Update app.py

app.py

@@ -77,6 +77,44 @@ def init_model():
         MODEL = None
 
 # -------------------- PREPROCESSING --------------------
+def calculate_entropy(img_array):
+    """
+    Calculates Shannon Entropy to measure image texture complexity.
+    Returns a float value (higher = more complex/diseased features).
+    """
+    try:
+        # Ensure image is 0-255 uint8 for histogram calculation
+        if img_array.dtype != np.uint8:
+            # If normalized 0-1, scale up. If just float 0-255, cast.
+            if np.max(img_array) <= 1.0:
+                calc_img = (img_array * 255).astype(np.uint8)
+            else:
+                calc_img = img_array.astype(np.uint8)
+        else:
+            calc_img = img_array
+
+        # Convert to grayscale if it's color (Batch dim, H, W, C) or (H, W, C)
+        if len(calc_img.shape) == 4: # (1, 224, 224, 3)
+            calc_img = calc_img[0]
+        
+        gray = cv2.cvtColor(calc_img, cv2.COLOR_RGB2GRAY)
+        
+        # Compute histogram
+        hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
+        
+        # Normalize histogram to get probabilities
+        hist_norm = hist.ravel() / hist.sum()
+        
+        # Filter zero values to avoid log(0) error
+        hist_norm = hist_norm[hist_norm > 0]
+        
+        # Entropy formula: -Sum(p * log2(p))
+        entropy_val = -np.sum(hist_norm * np.log2(hist_norm))
+        return float(entropy_val)
+    except Exception as e:
+        print(f"Entropy Error: {e}")
+        return 4.5 # Fallback average value
+
 def process_single_image(image_path):
     try:
         img = load_img(image_path, target_size=IMG_SIZE)
@@ -146,6 +184,9 @@ def analyze():
         idx = np.argmax(preds)
         label = CLASSES[idx]
         conf = preds[idx] * 100
+        
+        # 4. Calculate Real Entropy
+        entropy_val = calculate_entropy(input_data)
 
         # FORMAT RESULT
         mapping = {
@@ -160,7 +201,7 @@ def analyze():
             "diagnosis": diag, "severity": sev, "color": col, "icon": icon,
             "description": f"AI Analysis Result: {diag}",
             "confidence": f"{conf:.1f}%",
-            "features": {"entropy": "Verified"}
+            "features": {"entropy": f"{entropy_val:.3f}"}
         })
 
     except Exception as e: