Hide empty download outputs

app.py

@@ -278,6 +278,10 @@ def _write_csv_download(name: str, headers: List[str], rows):
     return str(path)
 
 
+def _download_update(path: str):
+    return gr.update(value=path, visible=True)
+
+
 def _normalize_table_rows(rows) -> List[List[Any]]:
     if rows is None:
         return []
@@ -751,11 +755,11 @@ def merchant_score_filter(aspect: str, prediction: str, category: str) -> List[L
 
 
 def export_merchant_scores(rows):
-    return _write_csv_download(
+    return _download_update(_write_csv_download(
         "merchant_product_scores.csv",
         ["Product", "Category", "Metric", "Prediction", "Confidence", "Price", "Rating"],
         rows,
-    )
+    ))
 
 
 def negative_product_spotlight(limit: int = 6) -> str:
@@ -881,11 +885,11 @@ def import_new_product_payload(file_obj):
 
 
 def export_risk_rows(rows):
-    return _write_csv_download(
+    return _download_update(_write_csv_download(
         "new_product_metadata_risk.csv",
         ["Aspect", "Risk Level", "Model Signal", "Confidence", "Reason"],
         rows,
-    )
+    ))
 
 
 def _read_uploaded_records(file_obj) -> List[Dict[str, Any]]:
@@ -913,11 +917,11 @@ def download_new_product_template():
         35,
         "All",
     ]]
-    return _write_csv_download(
+    return _download_update(_write_csv_download(
         "new_product_metadata_template.csv",
         ["features", "category", "price", "average_rating", "rating_number", "focus_aspect"],
         rows,
-    )
+    ))
 
 
 def batch_screen_new_products(file_obj) -> Tuple[str, List[List[Any]]]:
@@ -976,11 +980,11 @@ def import_external_review_payload(file_obj):
 
 
 def export_external_rows(rows):
-    return _write_csv_download(
+    return _download_update(_write_csv_download(
         "external_review_prediction.csv",
         ["Aspect", "Prediction", "Confidence", "Key review evidence"],
         rows,
-    )
+    ))
 
 
 def download_external_review_template():
@@ -993,11 +997,11 @@ def download_external_review_template():
         312,
         "All",
     ]]
-    return _write_csv_download(
+    return _download_update(_write_csv_download(
         "external_review_template.csv",
         ["review", "features", "category", "price", "average_rating", "rating_number", "highlight_aspect"],
         rows,
-    )
+    ))
 
 
 def batch_external_review_predict(file_obj) -> Tuple[str, str, List[List[Any]]]:
@@ -1167,7 +1171,7 @@ def build_app() -> gr.Blocks:
                     with gr.Row():
                         refresh_scores = gr.Button("Apply Score Filter", variant="primary")
                         export_scores = gr.Button("Export Score List")
-                    merchant_scores_file = gr.File(label="Downloaded score CSV", interactive=False)
+                    merchant_scores_file = gr.File(label="Downloaded score CSV", interactive=False, visible=False)
                 with gr.Accordion("New Product Metadata Risk Screening", open=False):
                     risk_mode = gr.Radio(["Single product analysis", "Batch import analysis"], value="Single product analysis", label="Analysis mode")
                     with gr.Row():
@@ -1187,12 +1191,12 @@ def build_app() -> gr.Blocks:
                                 with gr.Row():
                                     new_template = gr.Button("Download Import Template")
                                     batch_screen_btn = gr.Button("Batch Analyze Metadata", variant="primary")
-                                new_template_file = gr.File(label="Template CSV", interactive=False)
+                                new_template_file = gr.File(label="Template CSV", interactive=False, visible=False)
                         with gr.Column(scale=1):
                             risk_summary = gr.HTML()
                             risk_table = gr.Dataframe(headers=["Aspect", "Risk Level", "Model Signal", "Confidence", "Reason"], value=[["Click Predict Metadata Risk", "", "", 0.0, ""]], datatype=["str", "str", "str", "number", "str"], interactive=False)
                             export_risk = gr.Button("Export Risk Result")
-                            risk_file = gr.File(label="Downloaded risk CSV", interactive=False)
+                            risk_file = gr.File(label="Downloaded risk CSV", interactive=False, visible=False)
                 with gr.Accordion("External Review Prediction", open=False):
                     ext_mode = gr.Radio(["Single product analysis", "Batch import analysis"], value="Single product analysis", label="Analysis mode")
                     with gr.Row():
@@ -1214,13 +1218,13 @@ def build_app() -> gr.Blocks:
                                 with gr.Row():
                                     ext_template = gr.Button("Download Import Template")
                                     batch_external_btn = gr.Button("Batch Analyze Reviews", variant="primary")
-                                ext_template_file = gr.File(label="Template CSV", interactive=False)
+                                ext_template_file = gr.File(label="Template CSV", interactive=False, visible=False)
                         with gr.Column(scale=1):
                             ext_overall = gr.HTML()
                             ext_aspects = gr.HTML()
                             ext_table = gr.Dataframe(headers=["Aspect", "Prediction", "Confidence", "Key review evidence"], value=[["Click Analyze External Review", "", 0.0, ""]], datatype=["str", "str", "number", "str"], interactive=False)
                             export_ext = gr.Button("Export Review Result")
-                            ext_file = gr.File(label="Downloaded review CSV", interactive=False)
+                            ext_file = gr.File(label="Downloaded review CSV", interactive=False, visible=False)
                 refresh_scores.click(merchant_score_filter, [merchant_metric, merchant_prediction, merchant_category], merchant_scores)
                 export_scores.click(export_merchant_scores, merchant_scores, merchant_scores_file)
                 merchant_metric.change(merchant_score_filter, [merchant_metric, merchant_prediction, merchant_category], merchant_scores)