fix : close unclosed SKILL_SYNONYMS dict and remove duplicate parse_skills in recommender

src/utils/certification.py

@@ -1,7 +1,7 @@
 """Learning outcome assessment and certification system."""
 
 from typing import Dict, List, Optional
-from datetime import datetime
+from datetime import datetime, timezone
 import hashlib
 
 
@@ -28,7 +28,7 @@ def create_assessment(
             "skill": skill,
             "difficulty": difficulty,
             "questions": questions,
-            "created_at": datetime.utcnow().isoformat(),
+            "created_at": datetime.now(timezone.utc).isoformat(),
             "score": None,
             "passed": False,
             "completed_at": None,
@@ -48,7 +48,7 @@ def submit_assessment(
 
         assessment["score"] = score
         assessment["passed"] = score >= 70
-        assessment["completed_at"] = datetime.utcnow().isoformat()
+        assessment["completed_at"] = datetime.now(timezone.utc).isoformat()
 
         if assessment["passed"]:
             self._issue_certificate(assessment)
@@ -77,7 +77,7 @@ def _issue_certificate(self, assessment: Dict):
             "skill": assessment["skill"],
             "difficulty": assessment["difficulty"],
             "score": assessment["score"],
-            "issued_at": datetime.utcnow().isoformat(),
+            "issued_at": datetime.now(timezone.utc).isoformat(),
             "valid_until": None,
             "verification_code": self._generate_verification_code(cert_id),
         }
@@ -86,7 +86,7 @@ def _issue_certificate(self, assessment: Dict):
 
     def _generate_verification_code(self, cert_id: str) -> str:
         """Generate unique verification code."""
-        data = f"{cert_id}{datetime.utcnow().isoformat()}".encode()
+        data = f"{cert_id}{datetime.now(timezone.utc).isoformat()}".encode()
         return hashlib.sha256(data).hexdigest()[:16]
 
     def issue_digital_badge(self, user_id: str, skill: str, level: str) -> Dict:
@@ -97,7 +97,7 @@ def issue_digital_badge(self, user_id: str, skill: str, level: str) -> Dict:
             "user_id": user_id,
             "skill": skill,
             "level": level,
-            "issued_at": datetime.utcnow().isoformat(),
+            "issued_at": datetime.now(timezone.utc).isoformat(),
             "icon_url": f"/badges/{skill.lower()}_{level.lower()}.png",
             "description": f"{level} {skill} Badge",
         }

src/utils/code_review.py

@@ -7,7 +7,7 @@
 
 from typing import Dict, List, Optional
 from enum import Enum
-from datetime import datetime
+from datetime import datetime, timezone
 
 
 class ReviewStatus(Enum):
@@ -100,7 +100,7 @@ def submit_code(
             "code": code,
             "language": language,
             "description": description or "",
-            "submitted_at": datetime.utcnow().isoformat(),
+            "submitted_at": datetime.now(timezone.utc).isoformat(),
             "review_status": ReviewStatus.PENDING.value,
             "review_count": 0,
             "metrics": {},
@@ -128,13 +128,13 @@ def start_review(
             raise ValueError(f"Submission {submission_id} not found")
 
         submission = self.submissions[submission_id]
-        review_id = f"review_{submission_id}_{datetime.utcnow().timestamp()}"
+        review_id = f"review_{submission_id}_{datetime.now(timezone.utc).timestamp()}"
 
         review = {
             "review_id": review_id,
             "submission_id": submission_id,
             "reviewer_id": reviewer_id,
-            "started_at": datetime.utcnow().isoformat(),
+            "started_at": datetime.now(timezone.utc).isoformat(),
             "completed_at": None,
             "status": ReviewStatus.IN_PROGRESS.value,
             "overall_score": None,
@@ -178,7 +178,7 @@ def add_feedback_comment(
             "code_snippet": code_snippet,
             "feedback": feedback,
             "severity": severity,
-            "created_at": datetime.utcnow().isoformat(),
+            "created_at": datetime.now(timezone.utc).isoformat(),
         }
 
         self.feedback_comments[review_id].append(comment)
@@ -249,7 +249,7 @@ def complete_review(
         scores = [s["score"] for s in review["category_scores"].values()]
         overall_score = sum(scores) / len(scores) if scores else 0
 
-        review["completed_at"] = datetime.utcnow().isoformat()
+        review["completed_at"] = datetime.now(timezone.utc).isoformat()
         review["overall_score"] = round(overall_score, 2)
         review["summary"] = summary
         review["status"] = (

src/utils/recommender.py

@@ -105,6 +105,7 @@ def clear_caches():
     "ai":            "artificial intelligence",
     "k8s":           "kubernetes",
     "tf":            "tensorflow",
+}
 
 # Common aliases and abbreviations for skills
 # This improves recommendation accuracy by normalizing user input
@@ -165,50 +166,10 @@ def parse_skill_entries(skills_string):
         if s.strip()  # skip blanks produced by trailing / consecutive commas
     ]
     return [SKILL_SYNONYMS.get(token, token) for token in tokens]
-                entries = []
-                for item in parsed:
-                    if isinstance(item, dict):
-                        skill = _normalize_skill(str(item.get("skill", "")))
-                        proficiency = str(item.get("proficiency", "Beginner")).strip().title()
-                    else:
-                        skill = _normalize_skill(str(item))
-                        proficiency = "Beginner"
-
-                    if skill:
-                        entries.append(
-                            {
-                                "skill": SKILL_ALIASES.get(skill, skill),
-                                "proficiency": (
-                                    proficiency
-                                    if proficiency in (
-                                        "Beginner",
-                                        "Intermediate",
-                                        "Advanced",
-                                    )
-                                    else "Beginner"
-                                ),
-                            }
-                        )
-                return entries
-        except (json.JSONDecodeError, ValueError):
-            pass
 
-    return [
-        {
-            "skill": SKILL_ALIASES.get(_normalize_skill(skill), _normalize_skill(skill)),
-            "proficiency": "Beginner",
-        }
-        for skill in skills_string.split(",")
-        if skill.strip()
-    ]
 
 
-def parse_skills(skills_string):
-    return [entry["skill"] for entry in parse_skill_entries(skills_string)]
-
 
-def parse_skills(skills_string):
-    return [entry["skill"] for entry in parse_skill_entries(skills_string)]
 
 _nlp_model = None
 _project_embeddings_cache = {}