Update app.py

app.py

@@ -1248,1254 +1248,4 @@ else:
               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)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 []
-
-# 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 quick commit stats for a user (optimized for ranking)
-@st.cache_data(ttl=3600)  # Cache for 1 hour
-def get_user_commit_stats(username):
-    """Fetch basic commit statistics for a user"""
-    try:
-        total_commits = 0
-        items_count = {"model": 0, "dataset": 0, "space": 0}
-        
-        for kind in ["model", "dataset", "space"]:
-            try:
-                items = cached_list_items(username, kind)
-                items_count[kind] = len(items)
-                
-                # Sample a few repos to estimate commit activity
-                sample_size = min(5, len(items))  # Check up to 5 repos per type
-                if sample_size > 0:
-                    sample_items = items[:sample_size]
-                    for item in sample_items:
-                        try:
-                            rate_limiter.wait()
-                            commits = cached_list_commits(item.id, kind)
-                            total_commits += len(commits)
-                        except:
-                            pass
-                    
-                    # Estimate total commits based on sample
-                    if sample_size < len(items):
-                        total_commits = int(total_commits * len(items) / sample_size)
-            except:
-                pass
-        
-        # Calculate contribution score based on commits only
-        score = total_commits
-        
-        return {
-            "username": username,
-            "models": items_count["model"],
-            "spaces": items_count["space"],
-            "datasets": items_count["dataset"],
-            "estimated_commits": total_commits,
-            "score": score
-        }
-    except Exception as e:
-        return {
-            "username": username,
-            "models": 0,
-            "spaces": 0,
-            "datasets": 0,
-            "estimated_commits": 0,
-            "score": 0
-        }
-
-# Enhanced function to get trending accounts with commit-based ranking
-@st.cache_data(ttl=3600)  # Cache for 1 hour
-def get_trending_accounts_with_commits(limit=100):
-    try:
-        # First, get top accounts by model/space count
-        spaces_response = requests.get("https://huggingface.co/api/spaces", 
-                                      params={"limit": 10000}, 
-                                      timeout=30)
-        models_response = requests.get("https://huggingface.co/api/models", 
-                                      params={"limit": 10000}, 
-                                      timeout=30)
-        
-        # Process spaces data
-        top_space_owners = []
-        if spaces_response.status_code == 200:
-            spaces = spaces_response.json()
-            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
-            
-            top_space_owners = sorted(owner_counts_spaces.items(), key=lambda x: x[1], reverse=True)[:limit]
-        
-        # Process models data
-        top_model_owners = []
-        if models_response.status_code == 200:
-            models = models_response.json()
-            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
-            
-            top_model_owners = sorted(owner_counts_models.items(), key=lambda x: x[1], reverse=True)[:limit]
-        
-        # Get unique users from top 100 of both lists
-        unique_users = set()
-        for owner, _ in top_space_owners[:100]:
-            unique_users.add(owner)
-        for owner, _ in top_model_owners[:100]:
-            unique_users.add(owner)
-        
-        # Create progress bar for fetching commit stats
-        progress_text = st.empty()
-        progress_bar = st.progress(0)
-        progress_text.text(f"Analyzing top contributors... (0/{len(unique_users)})")
-        
-        # Fetch commit stats for all unique users
-        user_stats = []
-        with ThreadPoolExecutor(max_workers=5) as executor:
-            future_to_user = {executor.submit(get_user_commit_stats, user): user for user in unique_users}
-            completed = 0
-            for future in as_completed(future_to_user):
-                stats = future.result()
-                if stats["score"] > 0:  # Only include users with some activity
-                    user_stats.append(stats)
-                completed += 1
-                progress = completed / len(unique_users)
-                progress_bar.progress(progress)
-                progress_text.text(f"Analyzing top contributors... ({completed}/{len(unique_users)})")
-        
-        # Clear progress indicators
-        progress_text.empty()
-        progress_bar.empty()
-        
-        # Sort by score (combination of commits and repo counts)
-        user_stats.sort(key=lambda x: x["score"], reverse=True)
-        
-        # Extract rankings
-        trending_authors = [stat["username"] for stat in user_stats[:limit]]
-        
-        # Create detailed rankings for display
-        spaces_rank_data = [(stat["username"], stat["spaces"]) for stat in user_stats if stat["spaces"] > 0][:limit]
-        models_rank_data = [(stat["username"], stat["models"]) for stat in user_stats if stat["models"] > 0][:limit]
-        
-        return trending_authors, spaces_rank_data, models_rank_data, user_stats[:limit]
-        
-    except Exception as e:
-        st.error(f"Error fetching trending accounts: {str(e)}")
-        fallback_authors = ["ritvik77", "facebook", "google", "stabilityai", "Salesforce", "tiiuae", "bigscience"]
-        fallback_stats = [{"username": author, "models": 0, "spaces": 0, "datasets": 0, "estimated_commits": 0, "score": 0} for author in fallback_authors]
-        return fallback_authors, [(author, 0) for author in fallback_authors], [(author, 0) for author in fallback_authors], fallback_stats
-
-# 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 and analyzing top contributors... This may take a few moments."):
-    trending_accounts, top_owners_spaces, top_owners_models, user_stats = get_trending_accounts_with_commits(limit=100)
-
-# Sidebar
-with st.sidebar:
-    st.markdown('<h1 style="text-align: center; color: #1E88E5;">👤 Contributor</h1>', unsafe_allow_html=True)
-    
-    # Create tabs for rankings
-    tab1, tab2 = st.tabs([
-        "Top 100 Overall", 
-        "Top Spaces & Models"
-    ])
-    
-    with tab1:
-        # Show combined trending accounts list with commit-based ranking
-        st.markdown('<div class="subheader"><h3>🔥 Top 100 Contributors by Commits</h3></div>', unsafe_allow_html=True)
-        st.markdown('<p style="font-size: 0.9rem; color: #666; margin-bottom: 10px;">Ranked by total commit count</p>', unsafe_allow_html=True)
-        
-        # Create a data frame for the table
-        if user_stats:
-            # Create the overall ranking dataframe with trophies for top 3
-            overall_data = []
-            for idx, stat in enumerate(user_stats[: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
-                
-                overall_data.append([
-                    f"{rank_display}{stat['username']}", 
-                    str(stat['estimated_commits']),
-                    str(stat['models']),
-                    str(stat['spaces']),
-                    str(stat['datasets'])
-                ])
-            
-            ranking_data_overall = pd.DataFrame(
-                overall_data, 
-                columns=["Contributor", "Total Commits", "Models", "Spaces", "Datasets"]
-            )
-            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
-                column_config={
-                    "Contributor": st.column_config.TextColumn("Contributor"),
-                    "Total Commits": st.column_config.TextColumn("Total Commits"),
-                    "Models": st.column_config.TextColumn("Models"),
-                    "Spaces": st.column_config.TextColumn("Spaces"),
-                    "Datasets": st.column_config.TextColumn("Datasets")
-                },
-                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"])
-            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"])
-            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:
-    # Find user's stats in the pre-calculated data
-    user_stat = next((stat for stat in user_stats if stat["username"] == username), None)
-    
-    # Create a header card with contributor info
-    header_col1, header_col2 = st.columns([1, 2])
-    with header_col1:
-        score_display = f"Score: {user_stat['score']:.1f}" if user_stat else "Score: N/A"
-        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 style="font-size: 14px; font-weight: bold;">{score_display}</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;">* Rankings are based on contribution scores combining repos and commit activity.</p>'
-                  f'</div>', unsafe_allow_html=True)
-
-    with st.spinner(f"Fetching detailed 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) 
+              f'</div>', unsafe_allow_html=True)