Update app.py

app.py

@@ -38,13 +38,14 @@ model = load_model(MODEL_PATH, compile=False)
 general_embedding_model = EfficientNetB0(weights="imagenet", include_top=False, pooling="avg")
 ood_detector = TomatoOODDetector(model, stats_path=STATS_PATH, embedding_model=general_embedding_model)
 
-# Tightening this (0.6x) to catch a hard-case apple photo caused a real
-# tomato photo (pile of tomatoes, unusual composition) to get wrongly
-# rejected at distance 12.225 — well within what real training photos span
-# (up to 12.306). That's a worse trade than letting one near-identical
-# apple stock photo through. Reverting to the raw data-derived threshold
-# (margin_factor=1.15 over actual training spread) rather than the
-# aggressive guess.
+# Deliberately tightened below an orange's observed distance (10.661) at the
+# user's explicit request, accepting the known risk: this sits below the
+# distance of at least one real tomato photo seen in testing (a pile of
+# tomatoes, unusual composition, at 12.225), which was wrongly rejected at
+# a similarly tight setting before. Unusual real tomato photos may now get
+# rejected as "not a tomato" — this trade was made knowingly, not by default.
+if ood_detector.distance_threshold is not None:
+    ood_detector.distance_threshold = 10.3
 OOD_CONFIDENCE_THRESHOLD = 0.55
 
 
@@ -100,4 +101,4 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)