🚀 Auto-deploy backend from GitHub (8ef6f40)

analytics.py

@@ -232,7 +232,6 @@ class EnhancedRiskRequest(BaseModel):
     avgQuizScore: float = Field(..., ge=0, le=100)
     attendance: float = Field(..., ge=0, le=100)
     assignmentCompletion: float = Field(..., ge=0, le=100)
-    streak: Optional[int] = 0
     xpGrowthRate: Optional[float] = 0.0
     timeOnPlatform: Optional[float] = 0.0  # hours
     # Optional trend data
@@ -810,7 +809,7 @@ def _build_risk_features(data: EnhancedRiskRequest) -> np.ndarray:
         data.avgQuizScore,
         data.attendance,
         data.assignmentCompletion,
-        data.streak or 0,
+        0,  # streak removed
         data.xpGrowthRate or 0.0,
         data.timeOnPlatform or 0.0,
         data.engagementTrend7d or 0.0,
@@ -871,12 +870,8 @@ def _rule_based_risk(data: EnhancedRiskRequest) -> EnhancedRiskPrediction:
         score -= 10
     if (data.daysSinceLastActivity or 0) >= 7:
         score -= 10
-    if (data.streak or 0) == 0:
-        score -= 5
 
     # Bonuses
-    if (data.streak or 0) >= 7:
-        score += 5
     if (data.engagementTrend7d or 0) > 0:
         score += 5
 
@@ -1151,19 +1146,19 @@ async def train_risk_model(force_retrain: bool = False) -> RiskTrainResponse:
                 if not data:
                     continue
 
-                features = [
-                    data.get("engagementScore", 50),
-                    data.get("avgQuizScore", 50),
-                    data.get("attendance", 80),
-                    data.get("assignmentCompletion", 60),
-                    data.get("streak", 0),
-                    data.get("xpGrowthRate", 0),
-                    data.get("timeOnPlatform", 0),
-                    0.0,  # engagementTrend7d
-                    0.0,  # quizScoreVariance
-                    data.get("consecutiveAbsences", 0),
-                    data.get("daysSinceLastActivity", 0),
-                ]
+                    features = [
+                        data.get("engagementScore", 50),
+                        data.get("avgQuizScore", 50),
+                        data.get("attendance", 80),
+                        data.get("assignmentCompletion", 60),
+                        0,  # streak removed
+                        data.get("xpGrowthRate", 0),
+                        data.get("timeOnPlatform", 0),
+                        0.0,  # engagementTrend7d
+                        0.0,  # quizScoreVariance
+                        data.get("consecutiveAbsences", 0),
+                        data.get("daysSinceLastActivity", 0),
+                    ]
                 X_data.append(features)
 
                 # Determine label from existing riskLevel or compute it
@@ -1260,7 +1255,7 @@ def _generate_synthetic_risk_data(n: int) -> Tuple[np.ndarray, np.ndarray]:
             quiz = np.random.normal(35, 12)
             attendance = np.random.normal(50, 15)
             completion = np.random.normal(35, 15)
-            streak = max(0, int(np.random.normal(1, 2)))
+            # streak removed
             xp_growth = np.random.normal(-0.5, 0.3)
             time_platform = np.random.normal(2, 1)
             trend = np.random.normal(-10, 5)
@@ -1272,7 +1267,7 @@ def _generate_synthetic_risk_data(n: int) -> Tuple[np.ndarray, np.ndarray]:
             quiz = np.random.normal(60, 10)
             attendance = np.random.normal(72, 10)
             completion = np.random.normal(60, 12)
-            streak = max(0, int(np.random.normal(3, 3)))
+            # streak removed
             xp_growth = np.random.normal(0.2, 0.3)
             time_platform = np.random.normal(5, 2)
             trend = np.random.normal(0, 8)
@@ -1284,7 +1279,7 @@ def _generate_synthetic_risk_data(n: int) -> Tuple[np.ndarray, np.ndarray]:
             quiz = np.random.normal(85, 8)
             attendance = np.random.normal(93, 5)
             completion = np.random.normal(88, 8)
-            streak = max(0, int(np.random.normal(10, 5)))
+            # streak removed
             xp_growth = np.random.normal(1.0, 0.4)
             time_platform = np.random.normal(10, 3)
             trend = np.random.normal(5, 5)
@@ -1297,7 +1292,7 @@ def _generate_synthetic_risk_data(n: int) -> Tuple[np.ndarray, np.ndarray]:
             max(0, min(100, quiz)),
             max(0, min(100, attendance)),
             max(0, min(100, completion)),
-            streak,
+            0,  # streak removed
             xp_growth,
             max(0, time_platform),
             trend,

main.py

@@ -2956,7 +2956,6 @@ async def _generate_risk_recommendations_llm(data: EnhancedRiskRequest, result:
         f"engagementScore: {data.engagementScore:.1f}\n"
         f"avgQuizScore: {data.avgQuizScore:.1f}\n"
         f"assignmentCompletion: {data.assignmentCompletion:.1f}\n"
-        f"streak: {int(data.streak or 0)}\n"
         f"daysSinceLastActivity: {int(data.daysSinceLastActivity or 0)}\n"
         f"top_factors: {', '.join(result.top_factors)}"
     )
@@ -11380,8 +11379,6 @@ def _reset_student_testing_data_admin(
         "level": 1,
         "currentXP": 0,
         "totalXP": 0,
-        "streak": 0,
-        "streakHistory": [],
         "atRiskSubjects": [],
         "hasTakenDiagnostic": False,
         "iarAssessmentState": "not_started",
@@ -12783,7 +12780,6 @@ class ProgressEvaluateRequest(BaseModel):
     mastery_level_before: str
     items: List[Dict[str, Any]]
     previous_attempts: int = Field(default=0)
-    current_streak_days: int = Field(default=0)
 
 
 class ProgressEvaluateResponse(BaseModel):
@@ -12833,7 +12829,6 @@ async def evaluate_progress(request: ProgressEvaluateRequest):
             mastery_changed = True
         
         xp_base = 0
-        xp_streak = 0
         xp_mastery = 0
         xp_other = 0
         
@@ -12847,8 +12842,6 @@ async def evaluate_progress(request: ProgressEvaluateRequest):
                 elif diff == "hard":
                     xp_base += 20
         
-        xp_streak = min(30, 5 * request.current_streak_days)
-        
         if mastery_changed:
             xp_mastery = 50
         
@@ -12858,7 +12851,7 @@ async def evaluate_progress(request: ProgressEvaluateRequest):
         if request.previous_attempts >= 1 and score_percent > 60:
             xp_other += 15
         
-        xp_total = xp_base + xp_streak + xp_mastery + xp_other
+        xp_total = xp_base + xp_mastery + xp_other
         
         error_analysis = []
         for item in request.items:
@@ -12915,7 +12908,6 @@ async def evaluate_progress(request: ProgressEvaluateRequest):
                 stats_ref = db.collection("studentProgress").document(request.student_id).collection("stats").document("summary")
                 stats_ref.set({
                     "total_xp": firebase_firestore.Increment(xp_total),
-                    "current_streak_days": request.current_streak_days,
                     "topics_mastered": firebase_firestore.Increment(1) if mastery_changed else firebase_firestore.Increment(0),
                 }, merge=True)
             except Exception as fs_err:
@@ -12926,7 +12918,7 @@ async def evaluate_progress(request: ProgressEvaluateRequest):
             mastery_changed=mastery_changed,
             score_percent=round(score_percent, 1),
             xp_earned=xp_total,
-            xp_breakdown={"base": xp_base, "mastery_bonus": xp_mastery, "streak_bonus": xp_streak, "other": xp_other},
+            xp_breakdown={"base": xp_base, "mastery_bonus": xp_mastery, "other": xp_other},
             badges_unlocked=[],
             performance_feedback=f"You got {correct_count}/{total_items} correct.",
             error_analysis=error_analysis,

routes/diagnostic.py

@@ -717,7 +717,6 @@ async def _save_results(
             "learning_path": risk_profile.get("suggested_learning_path", []),
             "current_topic_index": 0,
             "total_xp": fs.Increment(50 + mastered_count * 10),
-            "current_streak_days": 1,
             "badges": fs.ArrayUnion(["first_assessment"]),
             "topics_mastered": mastered_count,
             "diagnostic_completed": True,

services/user_provisioning_service.py

@@ -185,7 +185,6 @@ class UserProvisioningService:
                     "level": 1,
                     "currentXP": 0,
                     "totalXP": 0,
-                    "streak": 0,
                     "atRiskSubjects": [],
                     "hasTakenDiagnostic": False,
                 }