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.

vision.py

@@ -85,12 +85,14 @@ MODELS = {
         "adaptive_thinking": False,
         "fallbacks": False,
         "in_per_mtok": 2.00, "out_per_mtok": 10.00,
-        # A rough estimate, unlike the Anthropic figures (which were true'd
-        # up against real usage — see the README's grading-cost note). Only
-        # affects the ADVERTISED per-grade estimate in Settings; the actual
-        # billed cost always comes from the real usage this API call
-        # reports, never from this number.
-        "approx_image_tokens": 1500,
+        # Calibrated against 8 real single/two-photo grades once this model
+        # had actually been used (median 18.4k in / 1.2k out). The initial
+        # 1500 was a guess made before any real call existed and advertised
+        # roughly 2.2x under the true cost — a guess is fine to start from,
+        # but it has to be trued up once real usage exists, exactly as the
+        # Anthropic figures were.
+        "approx_image_tokens": 3560,
+        "approx_output_tokens": 1180,
     },
 }
 
@@ -853,6 +855,9 @@ def grade_card(images, api_key=None, model=None, effort=None, zoom_details=True)
     measured = cardimage.edge_wear_profile(images[0][0]) if can_measure else None
     centering = cardimage.centering_profile(images[0][0]) if can_measure else None
     aspect = cardimage.aspect_profile(images[0][0]) if can_measure else None
+    # Same detection failure that makes the measurements refuse also
+    # mis-frames every crop — see cardimage.framing_warning.
+    framing = cardimage.framing_warning(images[0][0]) if (crops or can_measure) else None
 
     prompt_parts = []
     if centering and centering.get("reliable") is False:
@@ -986,6 +991,20 @@ def grade_card(images, api_key=None, model=None, effort=None, zoom_details=True)
             "when you say which corner or edge a problem is on. They add no "
             "information the full photo lacked, only easier viewing, so do not "
             "read resampling softness as card wear.")
+        if framing:
+            prompt_parts.append(
+                "IMPORTANT CAVEAT ON THOSE CROPS: {} Because the crop "
+                "rectangle is derived from that same detection, each close-up "
+                "is cut relative to the wrong boundary — a corner close-up may "
+                "well be showing the CORNER OF A CASE, SLEEVE OR HOLDER with "
+                "the card's own corner sitting somewhere inside the frame, or "
+                "out of it. Do not assume the crop's own edge is the card's "
+                "edge. Find the actual card edge inside each close-up first "
+                "and judge only that; if a given close-up doesn't clearly "
+                "contain the card's real corner or edge, say cannot_assess "
+                "for that category rather than grading the holder. Damage, "
+                "scuffing or whitening on a case is not damage to the "
+                "card.".format(framing))
     prompt_parts.append("Estimate the PSA grade this trading card would likely receive.")
 
     parsed, usage = _call_vision(
@@ -1084,8 +1103,11 @@ def price_guide():
     guide = {}
     for model_id, caps in MODELS.items():
         # 1 supplied photo + ~12 generated close-ups, JSON verdict out.
+        # Per-model output estimate where one is known: GPT-5.6 Sol
+        # reliably writes ~1.2k tokens of verdict against Sonnet's ~0.9k,
+        # enough to matter at these prices.
         est_in = caps["approx_image_tokens"] * 5 + 600
-        est_out = 700
+        est_out = caps.get("approx_output_tokens", 700)
         rate_in, rate_out = current_rates(caps)
         guide[model_id] = {
             "label": caps["label"],