Update app.py

app.py

@@ -4,61 +4,60 @@ import cv2
 import numpy as np
 import re
 
-# Initialize OCR reader
+# Initialize EasyOCR reader
 reader = easyocr.Reader(['en'], gpu=False)
 
-# Preprocessing function to enhance OCR accuracy
+# Step 1: Preprocessing Function
 def preprocess(img):
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
-    # Sharpening kernel (helps deblur weak edges)
+    # Apply sharpening for deblurring
     sharpen_kernel = np.array([[-1, -1, -1],
-                                [-1, 9, -1],
+                                [-1,  9, -1],
                                 [-1, -1, -1]])
     sharp = cv2.filter2D(gray, -1, sharpen_kernel)
 
-    # Adaptive thresholding for binarization
-    thresh = cv2.adaptiveThreshold(sharp, 255,
-                                   cv2.ADAPTIVE_THRESH_MEAN_C,
-                                   cv2.THRESH_BINARY_INV, 11, 4)
+    # Apply adaptive thresholding
+    thresh = cv2.adaptiveThreshold(
+        sharp, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 4)
 
-    # Dilation to strengthen character boundaries
+    # Strengthen characters using dilation
     kernel = np.ones((2, 2), np.uint8)
     dilated = cv2.dilate(thresh, kernel, iterations=1)
 
     return dilated
 
-# Extract weight using regex
+# Step 2: Extract weight using regex
 def extract_weight(results):
-    for text, _, _ in results:
-        match = re.search(r'\b\d{2,4}(\.\d{1,2})?\b', text)
-        if match:
-            return match.group()
+    for result in results:
+        text = result[0] if isinstance(result, (list, tuple)) else ""
+        if isinstance(text, str):
+            match = re.search(r'\b\d{2,4}(\.\d{1,2})?\b', text)
+            if match:
+                return match.group()
     return None
 
-# Complete OCR pipeline
+# Step 3: Main OCR Function
 def get_weight(img):
-    # Convert RGB to BGR
     img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
 
-    # Resize small or distant images (heuristic)
+    # Resize small (distant) images
     if img.shape[0] < 500:
-        img = cv2.resize(img, None, fx=2.0, fy=2.0,
-                         interpolation=cv2.INTER_CUBIC)
+        img = cv2.resize(img, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC)
 
-    preprocessed = preprocess(img)
-    results = reader.readtext(preprocessed)
+    processed = preprocess(img)
+    results = reader.readtext(processed)
     weight = extract_weight(results)
 
     return f"Detected Weight: {weight if weight else 'Not found'}"
 
-# Gradio UI
+# Step 4: Gradio Interface
 demo = gr.Interface(
     fn=get_weight,
     inputs=gr.Image(type="numpy", label="Upload Image"),
     outputs=gr.Textbox(label="Detected Weight"),
     title="Auto Weight Logger (Blur & Distance Aware)",
-    description="Extracts weight from digital display images using OCR & preprocessing."
+    description="Extracts weight from distant or blurry display images using OCR + preprocessing."
 )
 
 if __name__ == "__main__":