fixed

app/__init__.py

@@ -23,6 +23,12 @@ login.login_view = 'auth.login'
 celery = Celery(__name__)
 cache = None  # Initialize later when app context is available
 
+# Set up user loader for Flask-Login
+@login.user_loader
+def load_user(user_id):
+    from .models import User
+    return User.query.get(int(user_id))
+
 def create_app(config_class=None):
     logger.info("Starting application initialization...")
     

app/routes/compliance.py

@@ -9,6 +9,13 @@ logger = logging.getLogger(__name__)
 
 compliance_bp = Blueprint('compliance', __name__)
 
+@compliance_bp.route('/report', methods=['GET'])
+@login_required
+def compliance_report():
+    """Render the compliance report page."""
+    ads = Ad.query.all()
+    return render_template('compliance_report.html', ads=ads)
+
 @compliance_bp.route('/anonymize/<ad_id>', methods=['POST'])
 @login_required
 @admin_required

app/services/ai_processor.py

@@ -1,6 +1,3 @@
-import cv2
-import pytesseract
-from transformers import pipeline
 from pathlib import Path
 import logging
 
@@ -22,17 +19,29 @@ class AIPipeline:
             if not (weights_path.exists() and config_path.exists()):
                 logger.warning("YOLOv4 files not found. Please run setup_yolo.py first.")
             else:
-                self.detector = cv2.dnn.readNet(str(weights_path), str(config_path))
+                # Lazy load OpenCV
+                self._setup_detector(str(weights_path), str(config_path))
                 
         except Exception as e:
             logger.error(f"Error initializing AI Pipeline: {e}")
             raise
 
+    def _setup_detector(self, weights_path, config_path):
+        """Set up the object detector with the given weights and config."""
+        try:
+            import cv2
+            self.detector = cv2.dnn.readNet(weights_path, config_path)
+        except Exception as e:
+            logger.error(f"Error setting up detector: {e}")
+            self.detector = None
+
     def _ensure_nlp_loaded(self):
         """Ensure NLP model is loaded before use."""
         if self.nlp is None:
             try:
                 logger.info("Loading NLP model...")
+                # Import transformers only when needed
+                from transformers import pipeline
                 self.nlp = pipeline("text-classification", model="roberta-base")
                 logger.info("NLP model loaded successfully")
             except Exception as e:
@@ -68,6 +77,7 @@ class AIPipeline:
         if not media or not hasattr(media, 'type') or media.type != "image":
             return None
         try:
+            import pytesseract
             return pytesseract.image_to_string(media.path)
         except Exception as e:
             logger.error(f"OCR error: {e}")
@@ -77,6 +87,7 @@ class AIPipeline:
         if not media or not hasattr(media, 'type') or media.type != "image" or not self.detector:
             return None
         try:
+            import cv2
             img = cv2.imread(media.path)
             blob = cv2.dnn.blobFromImage(img, 1/255, (416, 416), swapRB=True, crop=False)
             self.detector.setInput(blob)