app.py

import gradio as gr
import pandas as pd
from huggingface_hub import HfApi
from collections import defaultdict

# ------------------------------------------------------
# Get spaces with more details
api = HfApi()
spaces = api.list_spaces(limit=60000)  # Limiting to 60000 for now

# Create a DataFrame
data = []
for space in spaces:
    data.append({
        'id': space.id,
        'title': space.id.split('/')[-1],
        'author': space.author if space.author else space.id.split('/')[0],
        'likes': space.likes,
        'tags': space.tags if hasattr(space, 'tags') else [],
    })

df = pd.DataFrame(data)
print("Total spaces collected:", len(df))
print("\nSample of the data:")
print(df.head())

# ------------------------------------------------------

# Define categories and their keywords
categories = {
    'Text-to-Speech': ['tts', 'speech', 'voice', 'audio', 'kokoro'],
    'Transcription': ['transcribe', 'transcription'],
    'Agents': ['agent', 'agents', 'smol', 'multi-step', 'autobot', 'autoGPT' 'agentic'],
    'Image Gen/Editing': ['stable-diffusion', 'diffusion', 'flux', 'dalle', 'CLIP', 
                         'comic', 'gan', 'sdxl', 'pic', 'img', 'stable', 'midjourney', 
                         'diffusion', 'image', 'ControlNet', 'Control Net', 'dreambooth', 'blip', 'LoRA', 'img2img', 'style', 'art'],
    'Video': ['video', 'animation', 'motion', 'sora'],
    'Face/Portrait': ['face', 'portrait', 'gaze', 'facial'],
    'Chat/LLM': ['chat', 'llm', 'gpt', 'llama', 'text', 'language'],
    '3D': ['3d', 'mesh', 'point-cloud', 'depth'],
    'Audio': ['audio', 'tts', 'music', 'whisper', 'sound', 'voice'],
    'Vision': ['vision', 'detection', 'recognition', 'classifier'],
    'CLIP': ['image-to-text', 'describe-image'],
    'Games': ['game', 'games', 'play', 'playground'],
    'Finance': ['finance', 'stock', 'money', 'currency', 'bank', 'market'],
    'SAM': ['sam', 'segmentation', 'mask'],
    'Science': ['science', 'physics', 'chemistry', 'biology', 'math', 'astronomy', 'geology', 'meteorology', 'engineering', 'medicine', 'health', 'nutrition', 'environment', 'ecology', 'geography', 'geology', 'geophysics'],
    'Education': ['education', 'school', 'university', 'college', 'teaching', 'learning', 'study', 'research'],
    'Graph': ['graph', 'network', 'node', 'edge', 'path', 'tree', 'cycle', 'flow', 'matching', 'coloring', 'swarm'],
    'Research': ['research', 'study', 'experiment', 'paper', 'discovery', 'innovation', 'exploration', 'analysis'],
    'Document Analyis': ['pdf', 'RAG', 'idefecs'],
    'WebGPU': ['localModel', 'webGPU'],
    'Point Tracking': ['CoTracker', 'tapir', 'tapnet', 'point', 'track'],
    'Games': ['game', 'Unity', 'UE5', 'Unreal'],
    'Leaderboard': ['arena', 'leaderboard', 'timeline'],
    'Other': []  # Default category
}

def categorize_space(title, tags):
    title_lower = title.lower()
    # Convert tags to lowercase if tags exist
    tags_lower = [t.lower() for t in tags] if tags else []
    
    for category, keywords in categories.items():
        # Check both title and tags for keywords
        if any(keyword in title_lower for keyword in keywords) or \
           any(keyword in tag for keyword in keywords for tag in tags_lower):
            return category
    return 'Other'

# Add category to DataFrame
df['category'] = df.apply(lambda x: categorize_space(x['title'], x['tags']), axis=1)

# Show category distribution
category_counts = df['category'].value_counts()
print("\nCategory Distribution:")
print(category_counts)

# Show sample spaces from each category
print("\nSample spaces from each category:")
for category in categories.keys():
    print(f"\n{category}:")
    sample = df[df['category'] == category].head(3)
    print(sample[['title', 'likes']].to_string())
    
# ------------------------------------------------------
# Add total likes per category
category_likes = df.groupby('category')['likes'].sum().sort_values(ascending=False)
print("Total likes per category:")
print(category_likes)

print("\nTop 10 spaces in each category (sorted by likes):")
for category in categories.keys():
    print(f"\n=== {category} ===")
    top_10 = df[df['category'] == category].nlargest(10, 'likes')[['title', 'likes']]
    # Format output with padding for better readability
    print(top_10.to_string(index=False))

# ------------------------------------------------------

# Add space URLs
df['url'] = 'https://huggingface.co/spaces/' + df['id']

# Show the top 10 spaces from each category with their links
# print("Top 10 spaces in each category with links:")
# for category in categories.keys():
    # print(f"\n=== {category} ===")
    # top_10 = df[df['category'] == category].nlargest(10, 'likes')[['title', 'likes', 'url']]
    # Format output with padding for better readability
    # print(top_5.to_string(index=False))

# ------------------------------------------------------

def search_spaces(search_text, category):
    if category == "All Categories":
        spaces_df = df
    else:
        spaces_df = df[df['category'] == category]
    
    if search_text:
        spaces_df = spaces_df[spaces_df['title'].str.lower().str.contains(search_text.lower())]
    
    spaces = spaces_df.nlargest(100, 'likes')[['title', 'likes', 'url', 'category']]
    
    # Get category stats
    total_spaces = len(spaces_df)
    total_likes = spaces_df['likes'].sum()
    
    # Format the results as HTML with clickable links and stats
    html_content = f"""
    <div style='margin-bottom: 20px; padding: 10px; background-color: var(--color-background-primary); 
                border: 1px solid var(--color-border-primary); border-radius: 5px;'>
        <h3 style='color: var(--color-text-primary);'>Statistics:</h3>
        <p style='color: var(--color-text-primary);'>Total Spaces: {total_spaces}</p>
        <p style='color: var(--color-text-primary);'>Total Likes: {total_likes:,}</p>
    </div>
    <div style='max-height: 800px; overflow-y: auto;'>
        <div style='display: grid; grid-template-columns: repeat(3, 1fr); gap: 15px; padding: 10px;'>
    """
    
    for _, row in spaces.iterrows():
        # In the search_spaces function, update the card HTML template:
        html_content += f"""
        <div style='padding: 15px; 
                    border: 2px solid var(--color-border-primary); 
                    border-radius: 5px; 
                    background-color: var(--color-background-primary);
                    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
                    display: flex;
                    flex-direction: column;
                    height: 100%;
                    position: relative;'>
            <h3 style='margin-top: 0; margin-bottom: 10px;'>
                <a href='{row['url']}' target='_blank' 
                   style='color: #2196F3; 
                          text-decoration: none; 
                          font-weight: bold;'>{row['title']}</a>
            </h3>
            <div style='height: 2px; 
                        background: var(--color-border-primary);
                        margin: 10px 0;
                        width: 100%;'></div>
            <p style='color: var(--color-text-primary); margin: 8px 0;'>
                <span style='background-color: var(--color-accent-soft); 
                           padding: 2px 8px; 
                           border-radius: 12px;
                           font-size: 0.9em;'>
                    {row['category']}
                </span>
            </p>
            <p style='color: var(--color-text-primary); 
                      margin-top: auto; 
                      padding-top: 10px;
                      border-top: 1px solid var(--color-border-primary);'>
                ❤️ {row['likes']:,} likes
            </p>
        </div>
        """
    
    html_content += "</div></div>"
    return html_content

# Create the Gradio interface
def create_app():
    with gr.Blocks(
        title="Hugging Face Spaces Explorer",
        theme=gr.themes.Soft(
            primary_hue="blue",
            secondary_hue="blue",
        )
    ) as app:
        gr.Markdown("""
        # 🤗 Hugging Face Spaces Explorer
        Explore and discover popular Hugging Face Spaces by category. 
        Any currently uncategorized spaces will be listed under "Other" or "All Categories", 
        if you would like to help make Spaces easier to search and filter through feel free 
        to add on to my project or recommend additional filters!
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                # Category selection
                category_dropdown = gr.Dropdown(
                    choices=["All Categories"] + sorted(df['category'].unique()),
                    label="Select Category",
                    value="All Categories"
                )
                # Search box
                search_input = gr.Textbox(
                    label="Search Spaces",
                    placeholder="Enter search terms..."
                )
            
        # Display area for spaces
        spaces_display = gr.HTML(value=search_spaces("", "All Categories"))
        
        # Update display when category or search changes
        category_dropdown.change(
            fn=search_spaces,
            inputs=[search_input, category_dropdown],
            outputs=spaces_display
        )
        search_input.change(
            fn=search_spaces,
            inputs=[search_input, category_dropdown],
            outputs=spaces_display
        )
    
    return app

# Launch the app
app = create_app()
app.launch(share=True)