Refine consumer filtering and evidence layout

app.py

@@ -276,6 +276,34 @@ def _product_names() -> List[str]:
     return [p["name"] for p in PRODUCTS]
 
 
+def _product_names_for_category(category: str) -> List[str]:
+    if category == "All":
+        return _product_names()
+    names = [p["name"] for p in PRODUCTS if p.get("category") == category]
+    return names or _product_names()
+
+
+def _tags_for_category(category: str) -> List[str]:
+    products = PRODUCTS if category == "All" else [p for p in PRODUCTS if p.get("category") == category]
+    return sorted({tag for p in products for tag in p.get("tags", [])})
+
+
+def update_product_choices(category: str):
+    names = _product_names_for_category(category)
+    return gr.update(choices=names, value=names[0] if names else None)
+
+
+def update_filter_tags(category: str):
+    return gr.update(choices=_tags_for_category(category), value=[])
+
+
+def consumer_category_view(category: str, selected_aspect: str):
+    names = _product_names_for_category(category)
+    product_name = names[0] if names else _product_names()[0]
+    detail, aspects, evidence, rows = consumer_product_view(product_name, selected_aspect)
+    return gr.update(choices=names, value=product_name), detail, aspects, evidence, rows
+
+
 def _get_product(name: str) -> Dict[str, Any]:
     return next((p for p in PRODUCTS if p["name"] == name), PRODUCTS[0])
 
@@ -762,27 +790,31 @@ def build_app() -> gr.Blocks:
             with gr.Tab("Consumer Interface"):
                 with gr.Row():
                     with gr.Column(scale=4):
+                        consumer_category = gr.Dropdown(categories, value="All", label="Choose category")
                         product_select = gr.Dropdown(_product_names(), value=_product_names()[0], label="Choose a product")
                         consumer_aspect = gr.Dropdown(["All"] + ASPECTS, value="All", label="Highlight aspect evidence")
                         product_detail = gr.HTML()
-                        evidence_html = gr.HTML()
                     with gr.Column(scale=6):
                         aspect_html = gr.HTML()
+                        evidence_html = gr.HTML()
                 consumer_table = gr.Dataframe(headers=["Aspect", "Prediction", "Confidence", "Key review evidence"], datatype=["str", "str", "number", "str"], label="Aspect-level result table", interactive=False)
                 with gr.Accordion("Product Finder filters", open=False):
                     gr.HTML('<div class="compact-note">Optional: filter products by aspect sentiment, category, tags, and minimum rating.</div>')
                     with gr.Row():
-                        filter_aspect = gr.Dropdown(["Overall"] + ASPECTS, value="Overall", label="Target metric")
-                        filter_sentiment = gr.Radio(["Any", "Positive", "Negative", "Not_Mentioned", "Neutral"], value="Any", label="Preferred prediction")
-                        filter_category = gr.Dropdown(categories, value="All", label="Category")
-                    with gr.Row():
-                        filter_tags = gr.CheckboxGroup(tags, label="Required metadata tags")
-                        min_rating = gr.Slider(3.0, 5.0, value=4.0, step=0.1, label="Minimum product rating")
+                        with gr.Column(scale=1):
+                            filter_aspect = gr.Dropdown(["Overall"] + ASPECTS, value="Overall", label="Target metric")
+                            filter_sentiment = gr.Radio(["Any", "Positive", "Negative", "Not_Mentioned", "Neutral"], value="Any", label="Preferred prediction")
+                            filter_category = gr.Dropdown(categories, value="All", label="Category")
+                        with gr.Column(scale=1):
+                            filter_tags = gr.CheckboxGroup(tags, label="Required metadata tags")
+                            min_rating = gr.Slider(3.0, 5.0, value=4.0, step=0.1, label="Minimum product rating")
                     filter_btn = gr.Button("Filter Products", variant="primary")
                     filter_summary = gr.HTML()
                     filter_table = gr.Dataframe(headers=["Product", "Category", "Prediction", "Confidence", "Rating", "Price", "Metadata"], datatype=["str", "str", "str", "number", "number", "number", "str"], interactive=False, label="Filtered product candidates")
+                consumer_category.change(consumer_category_view, [consumer_category, consumer_aspect], [product_select, product_detail, aspect_html, evidence_html, consumer_table])
                 product_select.change(consumer_product_view, [product_select, consumer_aspect], [product_detail, aspect_html, evidence_html, consumer_table])
                 consumer_aspect.change(consumer_product_view, [product_select, consumer_aspect], [product_detail, aspect_html, evidence_html, consumer_table])
+                filter_category.change(update_filter_tags, filter_category, filter_tags)
                 filter_btn.click(filter_products, [filter_aspect, filter_sentiment, filter_category, filter_tags, min_rating], [filter_table, filter_summary])
 
             with gr.Tab("Merchant Interface"):