Update app.py

app.py

@@ -42,7 +42,7 @@ def detect_and_read_plate(image):
     logger.info("Starting image processing for license plate detection")
     try:
         detected_texts = []
-        results = model(image, conf=0.1)  # Lower confidence to increase detections
+        results = model(image, conf=0.1)  # Low confidence for broader detection
         logger.info(f"YOLO model returned {len(results)} results")
         
         for result in results:
@@ -52,7 +52,7 @@ def detect_and_read_plate(image):
             
             for box, conf in zip(boxes, confidences):
                 x1, y1, x2, y2 = map(int, box)
-                # Minimal size filter to skip tiny boxes
+                # Minimal size filter
                 if (x2 - x1) < 20 or (y2 - y1) < 10:
                     logger.warning(f"Skipping small box: ({x1}, {y1}, {x2}, {y2})")
                     continue
@@ -70,7 +70,7 @@ def detect_and_read_plate(image):
                     for res in ocr_result:
                         text = res[1]
                         confidence = res[2]
-                        # Light filtering: prefer alphanumeric, avoid long text
+                        # Light filtering: prefer alphanumeric, avoid overly long text
                         if len(text) <= 12 and confidence > 0.2 and re.match(r'^[A-Z0-9\s\-]+$', text):
                             detected_texts.append(f"{text} (conf: {conf:.2f})")
                             logger.info(f"Detected Plate: {text} (YOLO conf: {conf:.2f}, OCR conf: {confidence:.2f})")
@@ -90,7 +90,7 @@ def detect_and_read_plate(image):
 # Create Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Automatic Number Plate Recognition (ANPR)")
-    gr.Markdown("Upload an image of a car to detect and read its license plate.")
+    gr.Markdown("Upload an image of a car to detect and read its license plate. Results may take a few seconds.")
     
     with gr.Row():
         image_input = gr.Image(type="numpy", label="Upload an image of a car")