yolov12 doesnt belong in this PR

Author: tonylampada

roboflow/util/model_processor.py

@@ -58,9 +58,6 @@ def _get_processor_function(model_type: str) -> Callable:
     if "yolonas" in model_type:
         return _process_yolonas
 
-    if "yolov12" in model_type:
-        return _process_yolov12
-
     if "rfdetr" in model_type:
         return _process_rfdetr
 
@@ -227,46 +224,6 @@ def _process_yolo(model_type: str, model_path: str, filename: str) -> str:
     return zip_file_name
 
 
-def _process_yolov12(model_type: str, model_path: str, filename: str) -> str:
-    # For YOLOv12, since it uses a special Ultralytics version,
-    # state dict extraction and model artifacts are handled during model conversion
-
-    print(
-        "Note: Model must be trained using ultralytics from https://github.com/sunsmarterjie/yolov12 "
-        "or through the Roboflow platform"
-    )
-
-    # Check if model_path exists
-    if not os.path.exists(model_path):
-        raise FileNotFoundError(f"Model path {model_path} does not exist.")
-
-    # Find any .pt file in model path
-    model_files = os.listdir(model_path)
-    pt_file = next((f for f in model_files if f.endswith(".pt")), None)
-
-    if pt_file is None:
-        raise RuntimeError("No .pt model file found in the provided path")
-
-    # Copy the .pt file to weights.pt if not already named weights.pt
-    if pt_file != "weights.pt":
-        shutil.copy(os.path.join(model_path, pt_file), os.path.join(model_path, "weights.pt"))
-
-    required_files = ["weights.pt"]
-
-    optional_files = ["results.csv", "results.png", "model_artifacts.json"]
-
-    zip_file_name = "roboflow_deploy.zip"
-    with zipfile.ZipFile(os.path.join(model_path, zip_file_name), "w") as zipMe:
-        for file in required_files:
-            zipMe.write(os.path.join(model_path, file), arcname=file, compress_type=zipfile.ZIP_DEFLATED)
-
-        for file in optional_files:
-            if os.path.exists(os.path.join(model_path, file)):
-                zipMe.write(os.path.join(model_path, file), arcname=file, compress_type=zipfile.ZIP_DEFLATED)
-
-    return zip_file_name
-
-
 def _process_rfdetr(model_type: str, model_path: str, filename: str) -> str:
     _supported_types = ["rfdetr-base", "rfdetr-large"]
     if model_type not in _supported_types: