"""
Main Gradio application for EDA-Generator HuggingFace Space.
Features a chatbot interface and dataset EDA visualizations.
"""

import gradio as gr
from chatbot import chat_stream_fn
from eda import (
    create_prompt_length_histogram,
    create_word_count_boxplot,
    create_top_words_chart,
    create_length_vs_words_scatter,
    create_category_distribution,
    create_summary_stats,
    load_prompts_dataset
)

# Custom CSS for premium aesthetics
CUSTOM_CSS = """
/* Dark theme with gradient accents */
.gradio-container {
    background: linear-gradient(135deg, #1a1a2e 0%, #16213e 50%, #0f3460 100%) !important;
}

/* Header styling */
.header-title {
    text-align: center;
    background: linear-gradient(90deg, #667eea, #764ba2, #f093fb);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    font-size: 2.5rem;
    font-weight: 800;
    margin-bottom: 0.5rem;
}

.header-subtitle {
    text-align: center;
    color: #a0aec0;
    font-size: 1.1rem;
    margin-bottom: 2rem;
}

/* Tab styling */
.tabs {
    border-radius: 12px !important;
    overflow: hidden;
}

/* Chat container */
.chatbot {
    border-radius: 16px !important;
    border: 1px solid rgba(102, 126, 234, 0.3) !important;
    background: rgba(26, 26, 46, 0.8) !important;
}

/* Plot containers */
.plot-container {
    border-radius: 12px;
    padding: 1rem;
    background: rgba(255, 255, 255, 0.03);
    border: 1px solid rgba(102, 126, 234, 0.2);
    margin: 0.5rem 0;
}

/* Stats table */
.dataframe {
    border-radius: 8px !important;
    overflow: hidden;
}

/* Buttons */
.primary-btn {
    background: linear-gradient(90deg, #667eea, #764ba2) !important;
    border: none !important;
    border-radius: 8px !important;
}

/* Micro-animations */
.tab-nav button {
    transition: all 0.3s ease !important;
}

.tab-nav button:hover {
    transform: translateY(-2px);
    box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4);
}
"""

def create_app() -> gr.Blocks:
    """
    Create the main Gradio application with tabs for chatbot and EDA.
    
    Returns:
        Gradio Blocks application
    """
    with gr.Blocks(
        css=CUSTOM_CSS,
        theme=gr.themes.Soft(
            primary_hue="purple",
            secondary_hue="indigo",
            neutral_hue="slate",
        ).set(
            body_background_fill="*neutral_950",
            block_background_fill="*neutral_900",
            block_border_width="1px",
            block_border_color="*primary_700",
            input_background_fill="*neutral_800",
            button_primary_background_fill="*primary_600",
        ),
        title="EDA-Generator | AI Chatbot & Dataset Analysis"
    ) as demo:
        
        # Header
        gr.HTML("""
            <div style="padding: 2rem 0;">
                <h1 class="header-title">🚀 EDA-Generator</h1>
                <p class="header-subtitle">
                    AI-Powered Chatbot & Dataset Exploratory Analysis
                </p>
            </div>
        """)
        
        with gr.Tabs() as tabs:
            
            # ===== CHATBOT TAB =====
            with gr.TabItem("💬 AI Chatbot", id="chatbot"):
                gr.Markdown("""
                ### Chat with GPT-OSS-20B
                A powerful open-source language model for conversation, coding help, and more.
                """)
                
                chatbot_interface = gr.ChatInterface(
                    fn=chat_stream_fn,
                    chatbot=gr.Chatbot(
                        height=500,
                        placeholder="<strong>Ask me anything!</strong><br>I can help with coding, analysis, creative writing, and more.",
                        show_copy_button=True,
                    ),
                    textbox=gr.Textbox(
                        placeholder="Type your message here...",
                        container=False,
                        scale=7
                    ),
                    retry_btn="🔄 Retry",
                    undo_btn="↩️ Undo",
                    clear_btn="🗑️ Clear",
                    examples=[
                        "Explain machine learning in simple terms",
                        "Write a Python function to reverse a string",
                        "What are the best practices for data visualization?",
                        "Help me write a creative story opening"
                    ],
                )
            
            # ===== EDA TAB =====
            with gr.TabItem("📊 Dataset EDA", id="eda"):
                gr.Markdown("""
                ### Exploring `fka/awesome-chatgpt-prompts`
                Interactive visualizations of the popular ChatGPT prompts dataset.
                """)
                
                # Summary Statistics
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("#### 📋 Summary Statistics")
                        stats_df = create_summary_stats()
                        gr.Dataframe(
                            value=stats_df,
                            headers=["Metric", "Value"],
                            interactive=False,
                            wrap=True
                        )
                
                # Row 1: Distribution plots
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("#### Distribution of Prompt Lengths")
                        length_plot = gr.Plot(value=create_prompt_length_histogram())
                    
                    with gr.Column():
                        gr.Markdown("#### Word Count Distribution")
                        word_plot = gr.Plot(value=create_word_count_boxplot())
                
                # Row 2: Analysis plots
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("#### Top Words Analysis")
                        words_plot = gr.Plot(value=create_top_words_chart())
                    
                    with gr.Column():
                        gr.Markdown("#### Length vs Words Correlation")
                        scatter_plot = gr.Plot(value=create_length_vs_words_scatter())
                
                # Row 3: Category distribution
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("#### Category Distribution")
                        category_plot = gr.Plot(value=create_category_distribution())
                
                # Sample Data Preview
                with gr.Accordion("📄 Sample Data Preview", open=False):
                    df = load_prompts_dataset()
                    gr.Dataframe(
                        value=df[['act', 'prompt']].head(10),
                        headers=["Category (Act)", "Prompt"],
                        wrap=True,
                        max_height=300
                    )
            
            # ===== ABOUT TAB =====
            with gr.TabItem("ℹ️ About", id="about"):
                gr.Markdown("""
                ## About EDA-Generator
                
                This HuggingFace Space demonstrates:
                
                ### 🤖 AI Chatbot
                - Powered by **OpenAI's GPT-OSS-20B** model
                - 21 billion parameters with MoE architecture
                - Streaming responses for better UX
                - Built with HuggingFace Transformers
                
                ### 📊 Dataset Analysis
                - Analyzes the **fka/awesome-chatgpt-prompts** dataset
                - 1,040 curated prompts for various AI personas
                - Interactive Plotly visualizations
                
                ### 🛠️ Tech Stack
                - **Frontend**: Gradio
                - **ML**: HuggingFace Transformers
                - **Visualization**: Plotly, Matplotlib, Seaborn
                - **Data**: HuggingFace Datasets
                
                ---
                
                **Created with ❤️ using HuggingFace**
                """)
        
        # Footer
        gr.HTML("""
            <div style="text-align: center; padding: 2rem 0; color: #667eea; font-size: 0.9rem;">
                <p>🌟 Star this Space if you find it useful!</p>
            </div>
        """)
    
    return demo


# Create and launch the app
demo = create_app()

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