Update app/main.py (#2)

- Update app/main.py (640ebcbc29a546048350ff0452c0692220a55d66)


Co-authored-by: Fajar Syafatoni Raihannadif <fjarsra@users.noreply.huggingface.co>

app/main.py

@@ -1,349 +1,331 @@
-from fastapi import FastAPI, HTTPException, Body
-from app import schemas
-from app.services.llm_engine import llm_engine
-from app.services.skill_manager import skill_manager
-import pandas as pd
-import pickle
-import ast
-import os
-from sklearn.metrics.pairwise import linear_kernel
-from app.services.psych_service import psych_service
-from typing import List
-
-app = FastAPI(title="MORA - AI Learning Assistant (Final)")
-
-# --- GLOBAL MODELS STORE ---
-models = {
-    'df': None,
-    'tfidf': None,
-    'matrix': None
-}
-
-SKILL_KEYWORDS = []
-
-@app.on_event("startup")
-def load_skill_keywords():
-    global SKILL_KEYWORDS
-    try:
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        csv_path = os.path.join(current_dir, "data", "Skill Keywords.csv")
-        df = pd.read_csv(csv_path)
-        SKILL_KEYWORDS = df['keyword'].dropna().tolist()
-        print(f"✅ Berhasil memuat {len(SKILL_KEYWORDS)} keywords skill.")
-    except Exception as e:
-        print(f"⚠️ Gagal memuat dataset keyword: {e}")
-        SKILL_KEYWORDS = []
-
-# Fungsi Pembantu: Mencari keyword dalam pesan user
-def find_keywords_in_text(user_text: str):
-    found = []
-    text_lower = " " + user_text.lower() + " " # Tambah spasi biar aman deteksi kata pendek
-    
-    for k in SKILL_KEYWORDS:
-        # Cek sederhana: Apakah keyword ada di dalam pesan?
-        # Untuk kata pendek (<3 huruf) seperti "C", "R", "Go", kita pakai spasi agar tidak match "Car" atau "Goat"
-        if len(k) < 3:
-            if f" {k.lower()} " in text_lower:
-                found.append(k)
-        else:
-            if k.lower() in text_lower:
-                found.append(k)
-                
-    # Hapus duplikat dan kembalikan
-    return list(set(found))
-
-# --- 1. STARTUP: LOAD MODEL .PKL ---
-@app.on_event("startup")
-def load_models():
-    print("🔄 Loading Pre-trained Models...")
-    
-    # Menggunakan Absolute Path agar aman dijalankan dari mana saja
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    base_dir = os.path.dirname(current_dir)
-    artifacts_dir = os.path.join(base_dir, "model_artifacts")
-    
-    try:
-        with open(os.path.join(artifacts_dir, 'courses_df.pkl'), 'rb') as f:
-            models['df'] = pickle.load(f)
-        with open(os.path.join(artifacts_dir, 'tfidf_vectorizer.pkl'), 'rb') as f:
-            models['tfidf'] = pickle.load(f)
-        with open(os.path.join(artifacts_dir, 'tfidf_matrix.pkl'), 'rb') as f:
-            models['matrix'] = pickle.load(f)
-        print(f"✅ Models Loaded Successfully from: {artifacts_dir}")
-    except Exception as e:
-        print(f"❌ Error Loading Models: {e}")
-        print(f"👉 Pastikan folder 'model_artifacts' ada di: {base_dir}")
-
-# --- 2. ENDPOINT REKOMENDASI (ML POWERED) ---
-@app.post("/recommendations")
-def get_recommendations(user: schemas.UserProfile):
-    df = models.get('df')
-    tfidf = models.get('tfidf')
-    matrix = models.get('matrix')
-    
-    # Jika model belum siap, return kosong biar gak crash
-    if df is None: return []
-
-    # Mapping Level agar komputer mengerti urutan
-    LEVEL_MAP = {
-        'beginner': 1, 'dasar': 1, 'pemula': 1,
-        'intermediate': 2, 'menengah': 2,
-        'advanced': 3, 'mahir': 3, 'expert': 3, 'profesional': 3
-    }
-    
-    final_recs = []
-    # Set course yang sudah diambil agar tidak disarankan lagi
-    seen_courses = set(user.completed_courses)
-    
-    # --- LOGIKA CORE: Loop setiap 'Gap' Skill User ---
-    for gap in user.missing_skills:
-        skill_query = gap.skill_name
-        target_lvl_str = gap.target_level.lower()
-        target_lvl_num = LEVEL_MAP.get(target_lvl_str, 1) # Default 1 (Pemula)
-        
-        try:
-            # 1. Transform nama skill jadi vektor angka
-            vec = tfidf.transform([skill_query.lower()])
-            
-            # 2. Hitung kemiripan (Cosine Similarity)
-            scores = linear_kernel(vec, matrix).flatten()
-            
-            # 3. Ambil Top 15 kandidat
-            indices = scores.argsort()[:-15:-1]
-            
-            for idx in indices:
-                score = scores[idx]
-                # Filter awal: Skip jika kemiripan text terlalu rendah
-                if score < 0.1: continue
-                
-                course = df.iloc[idx]
-                c_id = int(course['course_id'])
-                
-                if c_id in seen_courses: continue
-                
-                # --- FILTER LEVEL (ADAPTIVE) ---
-                c_lvl_str = str(course['level_name']).lower()
-                c_lvl_num = LEVEL_MAP.get(c_lvl_str, 1)
-                
-                # Logic: Jangan kasih course yang levelnya DI ATAS target (kejauhan)
-                if c_lvl_num > target_lvl_num: continue
-                
-                # Logic Badge (Penanda)
-                if c_lvl_num == target_lvl_num:
-                    badge = "🎯 Target Pas"
-                else:
-                    badge = "↺ Review Dasar"
-
-                # Parse Tutorial List (karena di CSV formatnya string)
-                tuts = course['tutorial_list']
-                if isinstance(tuts, str):
-                    try: tuts = ast.literal_eval(tuts)
-                    except: tuts = []
-                
-                # Tambahkan ke hasil
-                final_recs.append({
-                    "skill": skill_query,
-                    "current_level": gap.target_level,
-                    "course_to_take": course['course_name'],
-                    "chapters": tuts[:3], # Ambil 3 bab pertama
-                    "match_score": round(score * 100, 1),
-                    "badge": badge
-                })
-                seen_courses.add(c_id)
-                
-        except Exception as e:
-            print(f"Error processing {skill_query}: {e}")
-            continue
-
-    # Urutkan berdasarkan skor kecocokan tertinggi
-    final_recs = sorted(final_recs, key=lambda x: x['match_score'], reverse=True)
-    
-    return final_recs[:5] # Kembalikan Top 5
-
-# --- 3. ENDPOINT CHAT ROUTER ---
-# app/main.py (Bagian process_chat saja)
-
-@app.post("/chat/process", response_model=schemas.ChatResponse)
-async def process_chat(req: schemas.ChatRequest):
-    role_data = skill_manager.get_role_data(req.role)
-    # --- [UPDATE BARU: Ektrak Silabus Lengkap] ---
-    # Kita buat string rapi berisi Skill + Topik-topiknya
-    found_keywords = find_keywords_in_text(req.message)
-    
-    # Siapkan context string untuk dikirim ke LLM
-    if found_keywords:
-        # Jika ketemu: "User bertanya tentang: Python, SQL"
-        keyword_context = ", ".join(found_keywords)
-        dataset_status = "FOUND"
-    else:
-        # Jika tidak ketemu
-        keyword_context = "NONE"
-        dataset_status = "NOT_FOUND"
-
-    # 2. Router
-    intent = await llm_engine.process_user_intent(req.message, [])
-    
-    action = intent.get('action')
-    # PERUBAHAN 1: Ambil List skills, bukan single skill
-    detected_skills_list = intent.get('detected_skills', [])
-    
-    final_reply = ""
-    response_data = None
-    
-    # 3. Logic
-    if action == "START_EXAM":
-        target_skill_ids = []
-        
-        # A. Cari ID untuk SEMUA skill yang dideteksi (Looping)
-        if detected_skills_list and role_data:
-            for ds in detected_skills_list:
-                for s in role_data['sub_skills']:
-                    # Cek kemiripan nama
-                    if s['name'].lower() in ds.lower() or ds.lower() in s['name'].lower():
-                        if s['id'] not in target_skill_ids:
-                            target_skill_ids.append(s['id'])
-        
-        # B. Jika ada skill yang valid, generate soal untuk MASING-MASING skill
-        if target_skill_ids:
-            exam_list = []
-            
-            for skid in target_skill_ids:
-                # Ambil level user
-                user_current_level = req.current_skills.get(skid, "beginner")
-                skill_details = skill_manager.get_skill_details(req.role, skid)
-                level_data = skill_details['levels'].get(user_current_level, skill_details['levels']['beginner'])
-                
-                # Generate Soal (Sequential)
-                llm_res = await llm_engine.generate_question(level_data['exam_topics'], user_current_level)
-                
-                # Masukkan ke list soal
-                exam_list.append({
-                    "skill_id": skid,
-                    "skill_name": skill_details['name'],
-                    "level": user_current_level,
-                    "question": llm_res['question_text'],
-                    "context": llm_res['grading_rubric']
-                })
-            
-            # C. Format Response Baru (Multi-Exam)
-            response_data = {
-                "mode": "multiple_exams", # Penanda buat frontend
-                "exams": exam_list        # List soal ada di sini
-            }
-            
-            skill_display = ", ".join([x['skill_name'] for x in exam_list])
-            final_reply = f"Siap! Saya siapkan {len(exam_list)} ujian untukmu: **{skill_display}**. Silakan kerjakan satu per satu di bawah ini! 👇"
-            
-        else:
-            action = "CASUAL_CHAT"
-            final_reply = await llm_engine.casual_chat(
-            req.message, 
-            [m.dict() for m in req.history], 
-            keyword_context, 
-            dataset_status 
-        )
-
-    elif action == "START_PSYCH_TEST":
-        response_data = {"trigger_psych_test": True}
-        final_reply = "Tenang, Mora punya tes kepribadian singkat untuk membantumu memilih job role antara **AI Engineer** atau **Front-End Developer**. Yuk coba sekarang! 👇"
-
-    elif action == "GET_RECOMMENDATION":
-        response_data = {"trigger_recommendation": True}
-        final_reply = "Sedang menganalisis kebutuhan belajarmu..."
-
-    elif action == "CASUAL_CHAT":
-        final_reply = await llm_engine.casual_chat(
-            req.message, 
-            [m.dict() for m in req.history], 
-            keyword_context, 
-            dataset_status 
-        )
-
-    return schemas.ChatResponse(
-        reply=final_reply,
-        action_type=action,
-        data=response_data
-    )
-
-
-@app.post("/exam/submit", response_model=schemas.EvaluationResponse)
-async def submit_exam(sub: schemas.AnswerSubmission):
-    evaluation = await llm_engine.evaluate_answer(
-        user_answer=sub.user_answer,
-        question_context={
-            "question_text": "REFER TO CONTEXT",
-            "grading_rubric": sub.question_context
-        }
-    )
-    
-    is_passed = evaluation['is_correct'] and evaluation['score'] >= 70
-    suggested_lvl = "intermediate" if is_passed else None # Logika sederhana
-    
-    return schemas.EvaluationResponse(
-        is_correct=evaluation['is_correct'],
-        score=evaluation['score'],
-        feedback=evaluation['feedback'],
-        passed=is_passed,
-        suggested_new_level=suggested_lvl
-    )
-
-# --- 5. ENDPOINT PROGRESS ---
-@app.post("/progress")
-def get_progress(req: schemas.ProgressRequest):
-    role_data = skill_manager.get_role_data(req.role)
-    if not role_data: return []
-    
-    progress_report = []
-    level_weight = {"beginner": 0, "intermediate": 1, "advanced": 2}
-
-    for skill in role_data['sub_skills']:
-        skill_id = skill['id']
-        user_level = req.current_skills.get(skill_id, "beginner")
-        
-        # Hitung Persen
-        current_stage = level_weight.get(user_level, 0)
-        percent = int((current_stage / 3) * 100)
-        if user_level == "beginner": percent = 5
-        elif user_level == "intermediate": percent = 50
-        elif user_level == "advanced": percent = 80
-        
-        # Sisa tutorial (dummy/static logic karena detail ada di rekomendasi)
-        remaining = 0 
-        
-        progress_report.append({
-            "skill_name": skill['name'],
-            "current_level": user_level,
-            "progress_percent": percent,
-            "remaining_tutorials": remaining
-        })
-        
-    return progress_report
-
-# ==========================================
-# ENDPOINT PSIKOLOGI (JOB ROLE TEST)
-# ==========================================
-
-@app.get("/psych/questions", response_model=List[schemas.PsychQuestionItem])
-def get_psych_questions():
-    """Mengambil daftar soal tes kepribadian."""
-    return psych_service.get_all_questions()
-
-@app.post("/psych/submit", response_model=schemas.PsychResultResponse)
-async def submit_psych_test(req: schemas.PsychSubmitRequest):
-    """Menerima jawaban user, hitung skor, dan minta analisis LLM."""
-    
-    # 1. Hitung Skor secara matematis
-    result = psych_service.calculate_result(req.answers)
-    
-    winner = result["winner"]
-    scores = result["scores"]
-    traits = result["traits"]
-    
-    # 2. Minta LLM buatkan kata-kata mutiara/analisis
-    analysis_text = await llm_engine.analyze_psych_result(winner, traits)
-    
-    return schemas.PsychResultResponse(
-        suggested_role=winner,
-        analysis=analysis_text,
-        scores=scores
+from fastapi import FastAPI, HTTPException, Body
+from app import schemas
+from app.services.llm_engine import llm_engine
+from app.services.skill_manager import skill_manager
+import pandas as pd
+import pickle
+import ast
+import os
+from sklearn.metrics.pairwise import linear_kernel
+from app.services.psych_service import psych_service
+from typing import List
+
+app = FastAPI(title="MORA - AI Learning Assistant (Final)")
+
+# --- GLOBAL MODELS STORE ---
+models = {
+    'df': None,
+    'tfidf': None,
+    'matrix': None
+}
+
+SKILL_KEYWORDS = []
+
+@app.on_event("startup")
+def load_skill_keywords():
+    global SKILL_KEYWORDS
+    try:
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        csv_path = os.path.join(current_dir, "data", "Skill Keywords.csv")
+        df = pd.read_csv(csv_path)
+        SKILL_KEYWORDS = df['keyword'].dropna().tolist()
+        print(f"✅ Berhasil memuat {len(SKILL_KEYWORDS)} keywords skill.")
+    except Exception as e:
+        print(f"⚠️ Gagal memuat dataset keyword: {e}")
+        SKILL_KEYWORDS = []
+
+# Fungsi Pembantu: Mencari keyword dalam pesan user
+def find_keywords_in_text(user_text: str):
+    found = []
+    text_lower = " " + user_text.lower() + " " # Tambah spasi biar aman deteksi kata pendek
+    
+    for k in SKILL_KEYWORDS:
+        # Cek sederhana: Apakah keyword ada di dalam pesan?
+        # Untuk kata pendek (<3 huruf) seperti "C", "R", "Go", kita pakai spasi agar tidak match "Car" atau "Goat"
+        if len(k) < 3:
+            if f" {k.lower()} " in text_lower:
+                found.append(k)
+        else:
+            if k.lower() in text_lower:
+                found.append(k)
+                
+    # Hapus duplikat dan kembalikan
+    return list(set(found))
+
+# --- 1. STARTUP: LOAD MODEL .PKL ---
+@app.on_event("startup")
+def load_models():
+    print("🔄 Loading Pre-trained Models...")
+    
+    # Menggunakan Absolute Path agar aman dijalankan dari mana saja
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    base_dir = os.path.dirname(current_dir)
+    artifacts_dir = os.path.join(base_dir, "model_artifacts")
+    
+    try:
+        with open(os.path.join(artifacts_dir, 'courses_df.pkl'), 'rb') as f:
+            models['df'] = pickle.load(f)
+        with open(os.path.join(artifacts_dir, 'tfidf_vectorizer.pkl'), 'rb') as f:
+            models['tfidf'] = pickle.load(f)
+        with open(os.path.join(artifacts_dir, 'tfidf_matrix.pkl'), 'rb') as f:
+            models['matrix'] = pickle.load(f)
+        print(f"✅ Models Loaded Successfully from: {artifacts_dir}")
+    except Exception as e:
+        print(f"❌ Error Loading Models: {e}")
+        print(f"👉 Pastikan folder 'model_artifacts' ada di: {base_dir}")
+
+# --- 2. ENDPOINT REKOMENDASI (ML POWERED) ---
+@app.post("/recommendations")
+def get_recommendations(user: schemas.UserProfile):
+    df = models.get('df')
+    tfidf = models.get('tfidf')
+    matrix = models.get('matrix')
+    
+    # Jika model belum siap, return kosong biar gak crash
+    if df is None: return []
+
+    # Mapping Level agar komputer mengerti urutan
+    LEVEL_MAP = {
+        'beginner': 1, 'dasar': 1, 'pemula': 1,
+        'intermediate': 2, 'menengah': 2,
+        'advanced': 3, 'mahir': 3, 'expert': 3, 'profesional': 3
+    }
+    
+    final_recs = []
+    # Set course yang sudah diambil agar tidak disarankan lagi
+    seen_courses = set(user.completed_courses)
+    
+    # --- LOGIKA CORE: Loop setiap 'Gap' Skill User ---
+    for gap in user.missing_skills:
+        skill_query = gap.skill_name
+        target_lvl_str = gap.target_level.lower()
+        target_lvl_num = LEVEL_MAP.get(target_lvl_str, 1) # Default 1 (Pemula)
+        
+        try:
+            # 1. Transform nama skill jadi vektor angka
+            vec = tfidf.transform([skill_query.lower()])
+            
+            # 2. Hitung kemiripan (Cosine Similarity)
+            scores = linear_kernel(vec, matrix).flatten()
+            
+            # 3. Ambil Top 15 kandidat
+            indices = scores.argsort()[:-15:-1]
+            
+            for idx in indices:
+                score = scores[idx]
+                # Filter awal: Skip jika kemiripan text terlalu rendah
+                if score < 0.1: continue
+                
+                course = df.iloc[idx]
+                c_id = int(course['course_id'])
+                
+                if c_id in seen_courses: continue
+                
+                # --- FILTER LEVEL (ADAPTIVE) ---
+                c_lvl_str = str(course['level_name']).lower()
+                c_lvl_num = LEVEL_MAP.get(c_lvl_str, 1)
+                
+                # Logic: Jangan kasih course yang levelnya DI ATAS target (kejauhan)
+                if c_lvl_num > target_lvl_num: continue
+                
+                # Logic Badge (Penanda)
+                if c_lvl_num == target_lvl_num:
+                    badge = "🎯 Target Pas"
+                else:
+                    badge = "↺ Review Dasar"
+
+                # Parse Tutorial List (karena di CSV formatnya string)
+                tuts = course['tutorial_list']
+                if isinstance(tuts, str):
+                    try: tuts = ast.literal_eval(tuts)
+                    except: tuts = []
+                
+                # Tambahkan ke hasil
+                final_recs.append({
+                    "skill": skill_query,
+                    "current_level": gap.target_level,
+                    "course_to_take": course['course_name'],
+                    "chapters": tuts[:3], # Ambil 3 bab pertama
+                    "match_score": round(score * 100, 1),
+                    "badge": badge
+                })
+                seen_courses.add(c_id)
+                
+        except Exception as e:
+            print(f"Error processing {skill_query}: {e}")
+            continue
+
+    # Urutkan berdasarkan skor kecocokan tertinggi
+    final_recs = sorted(final_recs, key=lambda x: x['match_score'], reverse=True)
+    
+    return final_recs[:5] # Kembalikan Top 5
+
+# --- 3. ENDPOINT CHAT ROUTER ---
+# app/main.py (Bagian process_chat saja)
+
+@app.post("/chat/process", response_model=schemas.ChatResponse)
+async def process_chat(req: schemas.ChatRequest):
+    role_data = skill_manager.get_role_data(req.role)
+    # --- [UPDATE BARU: Ektrak Silabus Lengkap] ---
+    # Kita buat string rapi berisi Skill + Topik-topiknya
+    found_keywords = find_keywords_in_text(req.message)
+    
+    # Siapkan context string untuk dikirim ke LLM
+    if found_keywords:
+        # Jika ketemu: "User bertanya tentang: Python, SQL"
+        keyword_context = ", ".join(found_keywords)
+        dataset_status = "FOUND"
+    else:
+        # Jika tidak ketemu
+        keyword_context = "NONE"
+        dataset_status = "NOT_FOUND"
+
+    # 2. Router
+    intent = await llm_engine.process_user_intent(req.message, [])
+    
+    action = intent.get('action')
+    # PERUBAHAN 1: Ambil List skills, bukan single skill
+    detected_skills_list = intent.get('detected_skills', [])
+    
+    final_reply = ""
+    response_data = None
+    
+    # 3. Logic
+    if action == "START_EXAM":
+        target_skill_ids = []
+        
+        # A. Cari ID untuk SEMUA skill yang dideteksi (Looping)
+        if detected_skills_list and role_data:
+            for ds in detected_skills_list:
+                for s in role_data['sub_skills']:
+                    # Cek kemiripan nama
+                    if s['name'].lower() in ds.lower() or ds.lower() in s['name'].lower():
+                        if s['id'] not in target_skill_ids:
+                            target_skill_ids.append(s['id'])
+        
+        # B. Jika ada skill yang valid, generate soal untuk MASING-MASING skill
+        if target_skill_ids:
+            exam_list = []
+            
+            for skid in target_skill_ids:
+                # Ambil level user
+                user_current_level = req.current_skills.get(skid, "beginner")
+                skill_details = skill_manager.get_skill_details(req.role, skid)
+                level_data = skill_details['levels'].get(user_current_level, skill_details['levels']['beginner'])
+                
+                # Generate Soal (Sequential)
+                llm_res = await llm_engine.generate_question(level_data['exam_topics'], user_current_level)
+                
+                # Masukkan ke list soal
+                exam_list.append({
+                    "skill_id": skid,
+                    "skill_name": skill_details['name'],
+                    "level": user_current_level,
+                    "question": llm_res['question_text'],
+                    "context": llm_res['grading_rubric']
+                })
+            
+            # C. Format Response Baru (Multi-Exam)
+            response_data = {
+                "mode": "multiple_exams", # Penanda buat frontend
+                "exams": exam_list        # List soal ada di sini
+            }
+            
+            skill_display = ", ".join([x['skill_name'] for x in exam_list])
+            final_reply = f"Siap! Saya siapkan {len(exam_list)} ujian untukmu: **{skill_display}**. Silakan kerjakan satu per satu di bawah ini! 👇"
+            
+        else:
+            action = "CASUAL_CHAT"
+            final_reply = await llm_engine.casual_chat(
+            req.message, 
+            [m.dict() for m in req.history], 
+            keyword_context, 
+            dataset_status 
+        )
+
+    elif action == "START_PSYCH_TEST":
+        response_data = {"trigger_psych_test": True}
+        final_reply = "Tenang, Mora punya tes kepribadian singkat untuk membantumu memilih job role antara **AI Engineer** atau **Front-End Developer**. Yuk coba sekarang! 👇"
+
+    elif action == "GET_RECOMMENDATION":
+        response_data = {"trigger_recommendation": True}
+        final_reply = "Sedang menganalisis kebutuhan belajarmu..."
+
+    elif action == "CASUAL_CHAT":
+        final_reply = await llm_engine.casual_chat(
+            req.message, 
+            [m.dict() for m in req.history], 
+            keyword_context, 
+            dataset_status 
+        )
+
+    return schemas.ChatResponse(
+        reply=final_reply,
+        action_type=action,
+        data=response_data
+    )
+
+
+@app.post("/exam/submit", response_model=schemas.EvaluationResponse)
+async def submit_exam(sub: schemas.AnswerSubmission):
+    evaluation = await llm_engine.evaluate_answer(
+        user_answer=sub.user_answer,
+        question_context={
+            "question_text": "REFER TO CONTEXT",
+            "grading_rubric": sub.question_context
+        }
+    )
+    
+    is_passed = evaluation['is_correct'] and evaluation['score'] >= 70
+    suggested_lvl = "intermediate" if is_passed else None # Logika sederhana
+    
+    return schemas.EvaluationResponse(
+        is_correct=evaluation['is_correct'],
+        score=evaluation['score'],
+        feedback=evaluation['feedback'],
+        passed=is_passed,
+        suggested_new_level=suggested_lvl
+    )
+
+# --- 5. ENDPOINT PROGRESS ---
+@app.post("/progress/analyze")
+async def get_progress_analysis(data: schemas.ProgressData):
+    # Konversi objek Pydantic ke Dictionary biasa
+    progress_dict = data.dict()
+    
+    # Panggil LLM khusus analisis
+    analysis_text = await llm_engine.analyze_progress(
+        user_name=data.user_name, 
+        progress_data=progress_dict
+    )
+    
+    return {"analysis": analysis_text}
+
+# ==========================================
+# ENDPOINT PSIKOLOGI (JOB ROLE TEST)
+# ==========================================
+
+@app.get("/psych/questions", response_model=List[schemas.PsychQuestionItem])
+def get_psych_questions():
+    """Mengambil daftar soal tes kepribadian."""
+    return psych_service.get_all_questions()
+
+@app.post("/psych/submit", response_model=schemas.PsychResultResponse)
+async def submit_psych_test(req: schemas.PsychSubmitRequest):
+    """Menerima jawaban user, hitung skor, dan minta analisis LLM."""
+    
+    # 1. Hitung Skor secara matematis
+    result = psych_service.calculate_result(req.answers)
+    
+    winner = result["winner"]
+    scores = result["scores"]
+    traits = result["traits"]
+    
+    # 2. Minta LLM buatkan kata-kata mutiara/analisis
+    analysis_text = await llm_engine.analyze_psych_result(winner, traits)
+    
+    return schemas.PsychResultResponse(
+        suggested_role=winner,
+        analysis=analysis_text,
+        scores=scores
     )