feat: add cog doctor command

docs/cli.md

@@ -66,6 +66,24 @@ cog build [flags]
       --use-cog-base-image           Use pre-built Cog base image for faster cold boots (default true)
       --use-cuda-base-image string   Use Nvidia CUDA base image, 'true' (default) or 'false' (use python base image). False results in a smaller image but may cause problems for non-torch projects (default "auto")
 ```
+## `cog doctor`
+
+Diagnose and fix common issues in your Cog project.
+
+By default, cog doctor reports problems without modifying any files.
+Pass --fix to automatically apply safe fixes.
+
+```
+cog doctor [flags]
+```
+
+**Options**
+
+```
+  -f, --file string   The name of the config file. (default "cog.yaml")
+      --fix           Automatically apply fixes
+  -h, --help          help for doctor
+```
 ## `cog exec`
 
 Execute a command inside a Docker environment defined by cog.yaml.

integration-tests/tests/doctor_clean_project.txtar

@@ -0,0 +1,17 @@
+# Test that cog doctor succeeds on a valid project with no issues.
+
+cog doctor
+stderr 'no issues found'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+predict: "predict.py:Predictor"
+
+-- predict.py --
+from cog import BasePredictor
+
+
+class Predictor(BasePredictor):
+    def predict(self, text: str) -> str:
+        return "hello " + text

integration-tests/tests/doctor_deprecated_fields.txtar

@@ -0,0 +1,21 @@
+# Test that cog doctor reports deprecated config fields as warnings.
+# Warnings do not cause a non-zero exit code; only errors do.
+
+cog doctor
+stderr 'python_packages'
+stderr 'deprecated'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+  python_packages:
+    - torch==2.0.0
+predict: "predict.py:Predictor"
+
+-- predict.py --
+from cog import BasePredictor
+
+
+class Predictor(BasePredictor):
+    def predict(self, text: str) -> str:
+        return "hello " + text

integration-tests/tests/doctor_deprecated_imports.txtar

@@ -0,0 +1,23 @@
+# Test that cog doctor detects deprecated imports.
+# ExperimentalFeatureWarning was removed in cog 0.17.
+
+! cog doctor
+stderr 'ExperimentalFeatureWarning'
+stderr 'removed'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+predict: "predict.py:Predictor"
+
+-- predict.py --
+import warnings
+from cog import BasePredictor
+from cog.types import ExperimentalFeatureWarning
+
+warnings.filterwarnings("ignore", category=ExperimentalFeatureWarning)
+
+
+class Predictor(BasePredictor):
+    def predict(self, text: str) -> str:
+        return "hello " + text

integration-tests/tests/doctor_exit_code.txtar

@@ -0,0 +1,10 @@
+# Test that cog doctor exits with code 1 when predict file is missing.
+
+! cog doctor
+stderr 'predict.py'
+stderr 'not found'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+predict: "predict.py:Predictor"

integration-tests/tests/doctor_fix_deprecated_imports.txtar

@@ -0,0 +1,35 @@
+# Test that cog doctor --fix removes deprecated imports.
+
+# First, doctor should detect the issue
+! cog doctor
+stderr 'ExperimentalFeatureWarning'
+
+# Fix the issue
+cog doctor --fix
+stderr 'Fixed'
+
+# Verify the import was removed from the file
+exec cat predict.py
+! stdout 'ExperimentalFeatureWarning'
+! stdout 'cog.types'
+
+# Re-running doctor should now pass
+cog doctor
+stderr 'no issues found'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+predict: "predict.py:Predictor"
+
+-- predict.py --
+import warnings
+from cog import BasePredictor
+from cog.types import ExperimentalFeatureWarning
+
+warnings.filterwarnings("ignore", category=ExperimentalFeatureWarning)
+
+
+class Predictor(BasePredictor):
+    def predict(self, text: str) -> str:
+        return "hello " + text

integration-tests/tests/doctor_fix_pydantic.txtar

@@ -0,0 +1,41 @@
+# Test that cog doctor --fix rewrites pydantic.BaseModel to cog.BaseModel.
+# After fix, re-running cog doctor should pass.
+
+# First, doctor should detect the issue
+! cog doctor
+stderr 'pydantic.BaseModel'
+
+# Fix the issue
+cog doctor --fix
+stderr 'Fixed'
+
+# Verify the file was modified: pydantic.BaseModel replaced with cog.BaseModel
+exec cat predict.py
+stdout 'from cog import BasePredictor, Path, BaseModel'
+! stdout 'from pydantic import BaseModel'
+! stdout 'ConfigDict'
+! stdout 'arbitrary_types_allowed'
+
+# Re-running doctor should now pass
+cog doctor
+stderr 'no issues found'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+predict: "predict.py:Predictor"
+
+-- predict.py --
+from cog import BasePredictor, Path
+from pydantic import BaseModel, ConfigDict
+
+
+class VoiceCloningOutputs(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    audio: Path
+    spectrogram: Path
+
+
+class Predictor(BasePredictor):
+    def predict(self, text: str) -> VoiceCloningOutputs:
+        return VoiceCloningOutputs(audio="a.wav", spectrogram="s.png")

integration-tests/tests/doctor_missing_predict_ref.txtar

@@ -0,0 +1,19 @@
+# Test that cog doctor detects when predict ref points to a nonexistent class.
+# cog.yaml references DoesNotExist but only Predictor exists in predict.py.
+
+! cog doctor
+stderr 'DoesNotExist'
+stderr 'not found'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+predict: "predict.py:DoesNotExist"
+
+-- predict.py --
+from cog import BasePredictor
+
+
+class Predictor(BasePredictor):
+    def predict(self, text: str) -> str:
+        return "hello " + text

integration-tests/tests/doctor_pydantic_basemodel.txtar

@@ -0,0 +1,26 @@
+# Test that cog doctor detects pydantic.BaseModel with arbitrary_types_allowed.
+# This is a common workaround that should use cog.BaseModel instead.
+
+! cog doctor
+stderr 'pydantic.BaseModel'
+stderr 'cog.BaseModel'
+
+-- cog.yaml --
+build:
+  python_version: "3.12"
+predict: "predict.py:Predictor"
+
+-- predict.py --
+from cog import BasePredictor, Path
+from pydantic import BaseModel, ConfigDict
+
+
+class VoiceCloningOutputs(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    audio: Path
+    spectrogram: Path
+
+
+class Predictor(BasePredictor):
+    def predict(self, text: str) -> VoiceCloningOutputs:
+        return VoiceCloningOutputs(audio="a.wav", spectrogram="s.png")