Update app.py

app.py

@@ -6,16 +6,31 @@ import numpy as np
 import gradio as gr
 import os
 
+# Ensure directories exist
+os.makedirs(os.getenv('EASYOCR_MODULE_PATH', '/app/.EasyOCR'), exist_ok=True)
+os.makedirs(os.getenv('YOLO_CONFIG_DIR', '/app/.config/Ultralytics'), exist_ok=True)
+
 # Download pretrained ANPR model (trained to detect license plates)
 ANPR_WEIGHTS = "anpr_yolov8.pt"
 if not os.path.exists(ANPR_WEIGHTS):
+    print(f"Downloading model weights to {ANPR_WEIGHTS}")
     os.system(f"wget -O {ANPR_WEIGHTS} https://github.com/madalinabuzatu/yolov8-license-plate-detection/releases/download/v1.0/best.pt")
 
-# Load YOLO ANPR model
-model = YOLO(ANPR_WEIGHTS)
+# Load YOLO ANPR model with error handling
+try:
+    model = YOLO(ANPR_WEIGHTS)
+    print(f"Successfully loaded YOLO model from {ANPR_WEIGHTS}")
+except Exception as e:
+    print(f"Error loading YOLO model from {ANPR_WEIGHTS}: {str(e)}")
+    raise
 
-# Load OCR reader
-reader = easyocr.Reader(['en'])
+# Load OCR reader with specified model storage directory
+try:
+    reader = easyocr.Reader(['en'], model_storage_directory=os.getenv('EASYOCR_MODULE_PATH', '/app/.EasyOCR'))
+    print("Successfully initialized EasyOCR reader")
+except Exception as e:
+    print(f"Error initializing EasyOCR reader: {str(e)}")
+    raise
 
 def detect_and_read_plate(image):
     results = model(image)
@@ -36,7 +51,6 @@ def detect_and_read_plate(image):
                 cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
                 cv2.putText(image, text, (x1, y1 - 10),
                             cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
-
     return image
 
 demo = gr.Interface(
@@ -46,4 +60,4 @@ demo = gr.Interface(
     title="Automatic Number Plate Recognition (ANPR)",
     description="Upload an image of a car to detect and read its license plate."
 )
-demo.launch(server_name="0.0.0.0", server_port=7860)
+demo.launch(server_name="0.0.0.0", server_port=7860)