Update app.py

app.py

@@ -37,12 +37,16 @@ def infer_activity_and_phrase(obj, box, img_height):
     Dynamically infer a construction activity and generate a descriptive phrase based on the detected object,
     using object name, bounding box size, and position.
     Args:
-        obj (str): Detected COCO object (e.g., 'truck', 'crane').
+        obj (str): Detected COCO object (e.g., 'truck', 'crane', 'person').
         box (list): Bounding box [x1, y1, x2, y2, conf, cls].
         img_height (int): Image height for position analysis.
     Returns:
         tuple: (activity, phrase) or (None, None) if unmapped.
     """
+    # If the object is a person, label it as a worker
+    if obj.lower() == 'person':
+        obj = 'worker'  # Change label from person to worker
+
     # Normalize object name
     obj_lower = obj.lower().replace('_', ' ')
     
@@ -50,6 +54,19 @@ def infer_activity_and_phrase(obj, box, img_height):
     if len(obj_lower) <= 4 or any(suffix in obj_lower for suffix in ['cat', 'dog', 'bird', 'fish']):
         return None, None
 
+    # Define common construction materials
+    materials = ['cement', 'concrete', 'bricks', 'iron', 'steel', 'sand', 'mortar', 'wood', 'plaster']
+
+    # If object is a material, adjust activity accordingly
+    if any(material in obj_lower for material in materials):
+        base_name = obj_lower.title()
+        activity = f"{base_name} pouring"
+        location = "work zone"  # You can be more specific based on detection
+        verb = random.choice(["observed", "detected", "noted"])
+        level = "ground level"  # Adjust the level based on detection position
+        phrase = f"{activity} {verb} at the {level}."
+        return activity, phrase
+
     # Extract bounding box info
     x1, y1, x2, y2, conf = box[:5]
     box_area = (x2 - x1) * (y2 - y1)  # Area of bounding box
@@ -63,7 +80,7 @@ def infer_activity_and_phrase(obj, box, img_height):
     base_name = obj_lower.title()
     
     # Synonym lists for activity variation (added more activities)
-    action_terms = ['Operation', 'Handling', 'Setup', 'Installation', 'Preparation', 'Transport', 'Lifting', 'Surveying', 'Piling', 'Concrete Work', 'Flooring', 'Finishing']
+    action_terms = ['Operation', 'Handling', 'Setup', 'Installation', 'Preparation', 'Transport', 'Lifting', 'Surveying', 'Piling', 'Concrete Work', 'Flooring', 'Finishing', 'Marking', 'Surveying']
     location_terms = ['site area', 'work zone', 'structural zone', 'foundation level', 'elevated area', 'safety zone', 'concrete pouring zone', 'flooring zone']
 
     # Categorize based on size and position
@@ -285,7 +302,7 @@ iface = gr.Interface(
         gr.File(label="Download PDF")
     ],
     title="Daily Progress Report Generator",
-    description="Upload up to 10 site photos. The AI model dynamically detects objects using YOLOv8 and infers construction activities like concrete work, flooring, piling, and finishing based on object names, sizes, and positions. The DPR is saved as a PDF and uploaded to Salesforce.",
+    description="Upload up to 10 site photos. The AI model dynamically detects objects using YOLOv8 and infers construction activities like concrete work, flooring, piling, finishing, and more based on object names, sizes, and positions. The DPR is saved as a PDF and uploaded to Salesforce.",
     allow_flagging="never"
 )