fix(inference): METADATA-35 fit Call 1/2 images to 1024 long edge

Downscale processed PNGs only on the inference send path (keep alpha, never
upscale). Stored inventory/Woo blobs unchanged. Adds Pillow + unit/e2e-style
tests; cite tests on METADATA-35.

backend/tests/test_inference.py

@@ -126,3 +126,82 @@ def test_test_inference_unconfigured(env):
     r = client.post("/api/v1/admin/test-inference")
     assert r.status_code == 200
     assert r.json()["ok"] is False and r.json()["mode"] == "fail"
+
+
+def _rgba_png(w: int, h: int) -> bytes:
+    """RGBA PNG: opaque body + transparent top-left block (capture-like alpha)."""
+    from PIL import Image
+    import io
+    im = Image.new("RGBA", (w, h), (10, 20, 30, 255))
+    for y in range(min(80, h)):
+        for x in range(min(80, w)):
+            im.putpixel((x, y), (0, 0, 0, 0))
+    buf = io.BytesIO()
+    im.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def test_fit_long_edge_png_downscales_over_1024():
+    """METADATA-35: Call 1 images long edge <= 1024; never upscale; keep alpha."""
+    from backend.inference import fit_long_edge_png
+    from PIL import Image
+    import io
+
+    src = _rgba_png(800, 1067)  # inventory 3:4, long edge 1067
+    out = fit_long_edge_png(src, max_long_edge=1024)
+    im = Image.open(io.BytesIO(out)).convert("RGBA")
+    assert max(im.size) == 1024
+    assert min(im.size) < 1024
+    w, h = im.size
+    assert abs((w / h) - (800 / 1067)) < 0.02
+    # Transparent block still transparent near origin after downscale
+    assert im.getpixel((0, 0))[3] == 0
+    assert im.getpixel((5, 5))[3] == 0
+
+
+def test_fit_long_edge_png_leaves_small_unchanged_dims():
+    from backend.inference import fit_long_edge_png
+    from PIL import Image
+    import io
+
+    src = _rgba_png(400, 300)
+    out = fit_long_edge_png(src, max_long_edge=1024)
+    im = Image.open(io.BytesIO(out))
+    assert im.size == (400, 300)
+
+
+def test_call1_sends_long_edge_at_most_1024(env, monkeypatch):
+    """METADATA-35 end-to-end: messages to the model use <=1024 long edge."""
+    client, conn = env
+    conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_enabled','true',NULL)")
+    conn.execute(
+        "INSERT INTO slabs(id,sku,length_in,thickness_in,status,server_rev,client_rev,created_at,updated_at) "
+        "VALUES ('s1','SLAB-01',100,2,'calibrated',1,1,'t','t')"
+    )
+    big = _rgba_png(800, 1067)
+    conn.execute(
+        "INSERT INTO slab_photos(id,slab_id,seq,processed_blob,created_at) VALUES ('p1','s1',1,?, 't')",
+        (big,),
+    )
+    conn.commit()
+    from backend.inference import InferenceClient
+    from PIL import Image
+    import io
+
+    captured = {}
+
+    def fake_chat(self, messages):
+        captured["messages"] = messages
+        return json.dumps({"species_id": 1, "species_confidence": 0.9})
+
+    monkeypatch.setattr(InferenceClient, "chat", fake_chat)
+    r = client.post("/api/v1/slabs/s1/inference/call1")
+    assert r.status_code == 200, r.text
+    content = captured["messages"][0]["content"]
+    imgs = [p for p in content if isinstance(p, dict) and p.get("type") == "image_url"]
+    assert len(imgs) == 1
+    url = imgs[0]["image_url"]["url"]
+    assert url.startswith("data:image/png;base64,")
+    raw = base64.b64decode(url.split(",", 1)[1])
+    im = Image.open(io.BytesIO(raw))
+    assert max(im.size) <= 1024