Update docs

docs/en/modes/predict.md

@@ -113,7 +113,8 @@ YOLOv8 can process different types of input sources for inference, as shown in t
 | OpenCV          | `cv2.imread('im.jpg')`                     | `np.ndarray`    | HWC format with BGR channels `uint8 (0-255)`.                                               |
 | numpy           | `np.zeros((640,1280,3))`                   | `np.ndarray`    | HWC format with BGR channels `uint8 (0-255)`.                                               |
 | torch           | `torch.zeros(16,3,320,640)`                | `torch.Tensor`  | BCHW format with RGB channels `float32 (0.0-1.0)`.                                          |
-| txt             | `'sources.txt'`                            | `str` or `Path` | txt file containing paths to images, videos, or directories.                                |
+| CSV             | `'sources.csv'`                            | `str` or `Path` | CSV file containing paths to images, videos, or directories.
+| TXT             | `'sources.txt'`                            | `str` or `Path` | TXT file containing paths to images, videos, or directories.                               |
 | video ✅        | `'video.mp4'`                              | `str` or `Path` | Video file in formats like MP4, AVI, etc.                                                   |
 | directory ✅    | `'path/'`                                  | `str` or `Path` | Path to a directory containing images or videos.                                            |
 | glob ✅         | `'path/*.jpg'`                             | `str`           | Glob pattern to match multiple files. Use the `*` character as a wildcard.                  |
@@ -245,16 +246,31 @@ Below are code examples for using each source type:
         results = model(source)  # list of Results objects
         ```
 
-    === "txt"
+    === "CSV"
 
-        Run inference on a collection of images, videos and directories listed in a txt file.
+        Run inference on a collection of images, videos and directories listed in a CSV file.
         ```python
         from ultralytics import YOLO
 
         # Load a pretrained YOLOv8n model
         model = YOLO("yolov8n.pt")
 
-        # Define a path to a txt file with images, videos and directories
+        # Define a path to a CSV file with images, videos and directories
+        source = "path/to/file.csv"
+
+        # Run inference on the source
+        results = model(source)  # list of Results objects
+
+    === "TXT"
+
+        Run inference on a collection of images, videos and directories listed in a TXT file.
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained YOLOv8n model
+        model = YOLO("yolov8n.pt")
+
+        # Define a path to a TXT file with images, videos and directories
         source = "path/to/file.txt"
 
         # Run inference on the source

ultralytics/engine/predictor.py

@@ -8,6 +8,7 @@ Usage - sources:
                                                 vid.mp4                         # video
                                                 screen                          # screenshot
                                                 path/                           # directory
+                                                list.csv                        # list of images, videos, directories
                                                 list.txt                        # list of images, videos, directories
                                                 list.streams                    # list of streams
                                                 'path/*.jpg'                    # glob