remove CSV inference source type

docs/en/modes/predict.md

@@ -113,7 +113,6 @@ YOLO11 can process different types of input sources for inference, as shown in t
 | [OpenCV](https://www.ultralytics.com/glossary/opencv) | `cv2.imread('image.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)`.                                          |
-| CSV                                                   | `'sources.csv'`                            | `str` or `Path` | CSV 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.                  |
@@ -246,22 +245,6 @@ Below are code examples for using each source type:
         results = model(source)  # list of Results objects
         ```
 
-    === "CSV"
-
-        Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
-        ```python
-        from ultralytics import YOLO
-
-        # Load a pretrained YOLO11n model
-        model = YOLO("yolo11n.pt")
-
-        # Define a path to a CSV file with images, URLs, videos and directories
-        source = "path/to/file.csv"
-
-        # Run inference on the source
-        results = model(source)  # list of Results objects
-        ```
-
     === "video"
 
         Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.