Deploy FaceCloak

README.md

@@ -10,7 +10,9 @@ app_file: app.py
 suggested_hardware: cpu-basic
 ---
 
-# 🛡️ FaceCloak — Adversarial Biometric Privacy
+# FaceCloak — Adversarial Biometric Privacy
+
+[**Live Demo on Hugging Face Spaces**](https://huggingface.co/spaces/a-01a/facecloak)
 
 **Upload your photo. Watch AI become blind to your face.**
 
@@ -36,9 +38,9 @@ The following table demonstrates the drop in recognition confidence on standardi
 
 | Strength (ε) | Steps | Original Similarity | Cloaked Similarity | Result |
 |--------------|-------|---------------------|--------------------|--------|
-| 0.01 (Weak)  | 50    | 0.999               | 0.654              | ⚠️ Matched |
-| 0.03 (Normal)| 100   | 0.999               | 0.128              | ✅ Cloaked |
-| 0.05 (Strong)| 200   | 0.999               | -0.215             | ✅ Cloaked |
+| 0.01 (Weak)  | 50    | 0.999               | 0.654              | WARNING |
+| 0.03 (Normal)| 100   | 0.999               | 0.128              | SUCCESS |
+| 0.05 (Strong)| 200   | 0.999               | -0.215             | SUCCESS |
 
 ## Limitations & Ethical Considerations
 
@@ -60,3 +62,8 @@ uv run python app.py
 uv run pytest -v                   # unit tests
 uv run pytest -v -m integration    # tests with real portrait images
 ```
+
+## License
+
+MIT License
+Copyright (c) 990aa

app.py

@@ -3,4 +3,3 @@ from facecloak.interface import APP_CSS, APP_THEME, demo
 
 if __name__ == "__main__":
     demo.launch(theme=APP_THEME, css=APP_CSS)
-

facecloak/cloaking.py

@@ -23,7 +23,7 @@ from facecloak.pipeline import (
 @dataclass(frozen=True, slots=True)
 class CloakHyperparameters:
     epsilon: float = 0.03
-    alpha_fraction: float = 0.1   # alpha = alpha_fraction * epsilon
+    alpha_fraction: float = 0.1  # alpha = alpha_fraction * epsilon
     num_steps: int = 100
     l2_lambda: float = 0.01
 
@@ -86,9 +86,7 @@ def cloak_face_tensor(
 
     model = model or get_embedding_model()
     original_batch = (
-        _prepare_face_batch(face_tensor)
-        .to(next(model.parameters()).device)
-        .detach()
+        _prepare_face_batch(face_tensor).to(next(model.parameters()).device).detach()
     )
     original_embedding = model(original_batch).detach()
     delta = torch.zeros_like(original_batch, requires_grad=True)
@@ -104,7 +102,9 @@ def cloak_face_tensor(
 
         cloaked_batch = torch.clamp(original_batch + delta, DISPLAY_MIN, DISPLAY_MAX)
         cloaked_embedding = model(cloaked_batch)
-        cosine = F.cosine_similarity(original_embedding, cloaked_embedding, dim=1).mean()
+        cosine = F.cosine_similarity(
+            original_embedding, cloaked_embedding, dim=1
+        ).mean()
 
         objective = -cosine - parameters.l2_lambda * delta.pow(2).mean()
         objective.backward()
@@ -113,7 +113,8 @@ def cloak_face_tensor(
             delta.add_(alpha * delta.grad.sign())
             delta.clamp_(-parameters.epsilon, parameters.epsilon)
             delta.copy_(
-                torch.clamp(original_batch + delta, DISPLAY_MIN, DISPLAY_MAX) - original_batch
+                torch.clamp(original_batch + delta, DISPLAY_MIN, DISPLAY_MAX)
+                - original_batch
             )
 
         # Optimization (Step 38): `.item()` blocks the execution stream. Only evaluate
@@ -139,7 +140,9 @@ def cloak_face_tensor(
         original_face_image=standardized_tensor_to_pil(original_cpu),
         cloaked_face_image=standardized_tensor_to_pil(final_cpu),
         perturbation_preview=perturbation_preview_image(delta_cpu),
-        amplified_diff=amplified_diff_image(original_cpu[0], final_cpu[0], amplification=75.0),
+        amplified_diff=amplified_diff_image(
+            original_cpu[0], final_cpu[0], amplification=75.0
+        ),
         cloaked_face_tensor=final_cpu[0],
         delta_tensor=delta_cpu[0],
         original_similarity=1.0,

facecloak/deploy.py

@@ -65,7 +65,9 @@ def default_space_repo_id(api: HfApi, token: str) -> str:
     whoami = api.whoami(token=token, cache=False)
     username = whoami.get("name")
     if not username:
-        raise FaceCloakError("Could not determine the Hugging Face username from the provided token.")
+        raise FaceCloakError(
+            "Could not determine the Hugging Face username from the provided token."
+        )
     return f"{username}/{PROJECT_SLUG}"
 
 

facecloak/interface.py

@@ -2,9 +2,7 @@
 
 from __future__ import annotations
 
-import io
 import tempfile
-from pathlib import Path
 
 import gradio as gr
 
@@ -18,7 +16,7 @@ from facecloak.pipeline import (
     interpret_score,
     verify_cloak,
 )
-from facecloak.project import PROJECT_NAME, PROJECT_TAGLINE
+from facecloak.project import PROJECT_NAME
 
 # ---------------------------------------------------------------------------
 # CSS — dark-on-light theme with explicit text colours for HF Spaces compat
@@ -33,28 +31,6 @@ APP_CSS = """
 
 body, .gradio-container {
     font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif !important;
-    color: #0f172a !important;
-    background: #f8fafc !important;
-}
-
-/* ── Background gradient ──────────────────────────────────────── */
-.gradio-container {
-    background:
-        radial-gradient(ellipse 80% 60% at 10% 0%, rgba(99, 102, 241, 0.08) 0%, transparent 55%),
-        radial-gradient(ellipse 60% 50% at 90% 5%, rgba(244, 114, 182, 0.07) 0%, transparent 50%),
-        linear-gradient(160deg, #f0f4ff 0%, #faf5ff 45%, #fff7f0 100%) !important;
-    min-height: 100vh;
-}
-
-/* ── Force dark text everywhere ──────────────────────────────── */
-label, .label-wrap, .block > label,
-span, p, h1, h2, h3, h4, h5, h6,
-.markdown, .markdown p, .markdown li,
-.markdown h1, .markdown h2, .markdown h3,
-.svelte-1ed2p3z, .svelte-1gfkn6j,
-textarea, input, .gr-textbox textarea,
-.gr-number input, .gr-slider label {
-    color: #0f172a !important;
 }
 
 /* ── Hero section ────────────────────────────────────────────── */
@@ -73,32 +49,40 @@ textarea, input, .gr-textbox textarea,
 
 /* ── Panel cards ─────────────────────────────────────────────── */
 .panel {
-    background: rgba(255, 255, 255, 0.85) !important;
+    background: #ffffff !important;
     border: 1px solid rgba(99, 102, 241, 0.12) !important;
     border-radius: 16px !important;
     box-shadow: 0 4px 24px rgba(99, 102, 241, 0.07) !important;
     padding: 1rem 1.25rem !important;
-    color: #0f172a !important;
 }
 .panel *, .panel .markdown, .panel .markdown p, .panel .markdown li {
     color: #0f172a !important;
 }
 
-/* ── Accordion ───────────────────────────────────────────────── */
-.accordion, details, details summary {
-    background: rgba(248, 250, 252, 0.9) !important;
-    color: #334155 !important;
+/* ── Accordion (What do these settings mean?) ────────────────── */
+.accordion, details {
+    background: #1e293b !important;
     border-radius: 12px !important;
+    border: 1px solid rgba(255,255,255,0.1) !important;
+}
+details summary, details summary * {
+    color: #f8fafc !important;
+    font-weight: 600 !important;
 }
 details *, details .markdown, details .markdown p {
-    color: #334155 !important;
+    color: #e2e8f0 !important;
 }
 
-/* ── Sliders ─────────────────────────────────────────────────── */
-.gr-slider label, input[type=range] + span,
-.gr-slider .label-wrap span {
-    color: #334155 !important;
-    font-weight: 500;
+/* ── Sliders and settings ────────────────────────────────────── */
+.gr-slider label, .gr-slider .label-wrap span, input[type=range] + span {
+    color: #f8fafc !important;
+    font-weight: 600 !important;
+}
+
+.gr-slider {
+    background: #1e293b !important;
+    padding: 1rem !important;
+    border-radius: 12px !important;
 }
 
 /* ── Buttons ─────────────────────────────────────────────────── */
@@ -145,24 +129,18 @@ details *, details .markdown, details .markdown p {
     border-radius: 10px !important;
     color: #0f172a !important;
     border: 1px solid rgba(99, 102, 241, 0.2) !important;
-    background: rgba(255,255,255,0.9) !important;
-}
-
-/* ── Images ──────────────────────────────────────────────────── */
-.image-container img {
-    border-radius: 12px !important;
-    box-shadow: 0 4px 20px rgba(0,0,0,0.1) !important;
+    background: #ffffff !important;
 }
 
 /* ── Tab strip ───────────────────────────────────────────────── */
-.tab-nav button {
-    color: #475569 !important;
-    font-weight: 500 !important;
+button[role="tab"] {
+    color: #1e293b !important;
+    font-weight: 600 !important;
+    font-size: 1.05rem !important;
 }
-.tab-nav button.selected {
+button[role="tab"][aria-selected="true"] {
     color: #4f46e5 !important;
     border-bottom-color: #4f46e5 !important;
-    font-weight: 600 !important;
 }
 """
 
@@ -179,6 +157,7 @@ APP_THEME = gr.themes.Base(
 # Score formatting helpers
 # ---------------------------------------------------------------------------
 
+
 def _pct(similarity: float) -> str:
     return f"{max(0.0, min(100.0, similarity * 100.0)):.1f}%"
 
@@ -196,6 +175,7 @@ def _format_detection_probability(probability: float | None) -> str:
 # Core processing — generator so Gradio streams progress (Step 28)
 # ---------------------------------------------------------------------------
 
+
 def generate_cloak(image, epsilon, num_steps, alpha_fraction):
     """Generator function that yields intermediate UI state every PGD step.
 
@@ -293,13 +273,13 @@ def generate_cloak(image, epsilon, num_steps, alpha_fraction):
         chunk = acc.lines[i : i + chunk_size]
         status_so_far += "\n".join(chunk) + "\n"
         yield (
-            None,    # orig_img – not yet
-            None,    # cloaked_img – not yet
-            None,    # diff_img – not yet
+            None,  # orig_img – not yet
+            None,  # cloaked_img – not yet
+            None,  # diff_img – not yet
             orig_score_text,
             "Optimizing…",
             status_so_far,
-            None,    # download path – not yet
+            None,  # download path – not yet
         )
 
     # ── Post-cloak verification (Step 21) ─────────────────────────────── #
@@ -321,7 +301,7 @@ def generate_cloak(image, epsilon, num_steps, alpha_fraction):
     warning_msg = ""
     if final_sim > 0.5:
         warning_msg = (
-            "\n⚠️  Partial cloak achieved. "
+            "\nWARNING: Partial cloak achieved. "
             "Try increasing the number of steps or the epsilon value."
         )
 
@@ -338,7 +318,7 @@ def generate_cloak(image, epsilon, num_steps, alpha_fraction):
 
     # ── Final yield with all outputs filled in ────────────────────────── #
     yield (
-        detected.image,          # original aligned face
+        detected.image,  # original aligned face
         result.cloaked_face_image,
         result.amplified_diff,
         orig_score_text,
@@ -352,10 +332,13 @@ def generate_cloak(image, epsilon, num_steps, alpha_fraction):
 # Compare-faces tab (kept for technical reviewers)
 # ---------------------------------------------------------------------------
 
+
 def compare_faces(image_a, image_b):
     try:
         if image_a is None or image_b is None:
-            raise FaceCloakError("Please provide both images before running a similarity check.")
+            raise FaceCloakError(
+                "Please provide both images before running a similarity check."
+            )
 
         detected_a = detect_primary_face(image_a)
         detected_b = detect_primary_face(image_b)
@@ -381,13 +364,13 @@ def compare_faces(image_a, image_b):
 # Build the Gradio app
 # ---------------------------------------------------------------------------
 
+
 def build_demo() -> gr.Blocks:  # noqa: C901
     with gr.Blocks(title=PROJECT_NAME) as demo:
-
         # ── Hero ─────────────────────────────────────────────────────── #
         gr.Markdown(
-            f"""
-# 🛡️ FaceCloak
+            """
+# <svg width="32" height="32" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" style="display:inline; vertical-align:middle; margin-right:8px;"><path d="M12 22s8-4 8-10V5l-8-3-8 3v7c0 6 8 10 8 10z"></path></svg> FaceCloak
 ## Upload your photo. Watch AI become blind to your face.
 
 This tool uses adversarial mathematics to make microscopic pixel changes that are invisible to humans but completely scramble AI facial recognition systems.
@@ -397,9 +380,8 @@ This tool uses adversarial mathematics to make microscopic pixel changes that ar
 
         # ── Tabs ─────────────────────────────────────────────────────── #
         with gr.Tab("Generate Cloak"):
-
             # ── Input section ─────────────────────────────────────────── #
-            gr.Markdown("### 📸 Step 1 — Upload Your Photo", elem_classes=["panel"])
+            gr.Markdown("### <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" style=\"display:inline; vertical-align:middle; margin-right:8px;\"><path d=\"M23 19a2 2 0 0 1-2 2H3a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h4l2-3h6l2 3h4a2 2 0 0 1 2 2z\"></path><circle cx=\"12\" cy=\"13\" r=\"4\"></circle></svg> Step 1 — Upload Your Photo", elem_classes=["panel"])
 
             with gr.Row():
                 with gr.Column(scale=1):
@@ -415,7 +397,7 @@ This tool uses adversarial mathematics to make microscopic pixel changes that ar
                     )
 
                 with gr.Column(scale=1):
-                    gr.Markdown("### ⚙️ Step 2 — Adjust Settings (optional)")
+                    gr.Markdown("### <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" style=\"display:inline; vertical-align:middle; margin-right:8px;\"><circle cx=\"12\" cy=\"12\" r=\"3\"></circle><path d=\"M19.4 15a1.65 1.65 0 0 0 .33 1.82l.06.06a2 2 0 0 1 0 2.83 2 2 0 0 1-2.83 0l-.06-.06a1.65 1.65 0 0 0-1.82-.33 1.65 1.65 0 0 0-1 1.51V21a2 2 0 0 1-2 2 2 2 0 0 1-2-2v-.09A1.65 1.65 0 0 0 9 19.4a1.65 1.65 0 0 0-1.82.33l-.06.06a2 2 0 0 1-2.83 0 2 2 0 0 1 0-2.83l.06-.06a1.65 1.65 0 0 0 .33-1.82 1.65 1.65 0 0 0-1.51-1H3a2 2 0 0 1-2-2 2 2 0 0 1 2-2h.09A1.65 1.65 0 0 0 4.6 9a1.65 1.65 0 0 0-.33-1.82l-.06-.06a2 2 0 0 1 0-2.83 2 2 0 0 1 2.83 0l.06.06a1.65 1.65 0 0 0 1.82.33H9a1.65 1.65 0 0 0 1-1.51V3a2 2 0 0 1 2-2 2 2 0 0 1 2 2v.09a1.65 1.65 0 0 0 1 1.51 1.65 1.65 0 0 0 1.82-.33l.06-.06a2 2 0 0 1 2.83 0 2 2 0 0 1 0 2.83l-.06.06a1.65 1.65 0 0 0-.33 1.82V9a1.65 1.65 0 0 0 1.51 1H21a2 2 0 0 1 2 2 2 2 0 0 1-2 2h-.09a1.65 1.65 0 0 0-1.51 1z\"></path></svg> Step 2 — Adjust Settings (optional)")
 
                     epsilon = gr.Slider(
                         minimum=0.01,
@@ -442,7 +424,7 @@ This tool uses adversarial mathematics to make microscopic pixel changes that ar
                         info="How aggressively each step adjusts pixels. 0.1 is a good default.",
                     )
 
-                    with gr.Accordion("🔬 What do these settings mean?", open=False):
+                    with gr.Accordion("What do these settings mean?", open=False):
                         gr.Markdown(
                             """
 **Perturbation Strength (epsilon):** Think of this as the maximum number of steps
@@ -461,7 +443,7 @@ The defaults work well for most photos. Only adjust if you are experimenting.
 
             # ── Trigger ───────────────────────────────────────────────── #
             cloak_btn = gr.Button(
-                "⚡ Generate Cloak",
+                "Generate Cloak",
                 variant="primary",
                 elem_id="cloak-btn",
             )
@@ -476,7 +458,7 @@ The defaults work well for most photos. Only adjust if you are experimenting.
             )
 
             # ── Results section ───────────────────────────────────────── #
-            gr.Markdown("### 🎯 Results", elem_classes=["panel"])
+            gr.Markdown("### <svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" style=\"display:inline; vertical-align:middle; margin-right:8px;\"><circle cx=\"12\" cy=\"12\" r=\"10\"></circle><circle cx=\"12\" cy=\"12\" r=\"6\"></circle><circle cx=\"12\" cy=\"12\" r=\"2\"></circle></svg> Results", elem_classes=["panel"])
 
             with gr.Row():
                 with gr.Column(scale=1):
@@ -514,7 +496,7 @@ The defaults work well for most photos. Only adjust if you are experimenting.
 
             with gr.Row(elem_id="download-btn"):
                 download_btn = gr.DownloadButton(
-                    label="⬇️  Download Cloaked Image",
+                    label="Download Cloaked Image",
                     variant="secondary",
                 )
 
@@ -543,10 +525,16 @@ The defaults work well for most photos. Only adjust if you are experimenting.
                 compare_image_a = gr.Image(label="Photo A", type="pil")
                 compare_image_b = gr.Image(label="Photo B", type="pil")
             with gr.Row():
-                aligned_a = gr.Image(label="Detected Face A", type="pil", interactive=False)
-                aligned_b = gr.Image(label="Detected Face B", type="pil", interactive=False)
+                aligned_a = gr.Image(
+                    label="Detected Face A", type="pil", interactive=False
+                )
+                aligned_b = gr.Image(
+                    label="Detected Face B", type="pil", interactive=False
+                )
             compare_button = gr.Button("Compare Faces", variant="primary")
-            pair_similarity = gr.Number(label="Cosine Similarity (1.0 = identical, 0 = unrelated)", precision=4)
+            pair_similarity = gr.Number(
+                label="Cosine Similarity (1.0 = identical, 0 = unrelated)", precision=4
+            )
             pair_summary = gr.Markdown(elem_classes=["panel"])
             compare_button.click(
                 fn=compare_faces,

facecloak/models.py

@@ -41,7 +41,7 @@ def get_embedding_model() -> InceptionResnetV1:
         parameter.requires_grad_(False)
     return model
 
+
 # Step 37: Optimize initialization (instantiate at module load time)
 get_face_detector()
 get_embedding_model()
-

facecloak/pipeline.py

@@ -118,7 +118,9 @@ def amplified_diff_image(
     return Image.fromarray(arr, mode="RGB")
 
 
-def detect_primary_face(image: Image.Image, detector: Any | None = None) -> DetectedFace:
+def detect_primary_face(
+    image: Image.Image, detector: Any | None = None
+) -> DetectedFace:
     """Detect and align the largest face, with input-size guard (Steps 30, 34)."""
     detector = detector or get_face_detector()
     rgb_image = ensure_rgb(resize_for_detection(image))
@@ -190,13 +192,13 @@ def interpret_score(similarity: float) -> tuple[str, str | None]:
         ``None`` when the cloak fully succeeded; a guidance string otherwise.
     """
     if similarity > 0.7:
-        label = "⚠️ Face Matched — AI still recognizes you"
+        label = "WARNING: Face Matched — AI still recognizes you"
         warning = "Partial cloak only. Try increasing the number of steps or epsilon."
     elif similarity > 0.3:
-        label = "🔶 Partial Cloak — recognition weakened"
+        label = "PARTIAL: Partial Cloak — recognition weakened"
         warning = "Try increasing the number of steps or the epsilon value for a stronger cloak."
     else:
-        label = "✅ Identity Cloaked — AI cannot recognize you"
+        label = "SUCCESS: Identity Cloaked — AI cannot recognize you"
         warning = None
     return label, warning
 

tests/test_deploy.py

@@ -47,7 +47,9 @@ def _local_temp_dir(name: str) -> Path:
 def test_read_env_file_parses_simple_key_values() -> None:
     temp_dir = _local_temp_dir("env")
     env_path = temp_dir / ".env"
-    env_path.write_text(f"{HF_TOKEN_ENV_VAR}=hf_test_token\nOTHER=value\n", encoding="utf-8")
+    env_path.write_text(
+        f"{HF_TOKEN_ENV_VAR}=hf_test_token\nOTHER=value\n", encoding="utf-8"
+    )
 
     values = read_env_file(env_path)
 

tests/test_integration.py

@@ -75,4 +75,3 @@ def test_verify_cloak_uses_fresh_mtcnn_pass_on_real_image() -> None:
     # A strong cloak can push cosine similarity negative (anti-correlated vectors).
     # pct = similarity * 100 and is NOT clamped — that is intentional.
     assert verification.pct == pytest.approx(verification.similarity * 100.0, abs=0.01)
-

tests/test_interface.py

@@ -48,9 +48,11 @@ def test_interface_has_no_phase_references() -> None:
     # The demo object doesn't expose raw text easily; check the source module
     import inspect
     from facecloak import interface
+
     source = inspect.getsource(interface)
     # Should not contain "Phase 2", "Phase 3" etc. in user-facing strings
     import re
-    matches = re.findall(r'\bPhase [0-9]\b', source)
+
+    matches = re.findall(r"\bPhase [0-9]\b", source)
     # Allow zero matches
     assert len(matches) == 0, f"Found phase number references in interface: {matches}"

tests/test_pipeline.py

@@ -10,7 +10,6 @@ import torch
 from facecloak.errors import FaceCloakError
 from facecloak.pipeline import (
     MAX_INPUT_DIMENSION,
-    MIN_CROP_DIMENSION,
     amplified_diff_image,
     cosine_similarity,
     detect_primary_face,
@@ -54,8 +53,11 @@ class DummyEmbeddingModel(torch.nn.Module):
 
 # ── detect_primary_face ─────────────────────────────────────────────────── #
 
+
 def test_detect_primary_face_returns_probability_and_image() -> None:
-    detected = detect_primary_face(Image.new("RGB", (64, 64), "white"), detector=DummyDetector())
+    detected = detect_primary_face(
+        Image.new("RGB", (64, 64), "white"), detector=DummyDetector()
+    )
 
     assert detected.tensor.shape == (3, 160, 160)
     assert detected.probability == pytest.approx(0.97)
@@ -64,11 +66,14 @@ def test_detect_primary_face_returns_probability_and_image() -> None:
 
 def test_detect_primary_face_raises_readable_error_when_no_face_found() -> None:
     with pytest.raises(FaceCloakError, match="No face detected"):
-        detect_primary_face(Image.new("RGB", (64, 64), "white"), detector=MissingFaceDetector())
+        detect_primary_face(
+            Image.new("RGB", (64, 64), "white"), detector=MissingFaceDetector()
+        )
 
 
 # ── resize_for_detection (Step 34) ──────────────────────────────────────── #
 
+
 def test_resize_for_detection_downsizes_large_images() -> None:
     big = Image.new("RGB", (2000, 1500), "white")
     resized = resize_for_detection(big)
@@ -90,6 +95,7 @@ def test_resize_for_detection_preserves_aspect_ratio() -> None:
 
 # ── Embedding extraction ─────────────────────────────────────────────────── #
 
+
 def test_extract_embedding_tensor_preserves_gradients() -> None:
     face_tensor = torch.ones(3, 4, 4, requires_grad=True)
     model = DummyEmbeddingModel()
@@ -103,6 +109,7 @@ def test_extract_embedding_tensor_preserves_gradients() -> None:
 
 # ── cosine_similarity ────────────────────────────────────────────────────── #
 
+
 def test_cosine_similarity_behaves_for_identical_and_opposite_vectors() -> None:
     same = cosine_similarity(np.array([1.0, 2.0]), np.array([1.0, 2.0]))
     opposite = cosine_similarity(np.array([1.0, 0.0]), np.array([-1.0, 0.0]))
@@ -113,6 +120,7 @@ def test_cosine_similarity_behaves_for_identical_and_opposite_vectors() -> None:
 
 # ── Visualization helpers ────────────────────────────────────────────────── #
 
+
 def test_tensor_visualization_helpers_return_pil_images() -> None:
     face_tensor = torch.zeros(3, 160, 160)
     face_image = standardized_tensor_to_pil(face_tensor)
@@ -143,6 +151,7 @@ def test_amplified_diff_image_amplifies_differences() -> None:
 
 # ── interpret_score (Step 22) ────────────────────────────────────────────── #
 
+
 def test_interpret_score_high_similarity_returns_matched_label() -> None:
     label, warning = interpret_score(0.85)
     assert "Matched" in label
@@ -163,12 +172,14 @@ def test_interpret_score_mid_range_returns_partial_label() -> None:
 
 # ── verify_cloak (Step 21) ───────────────────────────────────────────────── #
 
+
 def test_verify_cloak_uses_fresh_detection_not_original_tensor() -> None:
     """verify_cloak must run MTCNN on the cloaked PIL image, not reuse tensors."""
     import torch.nn.functional as F
 
     class SizeAgnosticModel(torch.nn.Module):
         """Returns L2-normalized mean of flattened tensor, works for any H×W."""
+
         device = torch.device("cpu")
 
         def forward(self, batch: torch.Tensor) -> torch.Tensor:
@@ -179,6 +190,7 @@ def test_verify_cloak_uses_fresh_detection_not_original_tensor() -> None:
 
     class NonZeroDetector:
         """Returns a non-zero face tensor so embedding is always non-zero."""
+
         def __call__(self, _image, return_prob=False):
             face_tensor = torch.ones(3, 160, 160) * 0.3  # ~38/255 grey
             if return_prob:
@@ -202,5 +214,3 @@ def test_verify_cloak_uses_fresh_detection_not_original_tensor() -> None:
     assert hasattr(result, "label")
     assert hasattr(result, "pct")
     assert isinstance(result.label, str) and len(result.label) > 0
-
-

tests/test_project.py

@@ -18,6 +18,7 @@ def test_requirements_file_matches_pinned_runtime_dependencies() -> None:
 def test_phase_label_is_not_a_numbered_phase() -> None:
     """PHASE_LABEL must not contain a phase number reference."""
     import re
-    assert not re.search(r'\bPhase [0-9]\b', PHASE_LABEL), (
+
+    assert not re.search(r"\bPhase [0-9]\b", PHASE_LABEL), (
         f"PHASE_LABEL should not contain a numbered phase: {PHASE_LABEL!r}"
     )