fix: resolve basicsr/torchvision shim bug, lint warnings, and code formatting

The torchvision compatibility shim for basicsr 1.4.2 was missing the
parent-package binding and only proxied a single attribute, causing
upscale and enhance-faces to fail at import time. The fix adds a
__getattr__ proxy for all attributes, binds the shim to the parent
package, and installs it in the dispatcher at startup for defense-in-depth.

Also removes unused anyInstalling variable, redundant `as any` cast,
and applies Biome formatting fixes across the codebase.

apps/api/src/routes/features.ts

@@ -239,7 +239,7 @@ export async function registerFeatureRoutes(app: FastifyInstance): Promise<void>
           for (const [otherId, otherBundle] of Object.entries(manifest.bundles)) {
             if (otherId === bundleId) continue;
             if (!isFeatureInstalled(otherId)) continue;
-            for (const m of (otherBundle as any).models ?? []) {
+            for (const m of otherBundle.models ?? []) {
               if (m.path) sharedPaths.add(m.path);
             }
           }

apps/docs/.vitepress/config.mts

@@ -3,7 +3,8 @@ import llmstxt from "vitepress-plugin-llms";
 
 export default defineConfig({
   title: "ashim",
-  description: "Documentation for ashim - A Self Hosted Image Manipulator. 45+ tools, local AI, pipelines, REST API.",
+  description:
+    "Documentation for ashim - A Self Hosted Image Manipulator. 45+ tools, local AI, pipelines, REST API.",
   base: "/ashim/",
   srcDir: ".",
   outDir: "./.vitepress/dist",

apps/web/src/components/settings/ai-features-section.tsx

@@ -91,8 +91,6 @@ export function AiFeaturesSection() {
     prevInstallingKeys.current = currentKeys;
   }, [installing, loadDiskUsage]);
 
-  const anyInstalling = Object.keys(installing).length > 0;
-
   return (
     <div className="space-y-5">
       <div className="flex items-center justify-between">
@@ -105,9 +103,7 @@ export function AiFeaturesSection() {
         <button
           type="button"
           onClick={installAll}
-          disabled={
+          disabled={installAllActive || bundles.every((b) => b.status === "installed")}
-            installAllActive || bundles.every((b) => b.status === "installed")
-          }
           className="flex items-center gap-2 px-4 py-2 rounded-lg bg-primary text-primary-foreground text-sm font-medium hover:bg-primary/90 transition-colors disabled:opacity-50"
         >
           <Download className="h-4 w-4" />

apps/web/src/stores/features-store.ts

@@ -232,9 +232,7 @@ export const useFeaturesStore = create<FeaturesState>((set, get) => {
       // Immediately mark every not-yet-installed bundle as queued so the UI
       // updates right away.  Exclude bundles that are already installing.
       const activeIds = new Set(Object.keys(get().installing));
-      const pending = get().bundles.filter(
+      const pending = get().bundles.filter((b) => b.status !== "installed" && !activeIds.has(b.id));
-        (b) => b.status !== "installed" && !activeIds.has(b.id),
-      );
       // Clear stale errors for these bundles
       const errors = { ...get().errors };
       for (const b of pending) delete errors[b.id];

packages/ai/python/dispatcher.py

@@ -49,6 +49,36 @@ def emit_progress(percent, stage):
     print(json.dumps({"progress": percent, "stage": stage}), file=sys.stderr, flush=True)
 
 
+# ── basicsr / torchvision compatibility shim ──────────────────────────
+# basicsr 1.4.2 (pulled in by realesrgan) does:
+#   from torchvision.transforms.functional_tensor import rgb_to_grayscale
+# but torchvision >= 0.17 removed the functional_tensor submodule,
+# merging everything into torchvision.transforms.functional.
+# We install a shim module ONCE here so every script in this process
+# benefits, rather than relying on each script to patch individually.
+try:
+    import torchvision.transforms.functional_tensor  # noqa: F401
+except (ImportError, ModuleNotFoundError):
+    try:
+        import types
+        import torchvision.transforms.functional as _F
+        import torchvision.transforms
+
+        _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        _shim.__getattr__ = lambda name: getattr(_F, name)
+        _shim.rgb_to_grayscale = _F.rgb_to_grayscale
+        sys.modules["torchvision.transforms.functional_tensor"] = _shim
+        torchvision.transforms.functional_tensor = _shim
+        print("[dispatcher] Installed torchvision.transforms.functional_tensor shim",
+              file=sys.stderr, flush=True)
+    except (ImportError, AttributeError):
+        # torchvision not installed yet — shim not needed until
+        # the upscale-enhance bundle is installed.
+        pass
+except Exception:
+    # Catch-all so dispatcher startup is never blocked.
+    pass
+
 # ── Pre-import heavy libraries ──────────────────────────────────────
 # These imports are the main source of cold-start latency.
 # By importing once at startup, subsequent requests skip the import cost.

packages/ai/python/enhance_faces.py

@@ -3,10 +3,11 @@ import sys
 import json
 import os
 
-# Patch for basicsr compatibility with torchvision >= 0.18.
+# Patch for basicsr compatibility with torchvision >= 0.17.
-# torchvision removed transforms.functional_tensor, merging it into
+# torchvision removed transforms.functional_tensor, merging everything
-# transforms.functional. basicsr still imports the old path, so we
+# into transforms.functional. basicsr 1.4.2 still imports the old path
-# create a shim module to redirect the import.
+# (e.g. rgb_to_grayscale), so we create a proxy module that forwards
+# ALL attribute lookups to the new location.
 try:
     import torchvision.transforms.functional_tensor  # noqa: F401
 except (ImportError, ModuleNotFoundError):
@@ -14,10 +15,20 @@ except (ImportError, ModuleNotFoundError):
         import types
         import torchvision.transforms.functional as _F
 
+        import torchvision.transforms
+
         _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        _shim.__getattr__ = lambda name: getattr(_F, name)
+        # Pre-populate the attribute basicsr actually imports so that
+        # `from torchvision.transforms.functional_tensor import rgb_to_grayscale`
+        # works (from-import checks __dict__ before __getattr__).
         _shim.rgb_to_grayscale = _F.rgb_to_grayscale
         sys.modules["torchvision.transforms.functional_tensor"] = _shim
-    except ImportError as e:
+        # The parent package must also reference the submodule for
+        # `from torchvision.transforms.functional_tensor import ...` to
+        # resolve correctly in all Python versions.
+        torchvision.transforms.functional_tensor = _shim
+    except (ImportError, AttributeError) as e:
         print(f"[enhance-faces] torchvision shim failed: {e}", file=sys.stderr, flush=True)
 
 

packages/ai/python/upscale.py

@@ -3,10 +3,11 @@ import sys
 import json
 import os
 
-# Patch for basicsr compatibility with torchvision >= 0.18.
+# Patch for basicsr compatibility with torchvision >= 0.17.
-# torchvision removed transforms.functional_tensor, merging it into
+# torchvision removed transforms.functional_tensor, merging everything
-# transforms.functional. basicsr still imports the old path, so we
+# into transforms.functional. basicsr 1.4.2 still imports the old path
-# create a shim module to redirect the import.
+# (e.g. rgb_to_grayscale), so we create a proxy module that forwards
+# ALL attribute lookups to the new location.
 try:
     import torchvision.transforms.functional_tensor  # noqa: F401
 except (ImportError, ModuleNotFoundError):
@@ -14,10 +15,20 @@ except (ImportError, ModuleNotFoundError):
         import types
         import torchvision.transforms.functional as _F
 
+        import torchvision.transforms
+
         _shim = types.ModuleType("torchvision.transforms.functional_tensor")
+        _shim.__getattr__ = lambda name: getattr(_F, name)
+        # Pre-populate the attribute basicsr actually imports so that
+        # `from torchvision.transforms.functional_tensor import rgb_to_grayscale`
+        # works (from-import checks __dict__ before __getattr__).
         _shim.rgb_to_grayscale = _F.rgb_to_grayscale
         sys.modules["torchvision.transforms.functional_tensor"] = _shim
-    except ImportError as e:
+        # The parent package must also reference the submodule for
+        # `from torchvision.transforms.functional_tensor import ...` to
+        # resolve correctly in all Python versions.
+        torchvision.transforms.functional_tensor = _shim
+    except (ImportError, AttributeError) as e:
         print(f"[upscale] torchvision shim failed: {e}", file=sys.stderr, flush=True)
 
 

tests/e2e-docker/full-tool-audit.mjs

@@ -46,13 +46,26 @@ async function test(name, path, settings, checks = {}) {
     if (checks.expectKey && checks.expectValue) {
       const actual = body[checks.expectKey];
       if (actual !== checks.expectValue) {
-        log(name, "FAIL", `Expected ${checks.expectKey}=${checks.expectValue} but got ${actual} (FALLBACK DETECTED)`);
+        log(
+          name,
+          "FAIL",
+          `Expected ${checks.expectKey}=${checks.expectValue} but got ${actual} (FALLBACK DETECTED)`,
+        );
         return;
       }
     }
     // Build detail string
     const parts = [];
-    for (const k of ["method", "model", "engine", "format", "width", "height", "facesDetected", "steps"]) {
+    for (const k of [
+      "method",
+      "model",
+      "engine",
+      "format",
+      "width",
+      "height",
+      "facesDetected",
+      "steps",
+    ]) {
       if (body[k] !== undefined) {
         const v = Array.isArray(body[k]) ? JSON.stringify(body[k]) : body[k];
         parts.push(`${k}=${v}`);
@@ -93,27 +106,82 @@ async function main() {
   console.log("--- GPU/AI TOOLS (must use GPU, no CPU fallback) ---\n");
 
   // Background removal — all models
-  await test("Remove BG (birefnet-general-lite)", "remove-background", { model: "birefnet-general-lite" }, { expectKey: "model", expectValue: "birefnet-general-lite" });
+  await test(
-  await test("Remove BG (birefnet-portrait)", "remove-background", { model: "birefnet-portrait" }, { expectKey: "model", expectValue: "birefnet-portrait" });
+    "Remove BG (birefnet-general-lite)",
-  await test("Remove BG (birefnet-general)", "remove-background", { model: "birefnet-general" }, { expectKey: "model", expectValue: "birefnet-general" });
+    "remove-background",
-  await test("Remove BG (u2net)", "remove-background", { model: "u2net" }, { expectKey: "model", expectValue: "u2net" });
+    { model: "birefnet-general-lite" },
-  await test("Remove BG (bria-rmbg)", "remove-background", { model: "bria-rmbg" }, { expectKey: "model", expectValue: "bria-rmbg" });
+    { expectKey: "model", expectValue: "birefnet-general-lite" },
-  await test("Remove BG (isnet-general-use)", "remove-background", { model: "isnet-general-use" }, { expectKey: "model", expectValue: "isnet-general-use" });
+  );
-  await test("Remove BG (birefnet-matting/Ultra)", "remove-background", { model: "birefnet-matting" }, { expectKey: "model", expectValue: "birefnet-matting" });
+  await test(
+    "Remove BG (birefnet-portrait)",
+    "remove-background",
+    { model: "birefnet-portrait" },
+    { expectKey: "model", expectValue: "birefnet-portrait" },
+  );
+  await test(
+    "Remove BG (birefnet-general)",
+    "remove-background",
+    { model: "birefnet-general" },
+    { expectKey: "model", expectValue: "birefnet-general" },
+  );
+  await test(
+    "Remove BG (u2net)",
+    "remove-background",
+    { model: "u2net" },
+    { expectKey: "model", expectValue: "u2net" },
+  );
+  await test(
+    "Remove BG (bria-rmbg)",
+    "remove-background",
+    { model: "bria-rmbg" },
+    { expectKey: "model", expectValue: "bria-rmbg" },
+  );
+  await test(
+    "Remove BG (isnet-general-use)",
+    "remove-background",
+    { model: "isnet-general-use" },
+    { expectKey: "model", expectValue: "isnet-general-use" },
+  );
+  await test(
+    "Remove BG (birefnet-matting/Ultra)",
+    "remove-background",
+    { model: "birefnet-matting" },
+    { expectKey: "model", expectValue: "birefnet-matting" },
+  );
 
   // Upscale
-  await test("Upscale (realesrgan 2x)", "upscale", { scale: 2, model: "realesrgan" }, { expectKey: "method", expectValue: "realesrgan" });
+  await test(
-  await test("Upscale (realesrgan 4x)", "upscale", { scale: 4, model: "realesrgan" }, { expectKey: "method", expectValue: "realesrgan" });
+    "Upscale (realesrgan 2x)",
+    "upscale",
+    { scale: 2, model: "realesrgan" },
+    { expectKey: "method", expectValue: "realesrgan" },
+  );
+  await test(
+    "Upscale (realesrgan 4x)",
+    "upscale",
+    { scale: 4, model: "realesrgan" },
+    { expectKey: "method", expectValue: "realesrgan" },
+  );
   await test("Upscale (lanczos 2x)", "upscale", { scale: 2, model: "lanczos" });
   await test("Upscale (auto)", "upscale", { scale: 2, model: "auto" });
 
   // Face enhancement
-  await test("Face Enhance (gfpgan)", "enhance-faces", { model: "gfpgan" }, { expectKey: "model", expectValue: "gfpgan" });
+  await test(
+    "Face Enhance (gfpgan)",
+    "enhance-faces",
+    { model: "gfpgan" },
+    { expectKey: "model", expectValue: "gfpgan" },
+  );
   await test("Face Enhance (codeformer)", "enhance-faces", { model: "codeformer" });
   await test("Face Enhance (auto)", "enhance-faces", { model: "auto" });
 
   // Colorize
-  await test("Colorize (ddcolor)", "colorize", { model: "ddcolor" }, { expectKey: "method", expectValue: "ddcolor" });
+  await test(
+    "Colorize (ddcolor)",
+    "colorize",
+    { model: "ddcolor" },
+    { expectKey: "method", expectValue: "ddcolor" },
+  );
   await test("Colorize (auto)", "colorize", { model: "auto" });
 
   // Noise removal — all tiers
@@ -126,8 +194,18 @@ async function main() {
   await test("Photo Restoration", "restore-photo", {});
 
   // OCR
-  await test("OCR (tesseract)", "ocr", { engine: "tesseract" }, { expectKey: "engine", expectValue: "tesseract" });
+  await test(
-  await test("OCR (paddleocr)", "ocr", { engine: "paddleocr" }, { expectKey: "engine", expectValue: "paddleocr-v5" });
+    "OCR (tesseract)",
+    "ocr",
+    { engine: "tesseract" },
+    { expectKey: "engine", expectValue: "tesseract" },
+  );
+  await test(
+    "OCR (paddleocr)",
+    "ocr",
+    { engine: "paddleocr" },
+    { expectKey: "engine", expectValue: "paddleocr-v5" },
+  );
 
   // Face operations (MediaPipe)
   await test("Face Blur", "blur-faces", { intensity: 50 });
@@ -159,7 +237,11 @@ async function main() {
   await test("Image Enhancement (vivid)", "image-enhancement", { preset: "vivid" });
   await test("Sharpening", "sharpening", { sigma: 1.5, amount: 1.0 });
   await test("Border", "border", { size: 20, color: "#ff0000" });
-  await test("Replace Color", "replace-color", { targetColor: "#ffffff", replacementColor: "#000000", tolerance: 30 });
+  await test("Replace Color", "replace-color", {
+    targetColor: "#ffffff",
+    replacementColor: "#000000",
+    tolerance: 30,
+  });
 
   // ════════════════════════════════════════════════════════════════
   // SECTION 3: UTILITY TOOLS
@@ -178,10 +260,18 @@ async function main() {
     formData.append("file", new File([imageBlob], "test.webp", { type: "image/webp" }));
     formData.append("settings", JSON.stringify({}));
     const res = await fetch(`${BASE}/api/v1/tools/favicon`, {
-      method: "POST", headers: { Authorization: `Bearer ${token}` }, body: formData,
+      method: "POST",
+      headers: { Authorization: `Bearer ${token}` },
+      body: formData,
     });
-    log("Favicon", res.ok ? "PASS" : "FAIL", `HTTP ${res.status}, ${res.headers.get('content-type')}`);
+    log(
-  } catch (e) { log("Favicon", "FAIL", e.message.slice(0, 100)); }
+      "Favicon",
+      res.ok ? "PASS" : "FAIL",
+      `HTTP ${res.status}, ${res.headers.get("content-type")}`,
+    );
+  } catch (e) {
+    log("Favicon", "FAIL", e.message.slice(0, 100));
+  }
 
   // ════════════════════════════════════════════════════════════════
   // SECTION 4: MULTI-IMAGE / SPECIAL TOOLS (may need special input)
@@ -189,7 +279,12 @@ async function main() {
   console.log("\n--- SPECIAL TOOLS (may need specific inputs) ---\n");
 
   await test("QR Generate", "qr-generate", { text: "https://ashim.app", size: 512, format: "png" });
-  await test("Text Overlay", "text-overlay", { text: "TEST", fontSize: 48, color: "#ff0000", position: "center" });
+  await test("Text Overlay", "text-overlay", {
+    text: "TEST",
+    fontSize: 48,
+    color: "#ff0000",
+    position: "center",
+  });
   await test("Vectorize", "vectorize", {});
   await test("SVG to Raster", "svg-to-raster", {}); // Will fail - needs SVG input
 
@@ -200,8 +295,8 @@ async function main() {
   console.log("  SUMMARY");
   console.log("=============================================================\n");
 
-  const passed = results.filter(r => r.status === "PASS");
+  const passed = results.filter((r) => r.status === "PASS");
-  const failed = results.filter(r => r.status === "FAIL");
+  const failed = results.filter((r) => r.status === "FAIL");
 
   console.log(`PASSED: ${passed.length}`);
   console.log(`FAILED: ${failed.length}`);
@@ -219,21 +314,34 @@ async function main() {
   // Check GPU usage in docker logs
   console.log("\n--- GPU USAGE CHECK ---\n");
   const { execSync } = await import("child_process");
-  const logs = execSync('docker logs ashim 2>&1', { encoding: 'utf-8', maxBuffer: 1024 * 1024 });
+  const logs = execSync("docker logs ashim 2>&1", { encoding: "utf-8", maxBuffer: 1024 * 1024 });
-  const gpuLines = logs.split('\n').filter(l =>
+  const gpuLines = logs
-    l.includes('[gpu]') || l.includes('[bridge]') || l.includes('[dispatcher]') ||
+    .split("\n")
-    l.includes('GPU') || l.includes('CUDA') || l.includes('CUDAExecution')
+    .filter(
-  );
+      (l) =>
+        l.includes("[gpu]") ||
+        l.includes("[bridge]") ||
+        l.includes("[dispatcher]") ||
+        l.includes("GPU") ||
+        l.includes("CUDA") ||
+        l.includes("CUDAExecution"),
+    );
   for (const line of gpuLines.slice(0, 15)) {
     console.log("  " + line.trim().slice(0, 120));
   }
 
   // Check for any fallback warnings
   console.log("\n--- FALLBACK/MISMATCH WARNINGS ---\n");
-  const warnLines = logs.split('\n').filter(l =>
+  const warnLines = logs
-    l.includes('mismatch') || l.includes('fallback') || l.includes('Falling back') ||
+    .split("\n")
-    l.includes('degraded') || l.includes('lanczos') && l.includes('warn')
+    .filter(
-  );
+      (l) =>
+        l.includes("mismatch") ||
+        l.includes("fallback") ||
+        l.includes("Falling back") ||
+        l.includes("degraded") ||
+        (l.includes("lanczos") && l.includes("warn")),
+    );
   if (warnLines.length === 0) {
     console.log("  None detected - no silent fallbacks occurred.");
   } else {
@@ -245,4 +353,7 @@ async function main() {
   process.exit(failed.length > 0 ? 1 : 0);
 }
 
-main().catch(err => { console.error("Fatal:", err); process.exit(1); });
+main().catch((err) => {
+  console.error("Fatal:", err);
+  process.exit(1);
+});

tests/e2e/helpers.ts

@@ -87,7 +87,12 @@ export function getTestImagePath(): string {
     ihdr[9] = 6; // RGBA
     fs.writeFileSync(
       _testImagePath,
-      Buffer.concat([sig, chunk("IHDR", ihdr), chunk("IDAT", deflated), chunk("IEND", Buffer.alloc(0))]),
+      Buffer.concat([
+        sig,
+        chunk("IHDR", ihdr),
+        chunk("IDAT", deflated),
+        chunk("IEND", Buffer.alloc(0)),
+      ]),
     );
   }