                columns=["Contributor", "Spaces Rank", "Models Rank"]
            )
            ranking_data_overall.index = ranking_data_overall.index + 1  # Start index from 1 for ranking

            st.dataframe(
                ranking_data_overall,
                height=900,  # 약 30행 정도 보이도록 픽셀 단위 높이 설정 (필요에 따라 조정 가능)
                column_config={
                    "Contributor": st.column_config.TextColumn("Contributor"),
                    "Spaces Rank": st.column_config.TextColumn("Spaces Rank"),
                    "Models Rank": st.column_config.TextColumn("Models Rank")
                },
                use_container_width=True,
                hide_index=False
            )

    with tab2:
        # Show trending accounts by Spaces & Models
        st.markdown('<div class="subheader"><h3>🚀 Spaces Leaders</h3></div>', unsafe_allow_html=True)
        
        # Create a data frame for the Spaces table with medals for top 3
        if top_owners_spaces:
            spaces_data = []
            for idx, (owner, count) in enumerate(top_owners_spaces[:50]):
                # Add medal emojis for top 3
                rank_display = ""
                if idx == 0:
                    rank_display = "🥇 "  # Gold medal for 1st place
                elif idx == 1:
                    rank_display = "🥈 "  # Silver medal for 2nd place
                elif idx == 2:
                    rank_display = "🥉 "  # Bronze medal for 3rd place
                
                spaces_data.append([f"{rank_display}{owner}", count])
            
            ranking_data_spaces = pd.DataFrame(spaces_data, columns=["Contributor", "Spaces Count(Top 500 positions)"])
            ranking_data_spaces.index = ranking_data_spaces.index + 1  # Start index from 1 for ranking
            
            st.dataframe(
                ranking_data_spaces,
                column_config={
                    "Contributor": st.column_config.TextColumn("Contributor"),
                    "Spaces Count": st.column_config.NumberColumn("Spaces Count", format="%d")
                },
                use_container_width=True,
                hide_index=False
            )
        
        # Display the top Models accounts list with medals for top 3
        st.markdown('<div class="subheader"><h3>🧠 Models Leaders</h3></div>', unsafe_allow_html=True)
        
        # Create a data frame for the Models table with medals for top 3
        if top_owners_models:
            models_data = []
            for idx, (owner, count) in enumerate(top_owners_models[:50]):
                # Add medal emojis for top 3
                rank_display = ""
                if idx == 0:
                    rank_display = "🥇 "  # Gold medal for 1st place
                elif idx == 1:
                    rank_display = "🥈 "  # Silver medal for 2nd place
                elif idx == 2:
                    rank_display = "🥉 "  # Bronze medal for 3rd place
                
                models_data.append([f"{rank_display}{owner}", count])
            
            ranking_data_models = pd.DataFrame(models_data, columns=["Contributor", "Models Count(Top 500 positions)"])
            ranking_data_models.index = ranking_data_models.index + 1  # Start index from 1 for ranking
            
            st.dataframe(
                ranking_data_models,
                column_config={
                    "Contributor": st.column_config.TextColumn("Contributor"),
                    "Models Count": st.column_config.NumberColumn("Models Count", format="%d")
                },
                use_container_width=True,
                hide_index=False
            )
    
    # Add visual divider
    st.markdown('<hr style="margin: 2rem 0; border-color: #e0e0e0;">', unsafe_allow_html=True)
    
    # Display contributor selection with enhanced styling
    st.markdown('<div class="subheader"><h3>Select Contributor</h3></div>', unsafe_allow_html=True)
    selected_trending = st.selectbox(
        "Choose from trending accounts",
        options=trending_accounts[:100],  # Limit to top 100
        index=0 if trending_accounts else None,
        key="trending_selectbox"
    )
    
    # Custom account input option with enhanced styling
    st.markdown('<div style="text-align: center; margin: 15px 0; font-weight: bold;">- OR -</div>', unsafe_allow_html=True)
    custom = st.text_input("Enter a username/organization:", placeholder="e.g. facebook, google...")
    
    # Add visual divider
    st.markdown('<hr style="margin: 1.5rem 0; border-color: #e0e0e0;">', unsafe_allow_html=True)
    
    # Set username based on selection or custom input
    if custom.strip():
        username = custom.strip()
    elif selected_trending:
        username = selected_trending
    else:
        username = "facebook"  # Default fallback
    
    # Year selection with enhanced styling
    st.markdown('<div class="subheader"><h3>🗓️ Time Period</h3></div>', unsafe_allow_html=True)
    year_options = list(range(datetime.now().year, 2017, -1))
    selected_year = st.selectbox("Select Year:", options=year_options)
    
    # Additional options for customization with enhanced styling
    st.markdown('<div class="subheader"><h3>⚙️ Display Options</h3></div>', unsafe_allow_html=True)
    show_models = st.checkbox("Show Models", value=True)
    show_datasets = st.checkbox("Show Datasets", value=True)
    show_spaces = st.checkbox("Show Spaces", value=True)

# Main Content
st.markdown(f'<h1 style="text-align: center; color: #1E88E5; margin-bottom: 2rem;">🤗 Hugging Face Contributions</h1>', unsafe_allow_html=True)

if username:
    # Create a header card with contributor info
    header_col1, header_col2 = st.columns([1, 2])
    with header_col1:
        st.markdown(f'<div style="background-color: #E3F2FD; padding: 20px; border-radius: 10px; border-left: 5px solid #1E88E5;">'
                  f'<h2 style="color: #1E88E5;">👤 {username}</h2>'
                  f'<p style="font-size: 16px;">Analyzing contributions for {selected_year}</p>'
                  f'<p><a href="https://huggingface.co/{username}" target="_blank" style="color: #1E88E5; font-weight: bold;">View Profile</a></p>'
                  f'</div>', unsafe_allow_html=True)
    
    with header_col2:
        # Add explanation about the app
        st.markdown(f'<div style="background-color: #F3E5F5; padding: 20px; border-radius: 10px; border-left: 5px solid #9C27B0;">'
                  f'<h3 style="color: #9C27B0;">About This Analysis</h3>'
                  f'<p>This dashboard analyzes {username}\'s contributions to Hugging Face in {selected_year}, including models, datasets, and spaces.</p>'
                  f'<p style="font-style: italic; font-size: 12px;">* Some metrics like follower growth are simulated for visualization purposes.</p>'
                  f'</div>', unsafe_allow_html=True)

    with st.spinner(f"Fetching contribution data for {username}..."):
        # Initialize variables for tracking
        overall_rank = None
        spaces_rank = None
        models_rank = None
        spaces_count = 0
        models_count = 0
        datasets_count = 0
        
        # Display contributor rank if in top 100
        if username in trending_accounts[:100]:
            overall_rank = trending_accounts.index(username) + 1
            
            # Create a prominent ranking display
            st.markdown(f'<div style="background-color: #FFF8E1; padding: 20px; border-radius: 10px; border-left: 5px solid #FFC107; margin: 1rem 0;">'
                      f'<h2 style="color: #FFA000; text-align: center;">🏆 Ranked #{overall_rank} in Top Contributors</h2>'
                      f'</div>', unsafe_allow_html=True)
            
            # Find user in spaces ranking
            for i, (owner, count) in enumerate(top_owners_spaces):
                if owner == username:
                    spaces_rank = i+1
                    spaces_count = count
                    break
            
            # Find user in models ranking
            for i, (owner, count) in enumerate(top_owners_models):
                if owner == username:
                    models_rank = i+1
                    models_count = count
                    break
                    
            # Display ranking visualization
            rank_chart = create_ranking_chart(username, overall_rank, spaces_rank, models_rank)
            if rank_chart:
                st.pyplot(rank_chart)
        
        # Create a dictionary to store commits by type
        commits_by_type = {}
        commit_counts_by_type = {}
        
        # Determine which types to fetch based on checkboxes
        types_to_fetch = []
        if show_models:
            types_to_fetch.append("model")
        if show_datasets:
            types_to_fetch.append("dataset")
        if show_spaces:
            types_to_fetch.append("space")
        
        if not types_to_fetch:
            st.warning("Please select at least one content type to display (Models, Datasets, or Spaces)")
            st.stop()

        # Create a progress container
        progress_container = st.container()
        progress_container.markdown('<h3 style="color: #1E88E5;">Fetching Repository Data...</h3>', unsafe_allow_html=True)
        progress_bar = progress_container.progress(0)

        # Fetch commits for each selected type
        for type_index, kind in enumerate(types_to_fetch):
            try:
                items = cached_list_items(username, kind)
                
                # Update counts for radar chart
                if kind == "model":
                    models_count = len(items)
                elif kind == "dataset":
                    datasets_count = len(items)
                elif kind == "space":
                    spaces_count = len(items)
                    
                repo_ids = [item.id for item in items]
                
                progress_container.info(f"Found {len(repo_ids)} {kind}s for {username}")

                # Process repos in chunks
                chunk_size = 5
                total_commits = 0
                all_commit_dates = []

                for i in range(0, len(repo_ids), chunk_size):
                    chunk = repo_ids[i:i + chunk_size]
                    with ThreadPoolExecutor(max_workers=min(5, len(chunk))) as executor:
                        future_to_repo = {
                            executor.submit(fetch_commits_for_repo, repo_id, kind, username, selected_year): repo_id
                            for repo_id in chunk
                        }
                        for future in as_completed(future_to_repo):
                            repo_commits, repo_count = future.result()
                            if repo_commits:
                                all_commit_dates.extend(repo_commits)
                                total_commits += repo_count
                    
                    # Update progress for all types
                    progress_per_type = 1.0 / len(types_to_fetch)
                    current_type_progress = min(1.0, (i + len(chunk)) / max(1, len(repo_ids)))
                    overall_progress = (type_index * progress_per_type) + (current_type_progress * progress_per_type)
                    progress_bar.progress(overall_progress)
                
                commits_by_type[kind] = all_commit_dates
                commit_counts_by_type[kind] = total_commits

            except Exception as e:
                st.warning(f"Error fetching {kind}s for {username}: {str(e)}")
                commits_by_type[kind] = []
                commit_counts_by_type[kind] = 0

        # Complete progress
        progress_bar.progress(1.0)
        progress_container.success("Data fetching complete!")
        time.sleep(0.5)  # Short pause for visual feedback
        progress_container.empty()  # Clear the progress indicators

        # Calculate total commits across all types
        total_commits = sum(commit_counts_by_type.values())

        # Main dashboard layout with improved structure
        st.markdown(f'<h2 style="color: #1E88E5; border-bottom: 2px solid #E0E0E0; padding-bottom: 8px; margin-top: 2rem;">Activity Overview</h2>', unsafe_allow_html=True)
        
        # Profile summary
        profile_col1, profile_col2 = st.columns([1, 2])
        
        with profile_col1:
            # Create a stats card with key metrics
            st.markdown(f'<div style="background-color: white; padding: 20px; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">'
                      f'<h3 style="color: #1E88E5; text-align: center; margin-bottom: 15px;">Contribution Stats</h3>'
                      f'<div style="display: flex; justify-content: space-between; margin-bottom: 10px;">'
                      f'<span style="font-weight: bold;">Total Commits:</span><span>{total_commits}</span></div>'
                      f'<div style="display: flex; justify-content: space-between; margin-bottom: 10px;">'
                      f'<span style="font-weight: bold;">Models:</span><span>{models_count}</span></div>'
                      f'<div style="display: flex; justify-content: space-between; margin-bottom: 10px;">'
                      f'<span style="font-weight: bold;">Datasets:</span><span>{datasets_count}</span></div>'
                      f'<div style="display: flex; justify-content: space-between; margin-bottom: 10px;">'
                      f'<span style="font-weight: bold;">Spaces:</span><span>{spaces_count}</span></div>'
                      f'</div>', unsafe_allow_html=True)
            
            # Type breakdown pie chart
            model_commits = commit_counts_by_type.get("model", 0)
            dataset_commits = commit_counts_by_type.get("dataset", 0)
            space_commits = commit_counts_by_type.get("space", 0)
            
            pie_chart = create_contribution_pie(model_commits, dataset_commits, space_commits)
            if pie_chart:
                st.pyplot(pie_chart)
        
        with profile_col2:
            # Display contribution radar chart
            radar_fig = create_contribution_radar(username, models_count, spaces_count, datasets_count, total_commits)
            st.pyplot(radar_fig)

            # Create DataFrame for all commits
            all_commits = []
            for commits in commits_by_type.values():
                all_commits.extend(commits)
            all_df = pd.DataFrame(all_commits, columns=["date"])
            if not all_df.empty:
                all_df = all_df.drop_duplicates()  # Remove any duplicate dates

        # Calendar heatmap for all commits in a separate section
        st.markdown(f'<h2 style="color: #1E88E5; border-bottom: 2px solid #E0E0E0; padding-bottom: 8px; margin-top: 2rem;">Contribution Calendar</h2>', unsafe_allow_html=True)
        
        if not all_df.empty:
            make_calendar_heatmap(all_df, "All Contributions", selected_year)
        else:
            st.info(f"No contributions found for {username} in {selected_year}")

        # Monthly activity chart
        st.markdown(f'<h2 style="color: #1E88E5; border-bottom: 2px solid #E0E0E0; padding-bottom: 8px; margin-top: 2rem;">Monthly Activity</h2>', unsafe_allow_html=True)
        
        monthly_fig = create_monthly_activity(all_df, selected_year)
        if monthly_fig:
            st.pyplot(monthly_fig)
        else:
            st.info(f"No activity data available for {username} in {selected_year}")
        
        # Follower growth simulation
        st.markdown(f'<h2 style="color: #1E88E5; border-bottom: 2px solid #E0E0E0; padding-bottom: 8px; margin-top: 2rem;">Growth Projection</h2>', unsafe_allow_html=True)
        st.markdown('<div style="background-color: #EDE7F6; padding: 10px; border-radius: 5px; margin-bottom: 15px;">'
                  '<p style="font-style: italic; margin: 0;">📊 This is a simulation based on contribution metrics - for visualization purposes only</p>'
                  '</div>', unsafe_allow_html=True)
        
        follower_chart = simulate_follower_data(username, spaces_count, models_count, total_commits)
        st.pyplot(follower_chart)
        
        # Analytics summary section
        if total_commits > 0:
            st.markdown(f'<h2 style="color: #1E88E5; border-bottom: 2px solid #E0E0E0; padding-bottom: 8px; margin-top: 2rem;">📊 Analytics Summary</h2>', unsafe_allow_html=True)
            
            # Contribution pattern analysis
            monthly_df = pd.DataFrame(all_commits, columns=["date"])
            monthly_df['date'] = pd.to_datetime(monthly_df['date'])
            monthly_df['month'] = monthly_df['date'].dt.month
            
            if not monthly_df.empty:
                most_active_month = monthly_df['month'].value_counts().idxmax()
                month_name = datetime(2020, most_active_month, 1).strftime('%B')
                
                # Create a summary card
                st.markdown(f'<div style="background-color: white; padding: 25px; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">'
                          f'<h3 style="color: #1E88E5; border-bottom: 1px solid #E0E0E0; padding-bottom: 10px;">Activity Analysis for {username}</h3>'
                          f'<ul style="list-style-type: none; padding-left: 5px;">'
                          f'<li style="margin: 15px 0; font-size: 16px;">📈 <strong>Total Activity:</strong> {total_commits} contributions in {selected_year}</li>'
                          f'<li style="margin: 15px 0; font-size: 16px;">🗓️ <strong>Most Active Month:</strong> {month_name} with {monthly_df["month"].value_counts().max()} contributions</li>'
                          f'<li style="margin: 15px 0; font-size: 16px;">🧩 <strong>Repository Breakdown:</strong> {models_count} Models, {spaces_count} Spaces, {datasets_count} Datasets</li>'
                          f'</ul>', unsafe_allow_html=True)
                
                # Add ranking context if available
                if overall_rank:
                    percentile = 100 - overall_rank
                    st.markdown(f'<div style="margin-top: 20px;">'
                              f'<h3 style="color: #1E88E5; border-bottom: 1px solid #E0E0E0; padding-bottom: 10px;">Ranking Analysis</h3>'
                              f'<ul style="list-style-type: none; padding-left: 5px;">'
                              f'<li style="margin: 15px 0; font-size: 16px;">🏆 <strong>Overall Ranking:</strong> #{overall_rank} (Top {percentile}% of contributors)</li>', unsafe_allow_html=True)
                    
                    badge_html = '<div style="margin: 20px 0;">'
                    
                    if spaces_rank and spaces_rank <= 10:
                        badge_html += f'<span style="background-color: #FFECB3; color: #FF6F00; padding: 8px 15px; border-radius: 20px; font-weight: bold; margin-right: 10px; display: inline-block; margin-bottom: 10px;">🌟 Elite Spaces Contributor (#{spaces_rank})</span>'
                    elif spaces_rank and spaces_rank <= 30:
                        badge_html += f'<span style="background-color: #E1F5FE; color: #0277BD; padding: 8px 15px; border-radius: 20px; font-weight: bold; margin-right: 10px; display: inline-block; margin-bottom: 10px;">✨ Outstanding Spaces Contributor (#{spaces_rank})</span>'
                    
                    if models_rank and models_rank <= 10:
                        badge_html += f'<span style="background-color: #FFECB3; color: #FF6F00; padding: 8px 15px; border-radius: 20px; font-weight: bold; margin-right: 10px; display: inline-block; margin-bottom: 10px;">🌟 Elite Models Contributor (#{models_rank})</span>'
                    elif models_rank and models_rank <= 30:
                        badge_html += f'<span style="background-color: #E1F5FE; color: #0277BD; padding: 8px 15px; border-radius: 20px; font-weight: bold; margin-right: 10px; display: inline-block; margin-bottom: 10px;">✨ Outstanding Models Contributor (#{models_rank})</span>'
                    
                    badge_html += '</div>'
                    
                    # Add achievement badges
                    if spaces_rank or models_rank:
                        st.markdown(badge_html, unsafe_allow_html=True)
                    
                    st.markdown('</ul></div></div>', unsafe_allow_html=True)
        
        # Detailed category analysis section
        st.markdown(f'<h2 style="color: #1E88E5; border-bottom: 2px solid #E0E0E0; padding-bottom: 8px; margin-top: 2rem;">Detailed Category Analysis</h2>', unsafe_allow_html=True)
        
        # Create category cards in columns
        cols = st.columns(len(types_to_fetch)) if types_to_fetch else st.columns(1)
        
        category_icons = {
            "model": "🧠",
            "dataset": "📦",
            "space": "🚀"
        }
        
        category_colors = {
            "model": "#FF9800",
            "dataset": "#2196F3",
            "space": "#4CAF50"
        }
        
        for i, kind in enumerate(types_to_fetch):
            with cols[i]:
                try:
                    emoji = category_icons.get(kind, "📊")
                    label = kind.capitalize() + "s"
                    color = category_colors.get(kind, "#1E88E5")
                    
                    total = len(cached_list_items(username, kind))
                    commits = commits_by_type.get(kind, [])
                    commit_count = commit_counts_by_type.get(kind, 0)
                    
                    # Create styled card header
                    st.markdown(f'<div style="background-color: white; padding: 20px; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); border-top: 5px solid {color};">'
                              f'<h3 style="color: {color}; text-align: center;">{emoji} {label}</h3>'
                              f'<div style="display: flex; justify-content: space-between; margin: 15px 0;">'
                              f'<span style="font-weight: bold;">Total:</span><span>{total}</span></div>'
                              f'<div style="display: flex; justify-content: space-between; margin-bottom: 15px;">'
                              f'<span style="font-weight: bold;">Commits:</span><span>{commit_count}</span></div>'
                              f'</div>', unsafe_allow_html=True)
                    
                    # Create calendar for this type
                    df_kind = pd.DataFrame(commits, columns=["date"])
                    if not df_kind.empty:
                        df_kind = df_kind.drop_duplicates()  # Remove any duplicate dates
                        make_calendar_heatmap(df_kind, f"{label} Commits", selected_year)
                    else:
                        st.info(f"No {label.lower()} activity in {selected_year}")
                        
                except Exception as e:
                    st.warning(f"Error processing {kind.capitalize()}s: {str(e)}")
                    # Show empty placeholder
                    st.markdown(f'<div style="background-color: white; padding: 20px; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); border-top: 5px solid #9E9E9E; text-align: center;">'
                              f'<h3 style="color: #9E9E9E;">⚠️ Error</h3>'
                              f'<p>Could not load {kind.capitalize()}s data</p>'
                              f'</div>', unsafe_allow_html=True)
        
        # Footer
        st.markdown('<hr style="margin: 3rem 0 1rem 0;">', unsafe_allow_html=True)
        st.markdown('<p style="text-align: center; color: #9E9E9E; font-size: 0.8rem;">Hugging Face Contributions Dashboard | Data fetched from Hugging Face API</p>', unsafe_allow_html=True)
else:
    # If no username is selected, show welcome screen
    st.markdown(f'<div style="text-align: center; margin: 50px 0;">'
              f'<img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg" style="width: 200px; margin-bottom: 30px;">'
              f'<h2>Welcome to Hugging Face Contributions Dashboard</h2>'
              f'<p style="font-size: 1.2rem;">Please select a contributor from the sidebar to view their activity.</p>'
              f'</div>', unsafe_allow_html=True)