compare to chicory-ui

app.py

@@ -2,9 +2,7 @@ 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
+from ui import categorize_products, create_demo  # Updated imports
 
 # Path to the embeddings file
 EMBEDDINGS_PATH = "ingredient_embeddings_voyageai.pkl"
@@ -25,182 +23,7 @@ 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
+# Launch the Gradio interface
 if __name__ == "__main__":
-    app.launch()
+    demo = create_demo()
+    demo.launch()