Update app.py

app.py

@@ -117,22 +117,55 @@ class SafetyMonitor:
         return image
 
     def process_frame(self, frame):
-        """Main processing pipeline for safety analysis."""
+        """Main processing pipeline for dynamic safety analysis."""
         if frame is None:
             return None, "No image provided"
-        
+    
         try:
-            # Detect objects in the image using YOLO
+            # Detect objects dynamically in the image using YOLO
             bbox_data, labels = self.detect_objects(frame)
             frame_with_boxes = self.draw_bounding_boxes(frame, bbox_data, labels)
-
-            # Get analysis from Groq's model
+    
+            # Get dynamic safety analysis from Groq's model
             analysis, _ = self.analyze_frame(frame)
-            return frame_with_boxes, analysis
             
+            # Dynamically parse the analysis to find any safety issues flagged
+            safety_issues = self.parse_safety_analysis(analysis)
+            
+            # Dynamically link detected objects to safety issues
+            for issue in safety_issues:
+                if 'helmet' in issue.lower():
+                    for idx, bbox in enumerate(bbox_data):
+                        x1, y1, x2, y2, conf, class_id = bbox
+                        if labels[int(class_id)] == 'person':
+                            # Dynamically label the missing helmet issue for detected persons
+                            cv2.putText(frame_with_boxes, "No Helmet!", (int(x1), int(y1) - 20), 
+                                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
+                            cv2.rectangle(frame_with_boxes, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
+    
+                # Add more dynamic checks here for gloves, boots, etc.
+                if 'glove' in issue.lower():
+                    for idx, bbox in enumerate(bbox_data):
+                        x1, y1, x2, y2, conf, class_id = bbox
+                        if labels[int(class_id)] == 'person':
+                            # Dynamically label missing gloves for detected persons
+                            cv2.putText(frame_with_boxes, "No Gloves!", (int(x1), int(y1) - 20), 
+                                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
+                            cv2.rectangle(frame_with_boxes, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 255), 2)
+            
+            return frame_with_boxes, analysis
+    
         except Exception as e:
             print(f"Processing error: {str(e)}")
             return None, f"Error processing image: {str(e)}"
+    
+    def parse_safety_analysis(self, analysis):
+        """Dynamically parse the safety analysis to identify issues."""
+        safety_issues = []
+        for line in analysis.split('\n'):
+            if "missing" in line.lower() or "no" in line.lower():
+                safety_issues.append(line.strip())
+        return safety_issues
 
 def create_monitor_interface():
     monitor = SafetyMonitor()