Audit fixes: mis-framed crops, GPT cost 2.2x low, camera button losing photos

1. detail_crops had no equivalent of the margin guard the measurements got.
   When box detection fails (card still in a case), every crop is cut
   relative to the wrong rectangle -- verified the 'TOP-LEFT CORNER'
   close-up of the cased Ohtani is actually the CASE's corner bracket. The
   measurements refuse and explain; the crops kept being produced and
   captioned authoritatively, and the prompt tells the model to judge
   corners/edges/surface *from* them. Now surfaces a framing caveat telling
   the model to locate the real card edge inside each crop and say
   cannot_assess rather than grade the holder.

2. gpt-5.6-sol's approx_image_tokens was a pre-launch guess (1500) that
   advertised ~2.2x under true cost across all 9 real calls. Recalibrated
   to 3560 against median real usage, and added a per-model output estimate
   since GPT writes ~1.2k tokens of verdict vs Sonnet's ~0.9k. Both models
   now advertise within ~3% of observed cost.

3. 'Take photo' didn't reset after a completed grade, unlike 'Choose from
   library'. The result view has no photo strip, so new photos piled up
   invisibly behind the old verdict, silently, to the 6-photo cap. Also
   fixed the cap itself being a silent no-op with no explanation.

cardimage.py

@@ -287,6 +287,31 @@ def _box_margin_reason(box, img_size):
     return None
 
 
+def framing_warning(image_bytes):
+    """The margin problem described in _box_margin_reason, or None.
+
+    Public because the CROPS have the same exposure to it as the pixel
+    measurements do, and worse consequences. When box detection can't find
+    the card's real boundary, every crop is cut relative to the wrong
+    rectangle — a "TOP-LEFT CORNER" close-up of a cased card shows the
+    CASE's corner bracket, with the card's actual corner off to one side.
+    The measurements at least refuse and say why; the crops keep being
+    produced and keep being captioned authoritatively, and the grading
+    prompt tells the model to judge corners/edges/surface *from* them. So
+    the caller needs to be able to warn about the framing rather than
+    silently pass off plastic as cardstock.
+    """
+    if Image is None:
+        return None
+    try:
+        img = Image.open(io.BytesIO(image_bytes))
+        img.load()
+        box = _detect_card_box(img) or (0, 0, img.width, img.height)
+        return _box_margin_reason(box, img.size)
+    except Exception:
+        return None
+
+
 def _surface_map(piece):
     """A band-pass view that isolates surface texture from the artwork.