copilot propositions

Author: qchapp

src/ai_agent/core/model_config.py

@@ -7,6 +7,8 @@
 
 log = logging.getLogger("core.model_config")
 
+_available_models_cache: Optional[Dict[str, Dict[str, Optional[str]]]] = None
+
 
 def get_agent_model() -> Dict[str, Optional[str]]:
     """
@@ -33,6 +35,10 @@ def get_available_models() -> Dict[str, Dict[str, Optional[str]]]:
         Dictionary mapping display_name -> model config
         Returns minimal default if config not available.
     """
+    global _available_models_cache
+    if _available_models_cache is not None:
+        return _available_models_cache
+
     available_models = get_available_models_config()
 
     model_configs = {}
@@ -59,6 +65,7 @@ def get_available_models() -> Dict[str, Dict[str, Optional[str]]]:
         }
 
     log.info(f"Loaded {len(model_configs)} models from config: {list(model_configs.keys())}")
+    _available_models_cache = model_configs
     return model_configs
 
 

src/ai_agent/ui/components.py

@@ -299,12 +299,15 @@ def handle_chat(
             ), gr.update(), gr.update(interactive=False)
 
             # ------------------------------------------------------------------
-            # Resize uploaded image files (max 500×500 px, aspect ratio kept).
-            # The resized paths are used for BOTH the preview and the backend.
+            # Resize uploaded image files for preview only (max 500×500 px).
+            # Original paths are kept for backend format detection.
             # Non-image files (DICOM, NIfTI, CSV, …) are passed through as-is.
             # ------------------------------------------------------------------
+            original_paths: List[str] = []
+            resized_paths: List[str] = []
+            resize_temps: List[str] = []
+
             if files:
-                resized_files: List[str] = []
                 for f in files:
                     if isinstance(f, str):
                         raw_path = f
@@ -315,41 +318,45 @@ def handle_chat(
                     else:
                         raw_path = str(f)
                     if raw_path:
-                        resized_files.append(resize_uploaded_image(raw_path))
-                files = resized_files  # replace with resized versions for all downstream use
+                        original_paths.append(raw_path)
+                        resized = resize_uploaded_image(raw_path)
+                        resized_paths.append(resized)
+                        if resized != raw_path:
+                            resize_temps.append(resized)
 
             # If files were uploaded, build and show preview immediately
-            if files:
-                file_paths = []
-                for f in files:
-                    if isinstance(f, str):
-                        file_paths.append(f)
-                    elif hasattr(f, "name"):
-                        file_paths.append(f.name)
-
-                if file_paths:
-                    # Build preview
-                    try:
-                        preview_path, meta_text = _build_preview_for_vlm(file_paths)
-                        if preview_path:
-                            # Show preview message
-                            preview_text = "📋 **Preview for analysis:**"
-                            if meta_text:
-                                preview_text += f"\n\n_{meta_text}_"
-                            history.append(
-                                {"role": "assistant", "content": preview_text}
-                            )
-                            history.append(
-                                {
-                                    "role": "assistant",
-                                    "content": {"path": preview_path},
-                                }
-                            )
-                            yield history, state_dict, gr.update(), gr.update(), gr.update(), gr.update(), None, gr.update(
-                                visible=False
-                            ), gr.update(), gr.update()
-                    except Exception as e:
-                        log.warning("Preview generation failed: %r", e)
+            if original_paths:
+                try:
+                    preview_path, meta_text = _build_preview_for_vlm(
+                        resized_paths, metadata_paths=original_paths
+                    )
+                    if preview_path:
+                        # Show preview message
+                        preview_text = "📋 **Preview for analysis:**"
+                        if meta_text:
+                            preview_text += f"\n\n_{meta_text}_"
+                        history.append(
+                            {"role": "assistant", "content": preview_text}
+                        )
+                        history.append(
+                            {
+                                "role": "assistant",
+                                "content": {"path": preview_path},
+                            }
+                        )
+                        yield history, state_dict, gr.update(), gr.update(), gr.update(), gr.update(), None, gr.update(
+                            visible=False
+                        ), gr.update(), gr.update()
+                except Exception as e:
+                    log.warning("Preview generation failed: %r", e)
+                finally:
+                    # Resized temp files were only needed for the preview.
+                    for tmp in resize_temps:
+                        try:
+                            os.unlink(tmp)
+                        except Exception:
+                            pass
+                    resize_temps.clear()
 
             # Show "thinking" indicator for agent processing
             thinking_msg = {"role": "assistant", "content": "🤔 Finding tools..."}
@@ -362,7 +369,7 @@ def handle_chat(
             try:
                 reply, new_state = respond(
                     message=message or "",
-                    files=files or [],
+                    files=original_paths or [],
                     state_dict=state_dict,
                     doc_index=doc_index,
                     model=model,

src/ai_agent/utils/previews.py

@@ -27,6 +27,18 @@
     ".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".tif", ".tiff"
 }
 
+# Mapping from extension to PIL format name (preserves original format on resize).
+_PIL_FORMAT_MAP: dict[str, str] = {
+    ".png": "PNG",
+    ".jpg": "JPEG",
+    ".jpeg": "JPEG",
+    ".webp": "WEBP",
+    ".bmp": "BMP",
+    ".gif": "GIF",
+    ".tif": "TIFF",
+    ".tiff": "TIFF",
+}
+
 
 def resize_uploaded_image(
     path: str,
@@ -72,7 +84,11 @@ def resize_uploaded_image(
         elif ext == ".webp":
             suffix, fmt, save_kw = ".webp", "WEBP", {"quality": 85}
         else:
-            suffix, fmt, save_kw = ".png", "PNG", {}
+            # Preserve original format and extension so downstream format
+            # detection (e.g. detect_ext_token) is not misled by a .png suffix.
+            fmt = _PIL_FORMAT_MAP.get(ext, "PNG")
+            suffix = ext if fmt != "PNG" else ".png"
+            save_kw = {}
 
         fd, tmp_path = tempfile.mkstemp(suffix=suffix)
         os.close(fd)
@@ -322,6 +338,7 @@ def pad_to_square(img: np.ndarray, target_size: int) -> np.ndarray:
 
 def _build_preview_for_vlm(
     image_paths: Optional[List[str]],
+    metadata_paths: Optional[List[str]] = None,
 ) -> Tuple[Optional[str], Optional[str]]:
     """
     Build an enhanced preview image optimized for VLM analysis.
@@ -332,6 +349,13 @@ def _build_preview_for_vlm(
     - 4D data: Extract representative 3D volume, then multi-view
     - Medical images: Ensure proper intensity windowing
 
+    Args:
+        image_paths: Paths used to load and render the preview image.
+        metadata_paths: Paths used for metadata extraction only. When
+            provided, metadata reflects these files (e.g. the originals
+            before resizing) while the rendered preview uses
+            ``image_paths``. Defaults to ``image_paths``.
+
     Returns:
         (preview_path, metadata_text)
     """
@@ -346,7 +370,7 @@ def _build_preview_for_vlm(
 
     meta_text = None
     try:
-        meta_text = summarize_image_metadata(image_paths)
+        meta_text = summarize_image_metadata(metadata_paths or image_paths)
     except Exception:
         log.exception(
             "Image metadata summarization failed; continuing without metadata."

tests/test_previews_cache.py

@@ -136,8 +136,8 @@ def test_resize_uploaded_image_large_landscape(tmp_path: Path):
     result = previews.resize_uploaded_image(str(src))
 
     assert result != str(src), "Should write to a new temp file"
-    out_img = Image.open(result)
-    assert out_img.size == (500, 375)
+    with Image.open(result) as out_img:
+        assert out_img.size == (500, 375)
 
 
 def test_resize_uploaded_image_large_portrait(tmp_path: Path):
@@ -148,8 +148,8 @@ def test_resize_uploaded_image_large_portrait(tmp_path: Path):
     result = previews.resize_uploaded_image(str(src))
 
     assert result != str(src)
-    out_img = Image.open(result)
-    assert out_img.size == (375, 500)
+    with Image.open(result) as out_img:
+        assert out_img.size == (375, 500)
 
 
 def test_resize_uploaded_image_already_small_unchanged(tmp_path: Path):
@@ -192,9 +192,9 @@ def test_resize_uploaded_image_jpeg_no_transparency(tmp_path: Path):
     result = previews.resize_uploaded_image(str(src))
 
     assert result != str(src)
-    out_img = Image.open(result)
-    assert out_img.mode == "RGB"
-    assert out_img.size == (500, 375)
+    with Image.open(result) as out_img:
+        assert out_img.mode == "RGB"
+        assert out_img.size == (500, 375)
 
 
 def test_resize_uploaded_image_custom_bounds(tmp_path: Path):
@@ -204,6 +204,6 @@ def test_resize_uploaded_image_custom_bounds(tmp_path: Path):
 
     result = previews.resize_uploaded_image(str(src), max_width=200, max_height=200)
 
-    out_img = Image.open(result)
-    # 1000×500 scaled to fit 200×200: width limited → 200×100
-    assert out_img.size == (200, 100)
+    with Image.open(result) as out_img:
+        # 1000×500 scaled to fit 200×200: width limited → 200×100
+        assert out_img.size == (200, 100)