Update app.py

app.py

@@ -3,61 +3,82 @@ import easyocr
 import cv2
 import numpy as np
 import re
+import os
 
-# Initialize EasyOCR reader
+# Initialize EasyOCR Reader
 reader = easyocr.Reader(['en'], gpu=False)
 
-# Step 1: Preprocessing Function
+# Save processed image for inspection
+DEBUG_SAVE_IMAGE = True  # Turn this off in production
+
+# Preprocessing Function
 def preprocess(img):
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-    # Apply sharpening for deblurring
+    
+    # Sharpen for deblurring
     sharpen_kernel = np.array([[-1, -1, -1],
                                 [-1,  9, -1],
                                 [-1, -1, -1]])
     sharp = cv2.filter2D(gray, -1, sharpen_kernel)
-
-    # Apply adaptive thresholding
+    
+    # Adaptive Thresholding
     thresh = cv2.adaptiveThreshold(
-        sharp, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 4)
+        sharp, 255,
+        cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+        cv2.THRESH_BINARY_INV, 13, 2
+    )
 
-    # Strengthen characters using dilation
+    # Dilate to enhance characters
     kernel = np.ones((2, 2), np.uint8)
     dilated = cv2.dilate(thresh, kernel, iterations=1)
 
+    if DEBUG_SAVE_IMAGE:
+        os.makedirs("debug", exist_ok=True)
+        cv2.imwrite("debug/processed_debug.png", dilated)
+
     return dilated
 
-# Step 2: Extract weight using regex
+# Extract weight with regex
 def extract_weight(results):
     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)
+            match = re.search(r'\b\d{2,4}(\.\d{1,2})?\s?(kg|lb|KG|LB)?\b', text)
             if match:
                 return match.group()
     return None
 
-# Step 3: Main OCR Function
+# Main logic
 def get_weight(img):
     img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
 
-    # Resize small (distant) images
+    # Upscale low-res images (distant capture)
     if img.shape[0] < 500:
         img = cv2.resize(img, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC)
 
-    processed = preprocess(img)
-    results = reader.readtext(processed)
+    pre = preprocess(img)
+
+    # Try OCR
+    results = reader.readtext(pre)
+    print("📝 OCR Results:", results)  # Log what OCR sees
+
+    # Fallback: try raw image if nothing found
     weight = extract_weight(results)
+    if not weight:
+        print("🔁 Trying fallback: raw image")
+        fallback_results = reader.readtext(img)
+        print("📝 OCR Fallback:", fallback_results)
+        weight = extract_weight(fallback_results)
 
     return f"Detected Weight: {weight if weight else 'Not found'}"
 
-# Step 4: Gradio Interface
+# Gradio UI
 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 distant or blurry display images using OCR + preprocessing."
+    title="Auto Weight Logger (Smart OCR)",
+    description="Extracts weight even from blurry or distant display images."
 )
 
 if __name__ == "__main__":