Spaces:
Sleeping
Sleeping
File size: 10,970 Bytes
3020394 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 | import json
import tempfile
import unittest
from pathlib import Path
from unittest.mock import patch
import numpy as np
import cv2
from fastapi.testclient import TestClient
from app import main
from monitoring import feedback as feedback_mod
from monitoring import metrics as metrics_mod
class TestFeedbackRecording(unittest.TestCase):
def setUp(self):
self._tmpdir = tempfile.TemporaryDirectory()
self.tmp_path = Path(self._tmpdir.name)
self.feedback_path = self.tmp_path / "feedback.jsonl"
def tearDown(self):
self._tmpdir.cleanup()
def test_record_feedback_appends_entry(self):
entry = feedback_mod.record_feedback(
"abc.jpg", {"prix": 10000, "volume": 14.28},
corrected_by="pompiste-7", feedback_path=self.feedback_path,
)
self.assertEqual(entry["photo_reference"], "abc.jpg")
self.assertFalse(entry["converted"])
entries = feedback_mod.load_feedback_entries(self.feedback_path)
self.assertEqual(len(entries), 1)
self.assertEqual(entries[0]["corrected_fields"]["prix"], 10000)
def test_record_feedback_rejects_unknown_field(self):
with self.assertRaises(ValueError):
feedback_mod.record_feedback(
"abc.jpg", {"montant_total": 10000}, feedback_path=self.feedback_path,
)
def test_record_feedback_rejects_empty_corrections(self):
with self.assertRaises(ValueError):
feedback_mod.record_feedback("abc.jpg", {}, feedback_path=self.feedback_path)
def test_load_feedback_entries_missing_file_returns_empty(self):
entries = feedback_mod.load_feedback_entries(self.tmp_path / "nope.jsonl")
self.assertEqual(entries, [])
class TestFeedbackConversion(unittest.TestCase):
def setUp(self):
self._tmpdir = tempfile.TemporaryDirectory()
self.tmp_path = Path(self._tmpdir.name)
self.photos_dir = self.tmp_path / "photos"
self.photos_dir.mkdir()
self.feedback_path = self.tmp_path / "feedback.jsonl"
self.annotations_path = self.tmp_path / "annotations.json"
self.crops_dir = self.tmp_path / "crops"
img = np.full((200, 400, 3), 30, dtype=np.uint8)
cv2.imwrite(str(self.photos_dir / "photo1.jpg"), img)
def tearDown(self):
self._tmpdir.cleanup()
def test_convert_creates_pending_review_annotation(self):
feedback_mod.record_feedback(
"photo1.jpg", {"prix": 10000, "volume": 14.28, "prix_litre": 700},
corrected_by="pompiste-7", feedback_path=self.feedback_path,
)
count = feedback_mod.convert_feedback_to_annotations(
photos_dir=self.photos_dir, feedback_path=self.feedback_path,
annotations_path=self.annotations_path, crops_dir=self.crops_dir,
)
self.assertEqual(count, 1)
with open(self.annotations_path, "r", encoding="utf-8") as f:
annotations = json.load(f)
self.assertIn("photo1.jpg", annotations)
entry = annotations["photo1.jpg"]
self.assertEqual(entry["status"], "pending_review")
self.assertEqual(entry["fields"]["prix"], "10000")
self.assertEqual(entry["fields"]["volume"], "14.28")
entries = feedback_mod.load_feedback_entries(self.feedback_path)
self.assertTrue(entries[0]["converted"])
def test_convert_is_idempotent_for_already_converted_entries(self):
feedback_mod.record_feedback(
"photo1.jpg", {"prix": 10000}, feedback_path=self.feedback_path,
)
feedback_mod.convert_feedback_to_annotations(
photos_dir=self.photos_dir, feedback_path=self.feedback_path,
annotations_path=self.annotations_path, crops_dir=self.crops_dir,
)
second_count = feedback_mod.convert_feedback_to_annotations(
photos_dir=self.photos_dir, feedback_path=self.feedback_path,
annotations_path=self.annotations_path, crops_dir=self.crops_dir,
)
self.assertEqual(second_count, 0)
def test_convert_skips_missing_photo(self):
feedback_mod.record_feedback(
"ghost.jpg", {"prix": 10000}, feedback_path=self.feedback_path,
)
count = feedback_mod.convert_feedback_to_annotations(
photos_dir=self.photos_dir, feedback_path=self.feedback_path,
annotations_path=self.annotations_path, crops_dir=self.crops_dir,
)
self.assertEqual(count, 0)
class TestMetrics(unittest.TestCase):
def setUp(self):
self._tmpdir = tempfile.TemporaryDirectory()
self.log_path = Path(self._tmpdir.name) / "api.log"
def tearDown(self):
self._tmpdir.cleanup()
def _write_log_line(self, payload):
with open(self.log_path, "a", encoding="utf-8") as f:
f.write(f"2026-07-07 10:00:00,000 | INFO | {json.dumps(payload, ensure_ascii=False)}\n")
def test_compute_metrics_on_missing_log_returns_zero(self):
m = metrics_mod.compute_metrics(self.log_path)
self.assertEqual(m["total_requests"], 0)
self.assertIsNone(m["success_rate"])
def test_compute_metrics_aggregates_success_and_blocking(self):
self._write_log_line({
"model_version": "v2",
"response": {"success": True, "confidence_score": 0.9, "image_quality": "valid", "message": "ok"},
})
self._write_log_line({
"model_version": "v2",
"response": {"success": False, "confidence_score": 0.2, "image_quality": "blurry", "message": "Photo floue, veuillez reprendre la photo."},
})
with open(self.log_path, "a", encoding="utf-8") as f:
f.write("2026-07-07 10:00:01,000 | INFO | Modèle CRNN chargé sur cpu.\n")
m = metrics_mod.compute_metrics(self.log_path)
self.assertEqual(m["total_requests"], 2)
self.assertEqual(m["success_rate"], 0.5)
self.assertAlmostEqual(m["avg_confidence_score"], 0.55)
self.assertEqual(m["blocking_causes"]["Photo floue, veuillez reprendre la photo."], 1)
self.assertEqual(m["image_quality_distribution"]["valid"], 1)
def test_list_failed_requests_returns_only_failures_most_recent_first(self):
self._write_log_line({
"response": {"success": True, "photo_reference": "ok.jpg", "message": "ok"},
})
self._write_log_line({
"response": {"success": False, "photo_reference": "first-fail.jpg",
"message": "Photo floue, veuillez reprendre la photo.",
"image_quality": "blurry", "confidence_score": 0.1},
})
self._write_log_line({
"response": {"success": False, "photo_reference": "second-fail.jpg",
"message": "Incohérence détectée entre montant, litres et prix.",
"image_quality": "valid", "confidence_score": 0.4},
})
failures = metrics_mod.list_failed_requests(self.log_path)
self.assertEqual(len(failures), 2)
self.assertEqual(failures[0]["photo_reference"], "second-fail.jpg")
self.assertEqual(failures[1]["photo_reference"], "first-fail.jpg")
self.assertNotIn("filename", failures[0])
def test_list_failed_requests_respects_limit(self):
for i in range(5):
self._write_log_line({
"response": {"success": False, "photo_reference": f"fail{i}.jpg", "message": "x"},
})
failures = metrics_mod.list_failed_requests(self.log_path, limit=2)
self.assertEqual(len(failures), 2)
class TestFeedbackAndMetricsEndpoints(unittest.TestCase):
def setUp(self):
self.client = TestClient(main.app)
self._tmpdir = tempfile.TemporaryDirectory()
self.fake_photos_dir = Path(self._tmpdir.name)
(self.fake_photos_dir / "photo.jpg").write_bytes(b"fake")
self._patch_photos_dir = patch.object(main, "PHOTOS_DIR", self.fake_photos_dir)
self._patch_photos_dir.start()
def tearDown(self):
self._patch_photos_dir.stop()
self._tmpdir.cleanup()
def test_feedback_rejects_unknown_photo_reference(self):
resp = self.client.post(
"/feedback",
data={"photo_reference": "does-not-exist.jpg", "corrected_prix": "10000"},
)
self.assertEqual(resp.status_code, 404)
def test_feedback_rejects_no_corrections(self):
resp = self.client.post("/feedback", data={"photo_reference": "photo.jpg"})
self.assertEqual(resp.status_code, 400)
def test_feedback_success_records_entry(self):
fake_entry = {"feedback_id": "fake-id"}
with patch.object(main, "record_feedback", return_value=fake_entry) as mock_record:
resp = self.client.post(
"/feedback",
data={"photo_reference": "photo.jpg", "corrected_prix": "10000", "corrected_by": "pompiste-7"},
)
self.assertEqual(resp.status_code, 200)
body = resp.json()
self.assertTrue(body["success"])
self.assertEqual(body["feedback_id"], "fake-id")
mock_record.assert_called_once()
args, kwargs = mock_record.call_args
self.assertEqual(args[0], "photo.jpg")
self.assertEqual(args[1], {"prix": 10000.0})
def test_metrics_endpoint_returns_aggregate(self):
fake_metrics = {"total_requests": 0, "success_rate": None,
"avg_confidence_score": None, "blocking_causes": {},
"image_quality_distribution": {}}
with patch.object(main, "compute_metrics", return_value=fake_metrics):
resp = self.client.get("/metrics")
self.assertEqual(resp.status_code, 200)
self.assertEqual(resp.json(), fake_metrics)
def test_failures_endpoint_returns_list(self):
fake_failures = [{"photo_reference": "photo.jpg", "message": "Photo floue."}]
with patch.object(main, "list_failed_requests", return_value=fake_failures):
resp = self.client.get("/failures")
self.assertEqual(resp.status_code, 200)
self.assertEqual(resp.json(), fake_failures)
def test_get_photo_returns_file(self):
resp = self.client.get("/photos/photo.jpg")
self.assertEqual(resp.status_code, 200)
self.assertEqual(resp.content, b"fake")
def test_get_photo_unknown_reference_returns_404(self):
resp = self.client.get("/photos/does-not-exist.jpg")
self.assertEqual(resp.status_code, 404)
def test_get_photo_rejects_path_traversal(self):
outside_file = Path(self._tmpdir.name).parent / "secret.txt"
outside_file.write_text("should not be servable")
try:
resp = self.client.get("/photos/..%2Fsecret.txt")
self.assertEqual(resp.status_code, 404)
finally:
outside_file.unlink(missing_ok=True)
if __name__ == "__main__":
unittest.main()
|