graduation_logic.py

# ======================================================================
# --- graduation_logic.py ---
# ======================================================================

import networkx as nx
from typing import Dict, Any, List

def _predict_naive(current_semester: int, total_sks_passed: int, last_gpa: float) -> Dict[str, Any]:
    """
    Logika perhitungan matematis dasar (Naive) berdasarkan SKS dan IPK.
    Menggunakan teks deskripsi custom sesuai permintaan user.
    """
    TARGET_SKS = 144
    TARGET_SEMESTER = 8
    MAX_SKS_REGULAR = 24    # Batas absolut reguler (biasanya IP >= 3.00)
    LIMIT_SKS_LOW_GPA = 20  # Batas jika IP < 3.00 
    SAFE_THRESHOLD = 18     # Batas aman/santai
    
    # 1. Hitung Sisa
    sks_needed = TARGET_SKS - total_sks_passed
    semesters_left = (TARGET_SEMESTER - current_semester) + 1    
    
    # Stats dasar untuk dikembalikan
    stats = {
        "sks_needed": sks_needed,
        "semesters_left": semesters_left,
        "required_pace": 0,
        "student_capacity": int(MAX_SKS_REGULAR if last_gpa >= 3.00 else LIMIT_SKS_LOW_GPA)
    }

    # --- LOGIC: Sudah Semester Akhir / Lewat ---
    if semesters_left <= 0:
        if sks_needed <= 0:
            return {
                "status": "Lulus", 
                "color": "green", 
                "description": "Selamat! Anda telah menyelesaikan kebutuhan SKS minimal.",
                "stats": stats
            }
        else:
            stats["required_pace"] = sks_needed
            return {
                "status": "Terlambat", 
                "color": "red", 
                "description": f"Saat ini semester {current_semester} dan SKS belum terpenuhi. Target lulus 8 semester sudah terlewat.",
                "stats": stats
            }

    # 2. Hitung Kecepatan yang Dibutuhkan (Required Pace)
    required_sks_per_sem = sks_needed / semesters_left
    stats["required_pace"] = round(required_sks_per_sem, 2)
    
    # 3. Evaluasi Status (Teks dari User)
    student_capacity = stats["student_capacity"]
    
    if required_sks_per_sem > MAX_SKS_REGULAR:
        # KASUS: Mustahil reguler
        return {
            "status": "🔴 Terlambat",
            "color": "red",
            "description": f"Target lulus semester 8 tidak memungkinkan. Anda butuh rata-rata {required_sks_per_sem:.1f} SKS yang perlu dipenuhi setiap semester selanjutnya, melebihi batas reguler yaitu 24 SKS.",
            "stats": stats
        }
        
    elif required_sks_per_sem <= SAFE_THRESHOLD:
        # KASUS: Aman
        return {
            "status": "🟢 Aman",
            "color": "green",
            "description": f"Posisi aman. Beban ringan, sisa (~{required_sks_per_sem:.1f} SKS yang perlu dipenuhi tiap semester. Pertahankan performa tiap semester!",
            "stats": stats
        }
        
    elif required_sks_per_sem <= student_capacity:
        # KASUS: Padat tapi Masih Mungkin
        status_text = "🟡 Jadwal Relatif Padat"
        return {
            "status": status_text,
            "color": "yellow",
            "description": f"Diperkirakan anda butuh ~{required_sks_per_sem:.1f} SKS yang perlu dipenuhi untuk semester-semester selanjutnya. Kapasitas SKS Anda ({student_capacity}) Sudah cukup mendukung untuk mengejar target ini.",
            "stats": stats
        }
        
    else: 
        # KASUS: Terhambat IPK
        return {
            "status": "🟠 Rawan Terlambat",
            "color": "orange",
            "description": f"Hati-hati! Anda butuh {required_sks_per_sem:.1f} SKS tiap semester agar lulus tepat waktu, tapi IPK saat ini membatasi jatah cuma {student_capacity} SKS.",
            "stats": stats
        }

def predict_graduation_status(
    current_semester: int, 
    total_sks_passed: int, 
    last_gpa: float,
    graph_G: nx.DiGraph = None,     # Optional: Graph object (pass by reference)
    passed_courses: List[str] = None # Optional: List kode MK lulus
) -> Dict[str, Any]:
    """
    Fungsi utama: Menjalankan logika Naive, lalu melakukan Override jika
    ditemukan masalah struktural pada graf (rantai prasyarat).
    """
    
    # 1. Jalankan Prediksi Naive (Matematis)
    result = _predict_naive(current_semester, total_sks_passed, last_gpa)
    
    # Jika data graf tidak lengkap atau status sudah Critical/Lulus, kembalikan hasil naive
    if graph_G is None or passed_courses is None:
        return result
    
    if result["color"] == "red" or result["status"] == "Lulus":
        return result

    # 2. LOGIKA OVERRIDE: Cek Rantai Prasyarat (Critical Path)
    try:
        # A. Identifikasi MK yang BELUM lulus
        all_courses = set(graph_G.nodes())
        passed_set = set(passed_courses)
        unpassed_courses = list(all_courses - passed_set)
        
        if not unpassed_courses:
            return result

        # B. Buat Subgraph (Hanya berisi matkul sisa & relasinya)
        subgraph_remaining = graph_G.subgraph(unpassed_courses)
        
        # C. Hitung Longest Path (Rantai Terpanjang) di subgraph
        # dag_longest_path mengembalikan list node, misal ['A', 'B', 'C'] -> Panjang 3
        if nx.is_directed_acyclic_graph(subgraph_remaining):
            longest_chain_path = nx.dag_longest_path(subgraph_remaining)
            min_semesters_needed_structural = len(longest_chain_path)
            
            semesters_left = result["stats"]["semesters_left"]
            
            # D. Bandingkan dengan Sisa Waktu
            if min_semesters_needed_structural > semesters_left:
                result["status"] = "🔴 Terlambat (Struktural)"
                result["color"] = "red"
                result["description"] = (
                    f"SKS tersisa cukup, namun terdeteksi rantai prasyarat panjang yang terdiri"
                    f"({min_semesters_needed_structural} Mata Kuliah Beruntun) dan tidak bisa diambil sekaligus dalam sisa waktu."
                )
                # Tambahkan info debug ke stats
                result["stats"]["structural_issue"] = True
                result["stats"]["longest_chain_len"] = min_semesters_needed_structural
                result["stats"]["longest_chain_path"] = longest_chain_path
        
    except Exception as e:
        print(f"Graph Analysis Warning: {e}")
        # Jika error graf, fallback ke hasil naive
        return result

    return result