compare to chicory-ui

ui.py

@@ -2,274 +2,80 @@ import gradio as gr
 from utils import SafeProgress, format_categories_html
 from embeddings import create_product_embeddings
 from similarity import compute_similarities
-from chicory_api import call_chicory_parser  # Import the Chicory API function
+from chicory_api import call_chicory_parser
 
 # 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
+def categorize_products(product_input, is_file=False, top_n=5, confidence_threshold=0.5, progress=None):
+    """Categorize products from text input or file"""
     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()]
-    
+
+    # Parse input
+    if is_file:
+        from utils import parse_product_file
+        try:
+            product_names = parse_product_file(product_input.name)
+        except Exception as e:
+            return f"<div style='color: #d32f2f; font-weight: bold;'>Error: {str(e)}</div>"
+    else:
+        product_names = [line.strip() for line in product_input.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...")
+        return "<div style='color: #d32f2f;'>No product names provided.</div>"
+
+    # Create embeddings
+    progress_tracker(0.2, desc="Generating product embeddings...")
     products_embeddings = create_product_embeddings(product_names)
-    
+
     # Call Chicory Parser API
     progress_tracker(0.5, desc="Calling Chicory Parser API...")
     chicory_results = call_chicory_parser(product_names)
-    
+
     # Compute similarities
-    progress_tracker(0.6, desc="Computing 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 = "<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]
+        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, chicory_result=chicory_results.get(product))
         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)
-    
-    # Call Chicory Parser API
-    progress_tracker(0.5, desc="Calling Chicory Parser API...")
-    chicory_results = call_chicory_parser(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, chicory_result=chicory_results.get(product))
-        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() as demo:
+        gr.Markdown("# Product Categorization Tool\nAnalyze products and find the most similar ingredients using AI embeddings.")
 
-    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],
+                text_input = gr.Textbox(lines=10, placeholder="Enter product names, one per line", label="Product Names")
+                text_output = gr.HTML(label="Categorization Results")
+                gr.Button("Categorize").click(
+                    fn=categorize_products,
+                    inputs=[text_input, gr.State(False), gr.Slider(1, 10, 5), gr.Slider(0.1, 0.9, 0.5)],
                     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],
+                file_input = gr.File(label="Upload JSON or text file with products", file_types=[".json", ".txt"])
+                file_output = gr.HTML(label="Categorization Results")
+                gr.Button("Process File").click(
+                    fn=categorize_products,
+                    inputs=[file_input, gr.State(True), gr.Slider(1, 10, 5), gr.Slider(0.1, 0.9, 0.5)],
                     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>
-        """)
-    
+
+        gr.Markdown("Powered by Voyage AI embeddings • Built with Gradio")
     return demo