Add smoke tests for ML detection pipeline

This file contains smoke tests for the ML detection pipeline, ensuring modules are partially implemented safely with warnings for unimplemented features.

Author: Santhosh-p653

ml/tests/test_ml_smoke.py

@@ -0,0 +1,148 @@
+"""
+Smoke tests for the ML detection pipeline.
+Designed to be partial-implementation safe — tests warn instead of
+failing hard when modules are not yet written.
+
+Place this file at: ml/tests/test_ml_smoke.py
+"""
+
+import ast
+import importlib
+import warnings
+from pathlib import Path
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _can_import(module: str) -> bool:
+    try:
+        importlib.import_module(module)
+        return True
+    except (ImportError, ModuleNotFoundError):
+        return False
+
+
+def _warn_not_implemented(feature: str):
+    warnings.warn(
+        f"{feature} not yet implemented — this test will enforce once the module exists.",
+        stacklevel=3,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Structure tests
+# ---------------------------------------------------------------------------
+
+class TestMLStructure:
+
+    def test_ml_directory_exists(self):
+        assert Path("ml").exists(), "ml/ directory must exist"
+
+    def test_no_syntax_errors(self):
+        errors = []
+        for f in Path("ml").rglob("*.py"):
+            try:
+                ast.parse(f.read_text(encoding="utf-8"))
+            except SyntaxError as e:
+                errors.append(f"{f}: {e}")
+        assert not errors, "Syntax errors in ml/:\n" + "\n".join(errors)
+
+
+# ---------------------------------------------------------------------------
+# Dependency checks (warn only — heavy deps like torch can't install in CI)
+# ---------------------------------------------------------------------------
+
+class TestMLDependencies:
+
+    def test_opencv_importable(self):
+        if not _can_import("cv2"):
+            _warn_not_implemented("OpenCV (cv2)")
+            return
+        import cv2
+        assert cv2.__version__, "cv2 should expose a version"
+
+    def test_numpy_importable(self):
+        assert _can_import("numpy"), "numpy must be importable"
+
+    def test_pillow_importable(self):
+        if not _can_import("PIL"):
+            _warn_not_implemented("Pillow (PIL)")
+
+
+# ---------------------------------------------------------------------------
+# Pipeline interface tests (warn if not yet implemented)
+# ---------------------------------------------------------------------------
+
+class TestDetectionPipeline:
+    """
+    Tests for the deepfake detection pipeline.
+    All tests warn gracefully if the pipeline module isn't written yet.
+    Once you implement ml/pipeline.py, these will enforce correctness.
+    """
+
+    def _get_pipeline_module(self):
+        for candidate in ("ml.pipeline", "pipeline", "ml.detector", "detector"):
+            try:
+                return importlib.import_module(candidate)
+            except (ImportError, ModuleNotFoundError):
+                continue
+        return None
+
+    def test_pipeline_module_exists(self):
+        mod = self._get_pipeline_module()
+        if mod is None:
+            _warn_not_implemented("ml/pipeline.py")
+            return
+        assert mod is not None
+
+    def test_detector_class_exists(self):
+        """ml/pipeline.py should expose a DeepfakeDetector class."""
+        mod = self._get_pipeline_module()
+        if mod is None:
+            _warn_not_implemented("DeepfakeDetector class")
+            return
+        assert hasattr(mod, "DeepfakeDetector"), (
+            "pipeline module must expose a DeepfakeDetector class"
+        )
+
+    def test_detector_has_predict_method(self):
+        """DeepfakeDetector must have a predict(image) method."""
+        mod = self._get_pipeline_module()
+        if mod is None:
+            _warn_not_implemented("DeepfakeDetector.predict()")
+            return
+        cls = getattr(mod, "DeepfakeDetector", None)
+        if cls is None:
+            return
+        assert hasattr(cls, "predict"), (
+            "DeepfakeDetector must have a predict() method"
+        )
+
+    def test_predict_returns_dict(self):
+        """predict() should return a dict with at least 'is_fake' and 'confidence'."""
+        import numpy as np
+
+        mod = self._get_pipeline_module()
+        if mod is None:
+            _warn_not_implemented("DeepfakeDetector.predict() return shape")
+            return
+
+        cls = getattr(mod, "DeepfakeDetector", None)
+        if cls is None:
+            return
+
+        try:
+            detector = cls()
+            dummy_image = np.zeros((224, 224, 3), dtype=np.uint8)
+            result = detector.predict(dummy_image)
+        except Exception as e:
+            _warn_not_implemented(f"predict() — raised {e}")
+            return
+
+        assert isinstance(result, dict), "predict() must return a dict"
+        assert "is_fake" in result, "result dict must contain 'is_fake'"
+        assert "confidence" in result, "result dict must contain 'confidence'"
+        assert isinstance(result["confidence"], float), "'confidence' must be a float"
+        assert 0.0 <= result["confidence"] <= 1.0, "'confidence' must be between 0 and 1"