Update app.py

app.py

@@ -14,10 +14,10 @@ model2 = YOLO(model2_path)
 # Initialize PaddleOCR globally to avoid re-initializing on each call
 # For Hugging Face Spaces, ensure necessary PaddleOCR models are downloaded (e.g., within Dockerfile or setup script)
 # Using 'en' for English license plates and disabling angle classification for simplicity
-ocr = PaddleOCR(
-          use_doc_orientation_classify=False,
-          use_doc_unwarping=False,
-          use_textline_orientation=False)
+ocr = PaddleOCR(use_doc_orientation_classify=False,
+                use_doc_unwarping=False,
+                use_textline_orientation=False,
+                lang='en') # Added lang='en' for better English OCR
 
 # Example images paths - these might need adjustment for Hugging Face Spaces
 # If these are locally stored, they need to be part of the uploaded files in your Space.
@@ -33,6 +33,9 @@ def detect_and_annotate(image):
     # Convert PIL image to OpenCV format
     img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
 
+    # Initialize message
+    output_message = ""
+    
     # Start with a copy of the original image for annotations
     annotated_frame = img.copy()
 
@@ -41,71 +44,70 @@ def detect_and_annotate(image):
 
     # IMPORTANT FIX: The .plot() method on results[0] already returns a new image with annotations.
     # We should use this directly as our base annotated_frame.
-    if results and results[0].boxes: # Check if vehicle results exist and have boxes
+    if results and results[0].boxes and len(results[0].boxes) > 0: # Check if vehicle results exist and have boxes
         # Plot vehicle detections. This returns an annotated NumPy array (BGR format).
-        annotated_frame = results[0].plot(line_width=3) 
-
+        annotated_frame = results[0].plot(line_width=3)  
+        
         # License plate detection only if vehicle is detected
         license_plate_results = model2(img)
-    
+
         if license_plate_results and len(license_plate_results[0].boxes) > 0:
             # Iterate through detected license plates if there are multiple
             for license_box_data in license_plate_results[0].boxes:
                 license_box = license_box_data.xyxy[0].cpu().numpy()
                 x1, y1, x2, y2 = map(int, license_box)
-    
+
                 # Draw rectangle for license plate on the annotated frame
                 cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), (255, 0, 0), 3)
-    
+
                 # OCR on license plate region
                 license_plate_region = img[y1:y2, x1:x2]
-    
+
                 try:
                     # IMPORTANT FIX: Pass the NumPy array directly to PaddleOCR's ocr method
                     # This avoids the file read/write error.
-                    ocr_result = ocr.predict(input=[license_plate_region]) 
-    
+                    ocr_result = ocr.ocr(img=license_plate_region, cls=True) # Changed from .predict to .ocr
+
                     license_plate_text = ""
-                    if ocr_result and ocr_result[0]:
-                        text = ocr_result[0]['rec_texts'][0]
+                    if ocr_result and ocr_result[0] and ocr_result[0][0]: # Check for valid OCR result structure
+                        # The OCR result structure for ocr.ocr is different: it's a list of blocks, each containing [bbox, (text, score)]
+                        # We are interested in the text from the first block's first item
+                        text = ocr_result[0][0][1][0] 
                         text = re.sub(r'[^a-zA-Z0-9]', '', text)
                         license_plate_text = text
-                    
-                    display_text = license_plate_text if license_plate_text else "No text found"
+                        output_message += f"Detected License Plate Text: {license_plate_text}\n"
+                    else:
+                        output_message += "No text found on license plate.\n"
+                        
+                    # Put text on the annotated frame (still show on image for visual feedback)
+                    display_text_on_image = license_plate_text if license_plate_text else "No text found"
+                    cv2.putText(annotated_frame, display_text_on_image, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+
                 except Exception as e:
-                    display_text = f"OCR Error: {e}"
+                    output_message += f"OCR Error: {e}\n"
                     print(f"OCR error on region: {e}")
-    
-                # Put text on the annotated frame
-                cv2.putText(annotated_frame, display_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-
-    else:
-        # If no vehicles are detected, start with a copy of the original image
-        annotated_frame = img.copy()
-        # Add text to indicate no vehicles were found
-        h, w, _ = annotated_frame.shape
-        text_message = "No vehicles detected."
-        font_scale = 1.0
-        font_thickness = 2
-        text_size = cv2.getTextSize(text_message, cv2.FONT_HERSHEY_SIMPLEX, font_scale, font_thickness)[0]
-        text_x = (w - text_size[0]) // 2
-        text_y = (h + text_size[1]) // 2
-        cv2.putText(annotated_frame, text_message, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 255), font_thickness, cv2.LINE_AA)
-
+        else:
+            output_message = "No number plate detected in vehicle.\n"
+    else:         
+        output_message = "No vehicles detected."
+        # If no vehicles are detected, simply return the original image (or a copy)
+        # as there's no need to annotate it with a "No vehicles detected" message on the image itself.
+        annotated_frame = img.copy() # Ensure we always return an image
 
     # Convert back to RGB for display in Gradio
     annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
-    return annotated_frame
+    return annotated_frame, output_message
 
 iface = gr.Interface(
     fn=detect_and_annotate,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="numpy"),
+    outputs=[gr.Image(type="numpy", label="Annotated Image"), 
+             gr.Textbox(label="Detection Messages")], # Added Textbox for messages
     title="Vehicle & License Plate Detection with OCR",
-    description="Upload an image. The app will detect vehicles and license plates, perform OCR, and display the result with annotations.",
+    description="Upload an image. The app will detect vehicles and license plates, perform OCR, and display the result with annotations and messages.",
     examples=example_images,
     cache_examples=False
 )
 
 if __name__ == "__main__":
-    iface.launch(share=True)
+    iface.launch(share=True)