Update app.py

app.py

@@ -66,14 +66,14 @@ def preprocess_image(img_cv):
     try:
         # Convert to grayscale
         gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
-        # Enhance contrast
+        # Enhance contrast with CLAHE (Contrast Limited Adaptive Histogram Equalization)
         clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
         contrast = clahe.apply(gray)
         # Reduce noise with Gaussian blur
         blurred = cv2.GaussianBlur(contrast, (5, 5), 0)
-        # Apply adaptive thresholding
+        # Apply adaptive thresholding for better binary image representation
         thresh = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
-        # Sharpen the image
+        # Sharpen the image to bring out more details in the numbers
         kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
         sharpened = cv2.filter2D(thresh, -1, kernel)
         return sharpened
@@ -97,7 +97,7 @@ def detect_roi(img_cv):
         # Find the largest contour (assuming it’s the display)
         largest_contour = max(contours, key=cv2.contourArea)
         x, y, w, h = cv2.boundingRect(largest_contour)
-        # Add padding
+        # Add padding to the detected region to ensure weight is fully captured
         padding = 10
         x = max(0, x - padding)
         y = max(0, y - padding)
@@ -239,4 +239,4 @@ with gr.Blocks(title="⚖️ Auto Weight Logger") as demo:
     """)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()