Add ZeroGPU inference decorators

app.py

@@ -31,6 +31,19 @@ except Exception:
 
 import gradio as gr
 
+try:
+    import spaces
+except Exception:
+    class _SpacesCompat:
+        def GPU(self, *args, **kwargs):
+            if args and callable(args[0]) and len(args) == 1 and not kwargs:
+                return args[0]
+            def decorator(fn):
+                return fn
+            return decorator
+    spaces = _SpacesCompat()
+
+
 from src import config as cfg
 from src.inference import AspectPredictor
 
@@ -248,6 +261,7 @@ def _aspect_rows(result: Dict[str, Any], review: str) -> List[List[Any]]:
     return rows
 
 
+@spaces.GPU(duration=120)
 def consumer_product_view(product_name: str, selected_aspect: str) -> Tuple[str, str, str, List[List[Any]]]:
     product = _get_product(product_name)
     try:
@@ -269,6 +283,7 @@ def consumer_product_view(product_name: str, selected_aspect: str) -> Tuple[str,
     return detail, _aspect_cards(result, product["review"], selected_aspect), evidence, _aspect_rows(result, product["review"])
 
 
+@spaces.GPU(duration=120)
 def filter_products(aspect: str, sentiment: str, category: str, tags: List[str], min_rating: float) -> Tuple[List[List[Any]], str]:
     rows = []
     best = None
@@ -351,6 +366,7 @@ def ablation_rows() -> List[List[Any]]:
     return [[labels.get(k, k), _num(ab.get(k, {}).get("mean_macro_f1")), _num(ab.get(k, {}).get("mean_accuracy"))] for k in labels if k in ab]
 
 
+@spaces.GPU(duration=120)
 def merchant_product_scores(metric: str) -> List[List[Any]]:
     rows = []
     monitored = _rank_products(PRODUCTS)[:MAX_MONITOR_PRODUCTS]
@@ -382,6 +398,7 @@ def _metadata_risks(features: str, categories: str, price: Any, rating: Any, cou
     return risks
 
 
+@spaces.GPU(duration=120)
 def screen_new_product(features: str, categories: str, price: Any, rating: Any, count: Any, focus: str) -> Tuple[str, List[List[Any]]]:
     review = "This is a new clothing item. Customers may comment on fit, fabric, quality, appearance, style, and value."
     try:
@@ -404,6 +421,7 @@ def screen_new_product(features: str, categories: str, price: Any, rating: Any,
     return f'<div class="note-card"><b>New product risk focus:</b> {_esc(summary)}<br><span class="muted">This is a metadata screening tool, not a replacement for real review evaluation.</span></div>', rows
 
 
+@spaces.GPU(duration=120)
 def external_review_predict(review: str, features: str, categories: str, price: Any, rating: Any, count: Any, selected_aspect: str) -> Tuple[str, str, List[List[Any]]]:
     try:
         result = _predict_custom(review, features, categories, price, rating, count)
@@ -605,8 +623,6 @@ def build_app() -> gr.Blocks:
                 refresh_research = gr.Button("Refresh Research Metrics", variant="primary")
                 refresh_research.click(lambda: (research_cards_html(), overall_metric_rows(), aspect_metric_rows(), ablation_rows()), outputs=[research_cards, overall_table, aspect_table, ablation_table])
         demo.load(consumer_product_view, [product_select, consumer_aspect], [product_detail, aspect_html, evidence_html, consumer_table])
-        demo.load(merchant_product_scores, merchant_metric, merchant_scores)
-        demo.load(filter_products, [filter_aspect, filter_sentiment, filter_category, filter_tags, min_rating], [filter_table, filter_summary])
     return demo
 
 demo = build_app()

requirements.txt

@@ -4,4 +4,5 @@ transformers>=4.35.0,<5.0.0
 huggingface-hub>=0.30.0
 numpy>=1.24.0
 pandas>=2.0.0
-scikit-learn>=1.3.0
+scikit-learn>=1.3.0
+spaces>=0.32.0