Refactor scoring weights into dictionary

tests/test_basic.py

@@ -271,6 +271,14 @@ def test_download_code_found():
     assert response.status_code == 200
 
 
+from utils.recommender import SCORING_WEIGHTS
+
+def test_scoring_weights_has_all_keys():
+    """SCORING_WEIGHTS must contain exactly the four expected keys."""
+    expected_keys = {"skill", "level", "interest", "time"}
+    assert set(SCORING_WEIGHTS.keys()) == expected_keys
+
+
 # ============================================================
 # Run tests directly (no pytest required)
 # ============================================================

utils/recommender.py

@@ -10,10 +10,12 @@
 # Scoring weights used by the recommendation engine.
 # Higher weights mean that criterion has more influence
 # on the final recommendation score.
-WEIGHT_SKILL = 3   # highest because they best reflect project compatibility
-WEIGHT_LEVEL = 2   # helps avoid projects that are too easy or too difficult
-WEIGHT_INTEREST = 2   # Interest alignment improves recommendation relevance
-WEIGHT_TIME = 1    # Time availability acts as a smaller tie-breaker factor
+SCORING_WEIGHTS = {
+    "skill":    3,
+    "level":    2,
+    "interest": 2,
+    "time":     1,
+}
 
 
 # Common aliases and abbreviations for skills
@@ -74,17 +76,17 @@ def score_single_project(
     matched_skills = sum(1 for skill in user_skills if skill in project_skills)
     # Add weighted points based on the number of matching skills.
     # More overlapping skills result in a higher recommendation score.
-    score += matched_skills * WEIGHT_SKILL
+    score += matched_skills * SCORING_WEIGHTS["skill"]
 
     # Award points for each additional matching criterion
     if project.get("level", "").lower() == level.lower():
-        score += WEIGHT_LEVEL
+        score += SCORING_WEIGHTS["level"]
 
     if project.get("interest", "").lower() == interest.lower():
-        score += WEIGHT_INTEREST
+        score += SCORING_WEIGHTS["interest"]
 
     if project.get("time", "").lower() == time_availability.lower():
-        score += WEIGHT_TIME
+        score += SCORING_WEIGHTS["time"]
 
     return score