import gradio as gr
from utils import SafeProgress, format_categories_html
from embeddings import create_product_embeddings
from similarity import compute_similarities

# Global variable for embeddings
embeddings = {}

def categorize_products_from_text(product_text, top_n=5, confidence_threshold=0.5, progress=None):
    """Categorize products from text input (one product per line)"""
    # Create a safe progress tracker
    progress_tracker = SafeProgress(progress)
    progress_tracker(0, desc="Starting...")
    
    # Parse input text to get product names
    product_names = [line.strip() for line in product_text.split("\n") if line.strip()]
    
    if not product_names:
        return "No product names provided."
    
    # Create product embeddings
    progress_tracker(0.1, desc="Generating product embeddings...")
    products_embeddings = create_product_embeddings(product_names)
    
    # Compute similarities
    progress_tracker(0.6, desc="Computing similarities...")
    all_similarities = compute_similarities(embeddings, products_embeddings)
    
    # Format results
    progress_tracker(0.9, desc="Formatting results...")
    output_html = "<div style='font-family: Arial, sans-serif;'>"
    
    for product, similarities in all_similarities.items():
        # Filter by confidence threshold and take top N
        filtered_similarities = [(ingredient, score) for ingredient, score in similarities 
                                if score >= confidence_threshold]
        top_similarities = filtered_similarities[:top_n]
        
        output_html += format_categories_html(product, top_similarities)
        output_html += "<hr style='margin: 15px 0; border: 0; border-top: 1px solid #eee;'>"
    
    output_html += "</div>"
    
    if not all_similarities:
        output_html = "<div style='color: #d32f2f; font-weight: bold; padding: 20px;'>No results found. Please check your input or try different products.</div>"
    
    progress_tracker(1.0, desc="Done!")
    return output_html

def categorize_products_from_file(file, top_n=5, confidence_threshold=0.5, progress=None):
    """Categorize products from a JSON or text file"""
    from utils import parse_product_file
    
    # Create a safe progress tracker
    progress_tracker = SafeProgress(progress)
    progress_tracker(0.1, desc="Reading file...")
    
    try:
        product_names = parse_product_file(file.name)
    except Exception as e:
        return f"<div style='color: #d32f2f; font-weight: bold;'>Error: {str(e)}</div>"
    
    if not product_names:
        return "<div style='color: #d32f2f;'>No product names found in the file.</div>"
    
    # Create product embeddings
    progress_tracker(0.2, desc="Generating product embeddings...")
    products_embeddings = create_product_embeddings(product_names)
    
    # Compute similarities
    progress_tracker(0.7, desc="Computing similarities...")
    all_similarities = compute_similarities(embeddings, products_embeddings)
    
    # Format results
    progress_tracker(0.9, desc="Formatting results...")
    output_html = f"<div style='font-family: Arial, sans-serif;'>"
    output_html += f"<div style='margin-bottom: 20px; padding: 10px; background-color: #e8f5e9; border-radius: 5px;'>"
    output_html += f"Found <b>{len(product_names)}</b> products in file. Showing results with confidence ≥ {confidence_threshold}."
    output_html += "</div>"
    
    for product, similarities in all_similarities.items():
        # Filter by confidence threshold and take top N
        filtered_similarities = [(ingredient, score) for ingredient, score in similarities 
                                if score >= confidence_threshold]
        top_similarities = filtered_similarities[:top_n]
        
        output_html += format_categories_html(product, top_similarities)
        output_html += "<hr style='margin: 15px 0; border: 0; border-top: 1px solid #eee;'>"
    
    output_html += "</div>"
    
    progress_tracker(1.0, desc="Done!")
    return output_html

def create_demo():
    """Create the Gradio interface"""
    # Basic CSS theme
    css = """
    .container {
        max-width: 1200px; 
        margin: auto; 
        padding: 0;
    }
    footer {display: none !important;}
    .header {
        background-color: #0d47a1; 
        padding: 15px 20px; 
        border-radius: 10px; 
        color: white; 
        margin-bottom: 20px;
        display: flex;
        align-items: center;
    }
    .header svg {
        margin-right: 10px;
        height: 30px;
        width: 30px;
    }
    .header h1 {
        margin: 0;
        font-size: 24px;
    }
    .description {
        margin-bottom: 20px; 
        padding: 15px; 
        background-color: #f5f5f5; 
        border-radius: 5px;
    }
    """

    # Custom theme
    theme = gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="indigo",
        font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui"]
    ).set(
        button_primary_background_fill="*primary_500",
        button_primary_background_fill_hover="*primary_600",
        button_secondary_background_fill="*neutral_200",
        block_title_text_size="lg",
        block_label_text_size="md"
    )

    with gr.Blocks(css=css, theme=theme) as demo:
        # Header with icon
        gr.HTML("""
        <div class="header">
            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="white">
                <path d="M12 2L2 7l10 5 10-5-10-5zM2 17l10 5 10-5M2 12l10 5 10-5"></path>
            </svg>
            <h1>Product Categorization Tool</h1>
        </div>
        <div class="description">
            This tool analyzes products and finds the most similar ingredients using AI embeddings.
            Just enter product names or upload a file to get started.
        </div>
        """)
        
        with gr.Tabs():
            with gr.TabItem("Text Input"):
                with gr.Row():
                    with gr.Column(scale=2):
                        example_products = [
                            "Tomato Sauce\nApple Pie\nGreek Yogurt\nChocolate Chip Cookies",
                            "Banana Bread\nOrange Juice\nGrilled Chicken\nCaesar Salad",
                            "Vanilla Ice Cream\nPizza Dough\nStrawberry Jam\nGrilled Salmon"
                        ]
                        
                        text_input = gr.Textbox(
                            lines=10, 
                            placeholder="Enter product names, one per line", 
                            label="Product Names"
                        )
                        
                        gr.Examples(
                            examples=example_products,
                            inputs=text_input,
                            label="Example Product Sets"
                        )
                        
                        with gr.Row():
                            with gr.Column(scale=1):
                                top_n = gr.Slider(
                                    minimum=1, 
                                    maximum=10, 
                                    value=5, 
                                    step=1, 
                                    label="Number of Top Categories"
                                )
                            with gr.Column(scale=1):
                                confidence = gr.Slider(
                                    minimum=0.1, 
                                    maximum=0.9, 
                                    value=0.5, 
                                    step=0.05, 
                                    label="Confidence Threshold"
                                )
                        
                        submit_button = gr.Button("Categorize Products", variant="primary")
                    
                    with gr.Column(scale=3):
                        text_output = gr.HTML(label="Categorization Results", 
                                             value="<div style='height: 450px; display: flex; justify-content: center; align-items: center; color: #666;'>Results will appear here</div>")
                
                submit_button.click(
                    fn=categorize_products_from_text,
                    inputs=[text_input, top_n, confidence],
                    outputs=text_output
                )
            
            with gr.TabItem("File Upload"):
                with gr.Row():
                    with gr.Column(scale=2):
                        file_input = gr.File(
                            label="Upload JSON or text file with products",
                            file_types=[".json", ".txt"]
                        )
                        
                        with gr.Accordion("Help", open=False):
                            gr.Markdown("""
                            - JSON files should contain either:
                              - A list of objects with a 'name' field for each product
                              - A simple array of product name strings
                            - Text files should have one product name per line
                            """)
                        
                        with gr.Row():
                            with gr.Column(scale=1):
                                file_top_n = gr.Slider(
                                    minimum=1, 
                                    maximum=10, 
                                    value=5, 
                                    step=1, 
                                    label="Number of Top Categories"
                                )
                            with gr.Column(scale=1):
                                file_confidence = gr.Slider(
                                    minimum=0.1, 
                                    maximum=0.9, 
                                    value=0.5, 
                                    step=0.05, 
                                    label="Confidence Threshold"
                                )
                        
                        file_button = gr.Button("Process File", variant="primary")
                    
                    with gr.Column(scale=3):
                        file_output = gr.HTML(
                            label="Categorization Results",
                            value="<div style='height: 450px; display: flex; justify-content: center; align-items: center; color: #666;'>Upload a file to see results</div>"
                        )
                
                file_button.click(
                    fn=categorize_products_from_file,
                    inputs=[file_input, file_top_n, file_confidence],
                    outputs=file_output
                )
        
        # Footer
        gr.HTML("""
        <div style="margin-top: 20px; text-align: center; color: #666;">
            Powered by Voyage AI embeddings • Built with Gradio
        </div>
        """)
    
    return demo