Update app.py

app.py

@@ -6,42 +6,46 @@ from datetime import datetime
 import re
 from PIL import Image
 
-# Initialize PaddleOCR (only once)
-ocr = PaddleOCR(use_angle_cls=True, lang='en')  # Use English OCR model
+# Initialize PaddleOCR once
+ocr = PaddleOCR(use_angle_cls=True, lang='en')  # English OCR model
 
 def detect_weight(image):
-    if image is None:
-        return "No image uploaded", "N/A", None
-
-    # Convert PIL Image to OpenCV format (NumPy array)
-    image = image.convert("RGB")
-    image_np = np.array(image)
-
-    # Preprocess: Grayscale + contrast enhancement (optional)
-    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-    gray_eq = cv2.equalizeHist(gray)
-    processed_image = cv2.cvtColor(gray_eq, cv2.COLOR_GRAY2RGB)  # convert back to RGB for PaddleOCR
-
-    # Run OCR
-    result = ocr.ocr(processed_image, cls=True)
-
-    best_match = None
-    best_conf = 0
-
-    # Search for a decimal number like 25.52
-    for line in result:
-        for box in line:
-            text, conf = box[1]
-            match = re.search(r"\d+\.\d+", text)
-            if match and conf > best_conf:
-                best_match = match.group()
-                best_conf = conf
-
-    if best_match:
-        now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        return f"Weight: {best_match} kg (Confidence: {round(best_conf * 100, 2)}%)", now, image
-    else:
-        return "No weight detected kg (Confidence: 0.0%)", "N/A", image
+    try:
+        if image is None:
+            return "No image uploaded", "N/A", None
+
+        # Convert PIL image to RGB numpy array
+        image = image.convert("RGB")
+        image_np = np.array(image)
+
+        # Convert to grayscale and enhance contrast
+        gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+        enhanced = cv2.equalizeHist(gray)
+        rgb_image = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB)
+
+        # Perform OCR
+        result = ocr.ocr(rgb_image, cls=True)
+
+        best_match = None
+        best_conf = 0
+
+        for line in result:
+            for box in line:
+                text, conf = box[1]
+                match = re.search(r"\d+\.\d+", text)
+                if match and conf > best_conf:
+                    best_match = match.group()
+                    best_conf = conf
+
+        if best_match:
+            now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            return f"Weight: {best_match} kg (Confidence: {round(best_conf * 100, 2)}%)", now, image
+        else:
+            return "No weight detected kg (Confidence: 0.0%)", "N/A", image
+
+    except Exception as e:
+        # Show error in output instead of crashing
+        return f"Error: {str(e)}", "Error", None
 
 # Gradio UI
 gr.Interface(