Update main.py

main.py

@@ -21,7 +21,7 @@ from .explanation_builder import build_explanation_from_rules
 app = FastAPI(
     title="GCOMPRO",
     description="API Prediksi Risiko Akademik dan Rekomendasi Mata Kuliah.",
-    version="1.5.0" 
+    version="1.6.0" 
 )
 
 # ======================================================================
@@ -60,6 +60,7 @@ class PredictionResponse(BaseModel):
 class RecommendationRequest(BaseModel):
     current_semester: int
     courses_passed: List[str]
+    mk_pilihan_failed: List[str] = [] # <--- Berisi kode spesifik (misal: "AAK4ABB3")
 
 class PrerequisiteInfo(BaseModel):
     code: str
@@ -80,6 +81,38 @@ class CourseRecommendation(BaseModel):
 # 3. Variabel Global & Pemuatan Model/Data
 # ======================================================================
 
+# <--- [UPDATE] DATABASE HARDCODE MATA KULIAH PILIHAN
+# Ini digunakan untuk lookup nama resmi saat user mengirim kode MK Pilihan yang gagal
+ELECTIVE_COURSES_DB = {
+    "AAK4ABB3": {"name": "New Generation Network", "sks": 3},
+    "AAK4BBB3": {"name": "Software Defined Network", "sks": 3},
+    "AAK4CBB3": {"name": "Rekayasa Jaringan", "sks": 3},
+    "AAK4DBB3": {"name": "Aplikasi Cyber Security", "sks": 3},
+    "AAK4EBB3": {"name": "Manajemen Telekomunikasi dan Transformasi Digital", "sks": 3},
+    "AAK4FBB3": {"name": "Adaptive Network", "sks": 3},
+    "AAK4GBB3": {"name": "Cloud Computing", "sks": 3},
+    "AAK4HBB3": {"name": "Koding dan Kompresi", "sks": 3},
+    "AAK4IBB3": {"name": "Steganografi dan Watermarking", "sks": 3},
+    "AAK4JBB3": {"name": "Mobile Application", "sks": 3},
+    "AAK4KBB3": {"name": "Speech Signal Processing", "sks": 3},
+    "AAK4LBB3": {"name": "Komunikasi Akses Wireless", "sks": 3},
+    "AAK4MBB3": {"name": "Wireless Optical Communication", "sks": 3},
+    "AAK4NBB3": {"name": "Broadband Optical Network", "sks": 3},
+    "AAK4OBB3": {"name": "Sistem Komunikasi Satelit", "sks": 3},
+    "AAK4PBB3": {"name": "Rekayasa Radio", "sks": 3},
+    "AAK4QBB3": {"name": "Radar, Navigasi dan Remote Sensing", "sks": 3},
+    "AAK4RBB3": {"name": "5G and Beyond", "sks": 3},
+    "AAK4SBB3": {"name": "Software Defined Radio", "sks": 3},
+    "AAK4TBB3": {"name": "Robotic Process Automation", "sks": 3},
+    "AAK4UBB3": {"name": "Rekayasa Frekuensi Radio dalam Komunikasi Selular", "sks": 3},
+    "AAK4VBB3": {"name": "Teknologi Radio Access Network (RAN)", "sks": 3},
+    "AAK4WBB3": {"name": "Internet of Things: Protokol, Platform, dan AI", "sks": 3},
+    "AAK4XBB3": {"name": "Jaringan Core Telekomunikasi", "sks": 3},
+    "AAK4YBB3": {"name": "Ethical Hacking", "sks": 3},
+    "AAK4ZBB3": {"name": "Keamanan Komunikasi Data", "sks": 3},
+    "AAK47BB3": {"name": "Rekayasa Penyiaran Digital", "sks": 3}
+}
+
 # --- Variabel Global untuk API Prediksi Risiko (App 1) ---
 ml_model = None
 MODEL_FEATURES = [
@@ -159,36 +192,88 @@ def on_startup():
 # 4. Helper Function (Untuk API Rekomendasi)
 # ======================================================================
 
-def get_recommendations_logic(current_semester: int, courses_passed_list: List[str]) -> List[Dict[str, Any]]:
-    """Inti dari logika rekomendasi, HANYA dengan validasi prereq."""
+def get_recommendations_logic(current_semester: int, courses_passed_list: List[str], mk_pilihan_failed_list: List[str]) -> List[Dict[str, Any]]:
+    """
+    Logika rekomendasi dengan injeksi data MK Pilihan dari Database Hardcode.
+    """
     passed_set = set(courses_passed_list)
     all_courses_set = set(course_details_map.keys())
     not_passed_courses = all_courses_set - passed_set
     
-    prereq_valid_candidates = []
+    # Tahap 1: Kumpulkan semua kandidat valid (Regular & Slot Pilihan)
+    raw_candidates = []
 
     for course_code in not_passed_courses:
         prereqs = prereq_map.get(course_code, [])
         if all(p_code in passed_set for p_code in prereqs):
             details = course_details_map.get(course_code)
             if not details: continue 
+            
             out_degree = out_degree_map.get(course_code, 0)
             semester = details.get("semester_plan", 1)
+            
+            # Hitung Base Score
             priority_score = (out_degree / semester) if semester > 0 else 0
             
             candidate_data = details.copy()
             candidate_data["priority_score"] = priority_score
-            prereq_valid_candidates.append(candidate_data)
+            candidate_data["is_retake_elective"] = False 
+            
+            raw_candidates.append(candidate_data)
     
-    catch_up_courses = [c for c in prereq_valid_candidates if c["semester_plan"] < current_semester]
-    current_semester_courses = [c for c in prereq_valid_candidates if c["semester_plan"] == current_semester]
-    future_courses = [c for c in prereq_valid_candidates if c["semester_plan"] > current_semester]
-
-    sorted_catch_up = sorted(catch_up_courses, key=lambda x: (x["semester_plan"], -x["priority_score"]))
-    sorted_current = sorted(current_semester_courses, key=lambda x: -x["priority_score"]) 
-    sorted_future = sorted(future_courses, key=lambda x: (x["semester_plan"], -x["priority_score"]))
-
-    final_ranked_list = sorted_catch_up + sorted_current + sorted_future
+    # Tahap 2: Pisahkan Slot MK Pilihan vs MK Wajib
+    elective_slots = []
+    regular_candidates = []
+    
+    for cand in raw_candidates:
+        # Deteksi slot template berdasarkan prefix kode
+        if cand["code"].startswith("MK_PILIHAN"):
+            elective_slots.append(cand)
+        else:
+            regular_candidates.append(cand)
+            
+    # Urutkan slot pilihan agar mengisi dari semester terkecil (misal sem 7 dulu)
+    elective_slots.sort(key=lambda x: x["semester_plan"])
+    
+    # <--- [UPDATE] Tahap 3: Suntikkan MK Pilihan Gagal + Lookup Database
+    processed_electives = []
+    failed_idx = 0
+    
+    # Prioritaskan mengisi slot dengan MK yang gagal
+    while failed_idx < len(mk_pilihan_failed_list) and len(elective_slots) > 0:
+        slot = elective_slots.pop(0) # Ambil slot template (misal MK_PILIHAN1)
+        failed_code = mk_pilihan_failed_list[failed_idx]
+        
+        # <--- [UPDATE] Lookup detail mata kuliah dari ELECTIVE_COURSES_DB
+        if failed_code in ELECTIVE_COURSES_DB:
+            real_name = ELECTIVE_COURSES_DB[failed_code]["name"]
+            real_sks = ELECTIVE_COURSES_DB[failed_code]["sks"]
+        else:
+            # Fallback jika kode tidak ada di DB (safety net)
+            real_name = "Mata Kuliah Pilihan (Unknown)"
+            real_sks = 3
+
+        # Modifikasi slot menjadi MK spesifik dengan data asli
+        slot["code"] = failed_code 
+        slot["name"] = f"{real_name} (Mengulang)" # Ubah nama jadi nama asli
+        slot["sks"] = real_sks
+        slot["priority_score"] += 1.0 # Boost score 
+        slot["is_retake_elective"] = True
+        
+        processed_electives.append(slot)
+        failed_idx += 1
+        
+    # Masukkan sisa slot pilihan (yang tidak di-override) kembali ke list
+    processed_electives.extend(elective_slots)
+    
+    # Gabungkan kembali semua kandidat
+    final_pool = regular_candidates + processed_electives
+    
+    # Tahap 4: Sorting Akhir (Score tinggi -> Semester kecil)
+    final_ranked_list = sorted(
+        final_pool, 
+        key=lambda x: (-x["priority_score"], x["semester_plan"])
+    )
     
     return final_ranked_list
 
@@ -221,7 +306,7 @@ def predict_risk(student_data: StudentFeatures):
         classes = ml_model.classes_
         probabilities = dict(zip(classes, prediction_proba[0]))
         
-        explanation_obj = {} # Ganti nama dari explanation_str
+        explanation_obj = {} 
         
         if hasattr(ml_model, 'tree_'):
             try:
@@ -248,7 +333,6 @@ def predict_risk(student_data: StudentFeatures):
                         "value": sample_value
                     })
                 
-                # Panggil builder, yang sekarang mengembalikan DICT
                 explanation_obj = build_explanation_from_rules(structured_rules, prediction_val)
 
             except Exception as e:
@@ -262,7 +346,6 @@ def predict_risk(student_data: StudentFeatures):
                 "factors": []
             }
         
-        # Kembalikan objek Pydantic yang sudah divalidasi
         return PredictionResponse(
             prediction=prediction_val,
             probabilities=probabilities,
@@ -278,7 +361,12 @@ async def recommend_courses(request: RecommendationRequest):
     if not course_details_map:
         raise HTTPException(status_code=503, detail="Data kurikulum belum siap. Silakan coba lagi nanti.")
 
-    ranked_candidates = get_recommendations_logic(request.current_semester, request.courses_passed)
+    ranked_candidates = get_recommendations_logic(
+        request.current_semester, 
+        request.courses_passed,
+        request.mk_pilihan_failed
+    )
+    
     top_3_candidates = ranked_candidates[:3]
     
     response_output = []
@@ -288,13 +376,19 @@ async def recommend_courses(request: RecommendationRequest):
         is_tertinggal_status = False
         reason = "Rekomendasi semester ini"
         
-        if course["semester_plan"] < request.current_semester:
+        if course.get("is_retake_elective"):
+            reason = "Wajib Mengulang (MK Pilihan Gagal)"
+            is_tertinggal_status = True
+        elif course["semester_plan"] < request.current_semester:
             reason = f"Mata kuliah tertinggal (Semester {course['semester_plan']})"
             is_tertinggal_status = True
         elif course["semester_plan"] > request.current_semester:
             reason = f"Akselerasi (Semester {course['semester_plan']})"
 
         prereq_codes = prereq_map.get(course["code"], [])
+        # Jika kode MK Pilihan diganti (misal AAK4ABB3), prereq_map.get("AAK4ABB3") akan None/Empty
+        # Ini benar karena MK Pilihan umumnya tidak punya prereq di graf ini (hanya placeholder)
+        
         prereq_details_list = []
         for p_code in prereq_codes:
             if p_code in course_details_map:
@@ -319,5 +413,4 @@ async def recommend_courses(request: RecommendationRequest):
             )
         )
         
-    return response_output
-
+    return response_output