import os
import sys
import gradio as gr
from utils import load_embeddings
from embeddings import create_product_embeddings
from similarity import compute_similarities
from ui import categorize_products_from_text, categorize_products_from_file

# Path to the embeddings file
EMBEDDINGS_PATH = "ingredient_embeddings_voyageai.pkl"

# Check if embeddings file exists
if not os.path.exists(EMBEDDINGS_PATH):
    print(f"Error: Embeddings file {EMBEDDINGS_PATH} not found!")
    print(f"Please ensure the file exists at {os.path.abspath(EMBEDDINGS_PATH)}")
    sys.exit(1)

# Load embeddings globally
try:
    embeddings_data = load_embeddings(EMBEDDINGS_PATH)
    # Make embeddings available to the UI functions
    import ui
    ui.embeddings = embeddings_data
except Exception as e:
    print(f"Error loading embeddings: {e}")
    sys.exit(1)

# 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"
)

# Create the Gradio interface directly in this file for Spaces
with gr.Blocks(css=css, theme=theme) as app:
    # 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"
                            )
                    
                    compare_chicory = gr.Checkbox(
                        label="Compare with Chicory Parser V3",
                        value=False,
                        info="When enabled, results will include comparisons with Chicory Parser V3 API"
                    )
                    
                    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>
    """)

# For Hugging Face Spaces compatibility - do not remove
if __name__ == "__main__":
    app.launch()