Update app.py

app.py

@@ -1,39 +1,43 @@
 import gradio as gr
-import easyocr
-import re
 import cv2
 import numpy as np
-from PIL import Image
+from paddleocr import PaddleOCR
 from datetime import datetime
+import re
+from PIL import Image
 
-reader = easyocr.Reader(['en'], gpu=False)
+ocr = PaddleOCR(use_angle_cls=True, lang='en')  # Better for clean numbers
 
 def detect_weight(image):
     if image is None:
         return "No image uploaded", "N/A", None
 
-    # Convert PIL to OpenCV
+    # Convert to OpenCV format
     image_np = np.array(image)
-    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
 
-    # Optional: Enhance contrast
+    # Preprocessing: Convert to grayscale, enhance contrast
+    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
     gray = cv2.equalizeHist(gray)
+    gray = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)  # Convert back to 3 channel for PaddleOCR
+
+    # Run OCR
+    result = ocr.ocr(gray, cls=True)
 
-    # OCR
-    results = reader.readtext(gray, detail=1)
+    best_match = None
+    best_conf = 0
 
-    weight = None
-    max_conf = 0
-    for (bbox, text, conf) in results:
-        text = text.strip()
-        match = re.search(r'\d+\.\d+', text)
-        if match and conf > max_conf:
-            weight = match.group()
-            max_conf = conf
+    for line in result:
+        for box in line:
+            text = box[1][0]
+            conf = box[1][1]
+            match = re.search(r"\d+\.\d+", text)
+            if match and conf > best_conf:
+                best_match = match.group()
+                best_conf = conf
 
-    if weight:
+    if best_match:
         now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        return f"Weight: {weight} kg (Confidence: {round(max_conf*100, 2)}%)", now, image
+        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