import gradio as gr
import pandas as pd
import plotly.express as px
from datetime import datetime, timedelta
import requests
from io import BytesIO


def create_trend_chart(space_id, daily_ranks_df):
    """라인 차트 생성 함수"""
    if space_id is None or daily_ranks_df.empty:
        return None
    
    try:
        space_data = daily_ranks_df[daily_ranks_df['id'] == space_id].copy()
        if space_data.empty:
            return None
        
        space_data = space_data.sort_values('date')
        
        fig = px.line(
            space_data,
            x='date',
            y='rank',
            title=f'Daily Rank Trend for {space_id}',
            labels={'date': 'Date', 'rank': 'Rank'},
            markers=True,
            height=500
        )
        
        fig.update_layout(
            xaxis_title="Date",
            yaxis_title="Rank",
            yaxis=dict(
                range=[100, 1],
                tickmode='linear',
                tick0=1,
                dtick=10
            ),
            hovermode='x unified',
            plot_bgcolor='white',
            paper_bgcolor='white',
            showlegend=False,
            margin=dict(t=50, r=20, b=40, l=40)
        )
        
        fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray')
        fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray')
        
        fig.update_traces(
            line_color='#2563eb',
            line_width=2,
            marker=dict(size=8, color='#2563eb')
        )
        
        return fig
    except Exception as e:
        print(f"Error creating chart: {e}")
        return None


def get_duplicate_spaces(top_100_spaces):
    """
    동일 username(또는 organization) 아래 여러 스페이스가 올라온 경우
    각각의 trendingScore를 합산한 뒤 상위 20명을 뽑는 함수
    """
    # username/spacename -> username만 추출
    top_100_spaces['clean_id'] = top_100_spaces['id'].apply(lambda x: x.split('/')[0])
    
    # clean_id별 트렌딩 스코어 합산
    score_sums = top_100_spaces.groupby('clean_id')['trendingScore'].sum()
    
    # 디버깅용 출력
    print("\n=== ID별 스코어 합산 결과 (상위 20) ===")
    for cid, score in score_sums.sort_values(ascending=False).head(20).items():
        print(f"Clean ID: {cid}, Total Score: {score}")
    
    # 상위 20개 추출
    top_20_scores = score_sums.sort_values(ascending=False).head(20)
    return top_20_scores


def create_duplicates_chart(score_sums):
    """중복(또는 여러 Spaces 운영) 사용자에 대한 합산 차트 생성"""
    if score_sums.empty:
        return None
    
    # 시각화를 위한 DataFrame 생성
    df = pd.DataFrame({
        'id': score_sums.index,
        'total_score': score_sums.values,
        'rank': range(1, len(score_sums) + 1)
    })
    
    # 디버깅용 출력
    print("\n=== 차트 데이터 (clean_id 단위) ===")
    print(df)
    
    fig = px.bar(
        df,
        x='id',
        y='rank',
        title="Top 20 Spaces by Combined Trending Score",
        height=500,
        text='total_score'
    )
    
    fig.update_layout(
        showlegend=False,
        margin=dict(t=50, r=20, b=40, l=40),
        plot_bgcolor='white',
        paper_bgcolor='white',
        xaxis_tickangle=-45,
        yaxis=dict(
            range=[len(df) + 0.5, 0.5],
            tickmode='linear',
            tick0=1,
            dtick=1
        )
    )
    
    fig.update_traces(
        marker_color='#4CAF50',
        texttemplate='%{text:.1f}',
        textposition='outside',
        hovertemplate='ID: %{x}<br>Rank: %{y}<br>Total Score: %{text:.1f}<extra></extra>'
    )
    
    fig.update_xaxes(
        title_text="User ID",
        showgrid=True,
        gridwidth=1,
        gridcolor='lightgray'
    )
    
    fig.update_yaxes(
        title_text="Rank",
        showgrid=True,
        gridwidth=1,
        gridcolor='lightgray'
    )
    
    return fig


def update_display(selection):
    """사용자가 Space를 선택했을 때, 상세 정보와 트렌드 차트를 업데이트"""
    global daily_ranks_df
    
    if not selection:
        return None, gr.HTML(value="<div style='text-align: center; padding: 20px; color: #666;'>Select a space to view details</div>")
    
    try:
        space_id = selection
        
        latest_data = daily_ranks_df[daily_ranks_df['id'] == space_id].sort_values('date').iloc[-1]
        
        info_text = f"""
        <div style="padding: 16px; background-color: white; border-radius: 8px; box-shadow: 0 1px 3px rgba(0,0,0,0.1);">
            <h3 style="margin: 0 0 12px 0;">Space Details</h3>
            <p style="margin: 4px 0;"><strong>ID:</strong> {space_id}</p>
            <p style="margin: 4px 0;"><strong>Current Rank:</strong> {int(latest_data['rank'])}</p>
            <p style="margin: 4px 0;"><strong>Trending Score:</strong> {latest_data['trendingScore']:.2f}</p>
            <p style="margin: 4px 0;"><strong>Created At:</strong> {latest_data['createdAt'].strftime('%Y-%m-%d')}</p>
            <p style="margin: 12px 0 0 0;">
                <a href="https://huggingface.co/spaces/{space_id}" 
                   target="_blank" 
                   style="color: #2563eb; text-decoration: none;">
                    View Space ↗
                </a>
            </p>
        </div>
        """
        
        chart = create_trend_chart(space_id, daily_ranks_df)
        return chart, gr.HTML(value=info_text)
        
    except Exception as e:
        print(f"Error in update_display: {e}")
        return None, gr.HTML(value=f"<div style='color: red;'>Error processing data: {str(e)}</div>")


def load_and_process_data():
    """
    - Parquet 파일 로드 후 30일 이내 데이터만 필터링
    - (선택) createdAt + id 기준 중복 제거
    - 날짜별 ranking 산정 -> daily_ranks_df 구성
    - 최신 날짜 기준 top 100 추출 후 id 중복 제거
    """
    try:
        url = "https://huggingface.co/datasets/cfahlgren1/hub-stats/resolve/main/spaces.parquet"
        response = requests.get(url)
        df = pd.read_parquet(BytesIO(response.content))
        
        # 30일 전 시점 계산
        thirty_days_ago = datetime.now() - timedelta(days=30)
        df['createdAt'] = pd.to_datetime(df['createdAt'])
        
        # 30일 내에 생성된 기록만 필터링
        df = df[df['createdAt'] >= thirty_days_ago].copy()
        
        # (선택) createdAt & id 기준 중복 제거
        df = (
            df
            .sort_values(['createdAt', 'trendingScore'], ascending=[True, False])
            .drop_duplicates(subset=['createdAt', 'id'], keep='first')
            .reset_index(drop=True)
        )
        
        # 날짜 범위 생성
        dates = pd.date_range(start=thirty_days_ago, end=datetime.now(), freq='D')
        daily_ranks = []
        
        # 날짜별로 rank 계산
        for date in dates:
            date_data = df[df['createdAt'].dt.date <= date.date()].copy()
            date_data = date_data.sort_values(['trendingScore', 'id'], ascending=[False, True])
            date_data['rank'] = range(1, len(date_data) + 1)
            date_data['date'] = date.date()
            daily_ranks.append(date_data[['id', 'date', 'rank', 'trendingScore', 'createdAt']])
        
        # 일자별 랭킹 데이터를 합침
        daily_ranks_df = pd.concat(daily_ranks, ignore_index=True)
        
        # 최신 날짜 기준 Top 100 추출
        latest_date = daily_ranks_df['date'].max()
        top_100_spaces = daily_ranks_df[
            (daily_ranks_df['date'] == latest_date) &
            (daily_ranks_df['rank'] <= 100)
        ].sort_values('rank').copy()
        
        # id 기준 중복 제거
        top_100_spaces = top_100_spaces.drop_duplicates(subset=['id'], keep='first').reset_index(drop=True)
        
        return daily_ranks_df, top_100_spaces

    except Exception as e:
        print(f"Error loading data: {e}")
        return pd.DataFrame(), pd.DataFrame()


# 메인 실행
print("Loading initial data...")
daily_ranks_df, top_100_spaces = load_and_process_data()
print("Data loaded successfully!")

# 중복(여러 Space) 운영 사용자에 대한 데이터 계산
duplicates = get_duplicate_spaces(top_100_spaces)
duplicates_chart = create_duplicates_chart(duplicates)

# Gradio 앱 생성
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # HF Space Ranking Tracker (~30 Days)
    
    Track, analyze, and discover trending AI applications in the Hugging Face ecosystem. 
    Our service continuously monitors and ranks all Spaces over a 30-day period, 
    providing detailed analytics and daily ranking changes for the top 100 performers.
    """)
    
    with gr.Tabs():
        with gr.Tab("Dashboard"):
            with gr.Row(variant="panel"):
                with gr.Column(scale=5):
                    trend_plot = gr.Plot(
                        label="Daily Rank Trend",
                        container=True
                    )
                with gr.Column(scale=5):
                    duplicates_plot = gr.Plot(
                        label="Multiple Entries Analysis",
                        value=duplicates_chart,
                        container=True
                    )
            
            with gr.Row():
                info_box = gr.HTML(
                    value="<div style='text-align: center; padding: 20px; color: #666;'>Select a space to view details</div>"
                )
            
            # Radio 버튼은 숨김. 아래 카드 클릭으로 선택하도록 유도
            space_selection = gr.Radio(
                choices=[row['id'] for _, row in top_100_spaces.iterrows()],
                value=None,
                visible=False
            )
            
            # Top 100 스페이스를 카드 형태로 표시
            html_content = """
            <div style='display: flex; flex-wrap: wrap; gap: 16px; justify-content: center;'>
            """ + "".join([
                f"""
                <div class="space-card" 
                     data-space-id="{row['id']}"
                     style="
                    border: 1px solid #e5e7eb;
                    border-radius: 8px;
                    padding: 16px;
                    margin: 8px;
                    background-color: hsl(210, {max(30, 90 - (row['rank'] / 100 * 60))}%, {min(97, 85 + (row['rank'] / 100 * 10))}%);
                    box-shadow: 0 1px 3px rgba(0,0,0,0.1);
                    display: inline-block;
                    width: 250px;
                    vertical-align: top;
                    cursor: pointer;
                    transition: all 0.2s;
                "
                onmouseover="this.style.transform='translateY(-2px)';this.style.boxShadow='0 4px 6px rgba(0,0,0,0.1)';"
                onmouseout="this.style.transform='none';this.style.boxShadow='0 1px 3px rgba(0,0,0,0.1)';"
                >
                    <div style="font-size: 1.2em; font-weight: bold; margin-bottom: 8px;">
                        #{int(row['rank'])}
                    </div>
                    <div style="margin-bottom: 8px;">
                        {row['id']}
                    </div>
                    <div style="color: #666; margin-bottom: 12px;">
                        Score: {row['trendingScore']:.2f}
                    </div>
                    <div style="display: flex; gap: 8px;">
                        <a href="https://huggingface.co/spaces/{row['id']}" 
                           target="_blank" 
                           style="padding: 6px 12px; background-color: white; color: #2563eb; text-decoration: none; border-radius: 4px; font-size: 0.9em; border: 1px solid #2563eb;"
                           onclick="event.stopPropagation();">
                            View Space ↗
                        </a>
                        <button onclick="event.preventDefault(); gradioEvent('{row['id']}');"
                                style="padding: 6px 12px; background-color: #2563eb; color: white; border: none; border-radius: 4px; cursor: pointer; font-size: 0.9em;">
                            View Trend
                        </button>
                    </div>
                </div>
                """
                for _, row in top_100_spaces.iterrows()
            ]) + """
            </div>
            <script>
            function gradioEvent(spaceId) {
                // Space 카드 내 "View Trend" 버튼 클릭 시, 대응되는 Radio 항목 선택 이벤트 발생
                const radio = document.querySelector(`input[type="radio"][value="${spaceId}"]`);
                if (radio) {
                    radio.checked = true;
                    const event = new Event('change');
                    radio.dispatchEvent(event);
                }
            }
            </script>
            """
            
            with gr.Row():
                space_grid = gr.HTML(value=html_content)
        
        with gr.Tab("About"):
            gr.Markdown("""
            ### Our Tracking System
            
            **What We Track**
            - Daily ranking changes for all Hugging Face Spaces
            - Comprehensive trending scores based on 30-day activity
            - Detailed performance metrics for top 100 Spaces
            - Historical ranking data with daily granularity
            
            **Key Features**
            - **Real-time Rankings**: Stay updated with daily rank changes
            - **Interactive Visualizations**: Track ranking trajectories over time
            - **Trend Analysis**: Identify emerging popular AI applications
            - **Direct Access**: Quick links to explore trending Spaces
            - **Performance Metrics**: Detailed trending scores and statistics
            
            ### Why Use HF Space Ranking Tracker?
            - Discover trending AI demos and applications
            - Monitor your Space's performance and popularity
            - Identify emerging trends in the AI community
            - Make data-driven decisions about your AI projects
            - Stay ahead of the curve in AI application development
            
            Our dashboard provides a comprehensive view of the Hugging Face Spaces ecosystem,
            helping developers, researchers, and enthusiasts track and understand the dynamics of popular AI applications.
            
            Whether you're monitoring your own Space's performance or discovering new trending applications,
            HF Space Ranking Tracker offers the insights you need.
            """)
    
    # 사용자 선택시(=Radio.value 변경 시) update_display 호출
    space_selection.change(
        fn=update_display,
        inputs=[space_selection],
        outputs=[trend_plot, info_box],
        api_name="update_display"
    )

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