Update bf_trigger.py

bf_trigger.py

@@ -375,32 +375,61 @@ def create_ai_asset(asset_dict, topical_map, collection_name, new_imgs, tags=Tru
     # container for the uploaded image to be used by the post request
     object_url = r.json()['object_url']
 
-    url_response = urllib.request.urlopen(image_url)
-    img_array = np.array(bytearray(url_response.read()), dtype=np.uint8)
-    image = cv2.imdecode(img_array, -1)
-    try:
-        height, width, c = image.shape
-    except:
-        height, width = image.shape
-    area = width*height
-    if width > height:
-      # landscape image
-      if area > 667000:
-        image = cv2.resize(image, (1000, 667))
-    else:
-      # portrait image
-      if area > 442236:
-        image = cv2.resize(image, (548, 807))
-
-    # image = sharpen_image(image)
-
-    with NamedTemporaryFile(delete=True, suffix='.jpg') as temp_image:
-      # fp = TemporaryFile()
-      cv2.imwrite(temp_image.name, image)
-      # fp.seek(0)
-      response = requests.put(upload_url, data=temp_image)
-    # fp.close()
+    def download_and_resize_image(image_url, upload_url):
+        # Fetch the image from the URL
+        url_response = urllib.request.urlopen(image_url)
+        img_array = np.array(bytearray(url_response.read()), dtype=np.uint8)
+    
+        # Try to decode the image using OpenCV
+        image = cv2.imdecode(img_array, -1)
+    
+        # If the image is None, it might be a HEIC file
+        if image is None:
+            heif_file = read_heif(img_array)
+            img = Image.frombytes(
+                heif_file.mode,
+                heif_file.size,
+                heif_file.data,
+                "raw",
+                heif_file.mode,
+                heif_file.stride
+            )
+            # Convert to RGB
+            img = img.convert('RGB')
+            # Save to a BytesIO object
+            img_bytes = BytesIO()
+            img.save(img_bytes, format='JPEG')
+            img_bytes.seek(0)
+            img_array = np.array(bytearray(img_bytes.read()), dtype=np.uint8)
+            # Decode the JPEG image using OpenCV
+            image = cv2.imdecode(img_array, -1)
+    
+        # Resize the image based on its dimensions and area
+        try:
+            height, width, c = image.shape
+        except:
+            height, width = image.shape
+    
+        area = width * height
+        if width > height:
+            # Landscape image
+            if area > 667000:
+                image = cv2.resize(image, (1000, 667))
+        else:
+            # Portrait image
+            if area > 442236:
+                image = cv2.resize(image, (548, 807))
+    
+        # Save the image to a temporary file and upload it
+        with NamedTemporaryFile(delete=True, suffix='.jpg') as temp_image:
+            cv2.imwrite(temp_image.name, image)
+            temp_image.seek(0)
+            response = requests.put(upload_url, data=temp_image)
+    
+        return response
 
+    response = download_and_resize_image(image_url, upload_url)
+    
     # posts image with image name
 
     r = requests.post(f'https://brandfolder.com/api/v4/collections/{collection_id}/assets', json={