refactor: 🛠️ update import paths from import_utils to package_utils and enhance CLI command structure

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>

Author: onuralpszr

docs/cli.md

@@ -1,4 +1,14 @@
-# CLI Commands
+## CLI commands
+
+This page documents the SAHI CLI (the program exposed by `sahi/cli.py`).
+
+### Top-level commands
+
+- `predict` — run sliced/standard prediction pipeline and export results (images, COCO json, pickles)
+- `predict-fiftyone` — run prediction and open results in FiftyOne
+- `coco` — subgroup for COCO-format utilities (evaluate, analyse, convert, slice)
+- `version` — print SAHI package version
+- `env` — print environment and dependency versions
 
 ## `predict` command usage
 

sahi/cli.py

@@ -7,11 +7,13 @@
 from sahi.scripts.coco_error_analysis import analyse
 from sahi.scripts.coco_evaluation import evaluate
 from sahi.scripts.slice_coco import slicer
-from sahi.utils.import_utils import print_environment_info
+from sahi.utils.package_utils import print_environment_info
+from sahi.utils.cli_helper import make_click_command
 
 
-@click.group()
+@click.group(help="SAHI command-line utilities: slicing-aided high-resolution inference and COCO tools")
 def cli():
+    """Top-level click group for SAHI CLI."""
     pass
 
 coco_app = {
@@ -31,90 +33,23 @@ def cli():
     "env": print_environment_info,
 }
 
-def _make_callback(obj):
-    """Return a callable suitable for click.Command:
-    - if obj is callable, call it with whatever click passes;
-    - otherwise print the object (e.g. version string).
-    """
-    if callable(obj):
-        def _cb(*args, **kwargs):
-            return obj(*args, **kwargs)
-    else:
-        def _cb(*args, **kwargs):
-            click.echo(str(obj))
-    return _cb
-
-import inspect
-
-
-def _click_params_from_signature(func):
-    """Create a list of click.Parameter (Argument/Option) objects from a Python
-    callable's signature. This provides a lightweight automatic mapping so
-    CLI options are available without manually writing decorators.
-
-    Rules (simple, pragmatic):
-    - positional parameters without default -> click.Argument (required)
-    - parameters with a default -> click.Option named --param-name
-    - bool defaults -> is_flag option
-    - list/tuple defaults -> multiple option
-    - skip *args/**kwargs and (self, cls)
-    - use annotation or default value to infer type when possible
-    """
-    params = []
-    sig = inspect.signature(func)
-    for name, p in sig.parameters.items():
-        # skip common unrepresentable params
-        if name in ("self", "cls"):
-            continue
-        if p.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
-            # skip *args/**kwargs
-            continue
-
-        if p.default is inspect._empty:
-            # required positional argument
-            params.append(click.Argument([name]))
-        else:
-            opt_name = f"--{name}"
-            # boolean flags
-            if isinstance(p.default, bool):
-                params.append(click.Option([opt_name], is_flag=True, default=p.default, help=f"(auto) default={p.default}"))
-            # lists/tuples -> multiple
-            elif isinstance(p.default, (list, tuple)):
-                params.append(click.Option([opt_name], multiple=True, default=tuple(p.default), help="(auto) multiple"))
-            else:
-                # infer type from annotation or default value
-                param_type = None
-                if p.annotation is not inspect._empty and p.annotation in (int, float, str, bool):
-                    param_type = p.annotation
-                elif p.default is not None:
-                    param_type = type(p.default)
-                else:
-                    param_type = str
-                params.append(click.Option([opt_name], default=p.default, type=param_type, help=f"(auto) default={p.default}"))
-    return params
-
-
-def _make_click_command(name, func):
-    """Build a click.Command for `func`, auto-generating params from signature if callable."""
-    params = _click_params_from_signature(func) if callable(func) else []
-    return click.Command(name, params=params, callback=_make_callback(func))
-
 
 def app() -> None:
     """Cli app."""
-    #fire.Fire(sahi_app)
-    # for loop to add commands to cli
+
     for command_name, command_func in sahi_app.items():
         if isinstance(command_func, dict):
-            # add subcommands
-            sub_cli = click.Group(command_name)
+            # add subcommands (create a named Group with help text)
+            sub_cli = click.Group(command_name, help=f"{command_name} related commands")
             for sub_command_name, sub_command_func in command_func.items():
-                sub_cli.add_command(_make_click_command(sub_command_name, sub_command_func))
+                sub_cli.add_command(make_click_command(sub_command_name, sub_command_func))
             cli.add_command(sub_cli)
         else:
-            cli.add_command(_make_click_command(command_name, command_func))
+            cli.add_command(make_click_command(command_name, command_func))
 
     cli()
 
+
 if __name__ == "__main__":
-    cli()
+    # build the application (register commands) and run
+    app()

sahi/models/base.py

@@ -7,7 +7,7 @@
 from sahi.annotation import Category
 from sahi.logger import logger
 from sahi.prediction import ObjectPrediction
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 from sahi.utils.torch_utils import empty_cuda_cache, select_device
 
 

sahi/models/huggingface.py

@@ -9,7 +9,7 @@
 from sahi.models.base import DetectionModel
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
-from sahi.utils.import_utils import ensure_package_minimum_version
+from sahi.utils.package_utils import ensure_package_minimum_version
 
 
 class HuggingfaceDetectionModel(DetectionModel):

sahi/models/mmdet.py

@@ -9,7 +9,7 @@
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
 from sahi.utils.cv import get_bbox_from_bool_mask, get_coco_segmentation_from_bool_mask
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 
 check_requirements(["torch", "mmdet", "mmcv", "mmengine"])
 

sahi/models/ultralytics.py

@@ -10,7 +10,7 @@
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
 from sahi.utils.cv import get_coco_segmentation_from_bool_mask
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 
 
 class UltralyticsDetectionModel(DetectionModel):

sahi/models/yolov5.py

@@ -8,7 +8,7 @@
 from sahi.models.base import DetectionModel
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
-from sahi.utils.import_utils import check_package_minimum_version
+from sahi.utils.package_utils import check_package_minimum_version
 
 
 class Yolov5DetectionModel(DetectionModel):

sahi/postprocess/combine.py

@@ -7,7 +7,7 @@
 from sahi.logger import logger
 from sahi.postprocess.utils import ObjectPredictionList, has_match, merge_object_prediction_pair
 from sahi.prediction import ObjectPrediction
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 
 
 def batched_nms(predictions: torch.tensor, match_metric: str = "IOU", match_threshold: float = 0.5):

sahi/predict.py

@@ -3,6 +3,17 @@
 import os
 import time
 from collections.abc import Generator
+
+from PIL import Image
+
+from sahi.logger import logger
+from sahi.utils.package_utils import is_available
+
+# TODO: This does nothing for this module. The issue named here does not exist
+# https://github.com/obss/sahi/issues/526
+if is_available("torch"):
+    import torch  # noqa: F401
+
 from functools import cmp_to_key
 
 import numpy as np
@@ -32,7 +43,7 @@
     visualize_object_predictions,
 )
 from sahi.utils.file import Path, increment_path, list_files, save_json, save_pickle
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 
 POSTPROCESS_NAME_TO_CLASS = {
     "GREEDYNMM": GreedyNMMPostprocess,

sahi/scripts/coco2fiftyone.py

@@ -1,8 +1,6 @@
 import time
 from pathlib import Path
 
-import click
-
 from sahi.utils.file import load_json
 
 
@@ -75,7 +73,3 @@ def main(
         print(f"SAHI has successfully launched a Fiftyone app at http://localhost:{fo.config.default_app_port}")
     while 1:
         time.sleep(3)
-
-
-if __name__ == "__main__":
-    fire.Fire(main)