clean

app.py

@@ -1,16 +1,3 @@
-"""
-Engine Part CV System — v5
-══════════════════════════
-Key upgrades over v4:
-  1. PCA projection  — compresses 1536-D CNN space to N most discriminative
-                        dimensions, so cosine gaps widen from 0.007 → 0.1+
-  2. Anomaly scoring — primary signal is "distance from Perfect centroid"
-                        rather than multi-class cosine race
-  3. Per-dim variance weighting (whitening) — equalises feature scales
-  4. Mahalanobis-style distance — accounts for within-class spread per axis
-  5. Gradio 6.0 fix  — theme/css moved to launch()
-"""
-
 import gradio as gr
 import cv2
 import numpy as np
@@ -35,8 +22,8 @@ CLUSTER_VERSION  = "v5"
 TEXTURE_WEIGHT   = 1.6
 MIN_SAMPLES_WARN = 5
 MIN_MATCH_SAMPLES= 3
-PCA_COMPONENTS   = 64      # reduce 1536-D → 64-D  (tune: 32–128)
-ANOMALY_THRESHOLD= 2.5     # mahalanobis z-score above this → FAIL
+PCA_COMPONENTS   = 64      
+ANOMALY_THRESHOLD= 2.5     
 PERFECT_CLASS    = "Perfect"
 
 
@@ -192,20 +179,7 @@ class FeatureExtractor:
 # ───────────────────────────────────────────────────────────────────────────────
 
 class PCAProjector:
-    """
-    Fits a PCA on ALL stored feature vectors across ALL classes, then
-    projects every query into the lower-dimensional discriminative subspace.
-
-    Why this fixes the 0.9944 / 0.9865 cosine collapse
-    ───────────────────────────────────────────────────
-    In 1536-D space virtually every unit vector has cosine similarity ≥ 0.98
-    to every other — this is the "curse of dimensionality".  PCA finds the
-    axes of maximum variance in the training data.  These axes correspond
-    to the visual differences BETWEEN classes (colour, texture, defect edges).
-    After projecting to 64-D the cosine gap between Perfect and Defected
-    typically widens from 0.007 → 0.10–0.30, making classification reliable.
-    """
-
+    
     def __init__(self, n_components: int = PCA_COMPONENTS):
         self.n_components = n_components
         self.pca    = None
@@ -393,11 +367,7 @@ class EnginePartDetector:
         return float(np.dot(a,b)/(na*nb)) if na>1e-8 and nb>1e-8 else 0.
 
     def _mahalanobis(self, query: np.ndarray, name: str) -> float:
-        """
-        Diagonal Mahalanobis distance from query to class centroid.
-        Accounts for per-axis spread — features with high variance within
-        a class contribute less to the distance score.
-        """
+       
         centroid  = self.centroids[name]
         cov_inv   = self.class_cov_inv.get(name)
         diff      = query - centroid
@@ -407,11 +377,7 @@ class EnginePartDetector:
             return float(np.linalg.norm(diff))
 
     def _anomaly_score(self, query_proj: np.ndarray) -> dict:
-        """
-        Primary decision signal: z-score distance from the Perfect centroid.
-        Lower = more like a Perfect part.
-        Returns dict with anomaly_z, perfect_dist, verdict.
-        """
+       
         if PERFECT_CLASS not in self.centroids:
             return {"anomaly_z": None, "verdict": "no_perfect_class"}
 
@@ -815,10 +781,6 @@ with gr.Blocks(title="Engine Part CV System v5") as demo:
         rst_btn.click(reset_all_ui, [], [rst_st, lib_txt, lib_roi])
         demo.load(update_library_preview, [], [lib_txt, lib_roi])
 
-    gr.Markdown("""<div class="footer">
-    Engine Part CV System v5 • PCA + Anomaly Scoring + Centroid Cosine
-    </div>""")
-
 
 if __name__ == "__main__":
     demo.launch(