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@@ -33,3 +33,42 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+knowledge_base/2118-Article[[:space:]]Text-14396-1-10-20230413.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/2233-Article[[:space:]]Text-12596-1-10-20240723.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/259034-differentiated-instruction-solusi-pembel-7b868815.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/266-Article[[:space:]]Text-1333-1-10-20220811.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/276698-memahami-gaya-belajar-untuk-meningkatkan-7924c83a.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/309-Article[[:space:]]Text-1228-1-10-20230402.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/442-Article[[:space:]]Text-1341-1-10-20220711.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/587955-analisis-teoritis-tujuan-pembelajaran-be-3194189b.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/5881-29852-1-PB.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/8736-25824-1-PB.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/897-Article[[:space:]]Text-4084-2-10-20240911.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/admin_jurnal,+3+Jurnal+Ahmad+Teguh+Purnawanto_removed.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/administrator,+106-534-1-PB.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/adminjip1,+23115-Article+Text-78714-1-4-20230501.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/adminopenjurnal,+publish+ke-3.doc.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Analisis[[:space:]]Gaya[[:space:]]Belajar[[:space:]]Peserta[[:space:]]Didik[[:space:]]dalam[[:space:]]Pembelajaran[[:space:]]di[[:space:]]Kelas.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Artikel+9.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/BAB[[:space:]]II[[:space:]]24[[:space:]]SEPTEMBER[[:space:]]FIX.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Buku[[:space:]]Multiple[[:space:]]Intellegence[[:space:]]Dalam[[:space:]]Pembelajaran-compressed-compressed.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Buku-Digital---MANAJEMEN-KELAS.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Buku-Model-Pengembangan-Pembelajaran-Berdiferensiasi-SMPN-20-Tangsel-_5-Maretisbn.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/humanika,+4-+J-HUMANIKA+VOL+21+N0+2+2021.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/humanika,+MENGGALI+KEMAMPUAN+Sunartini+-+5.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/IDENTIFIKASI[[:space:]]GAYA[[:space:]]BELAJAR.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/IIS-NURHAYATI_2217119_LANDASAN-FILOSOFIS.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/IMPLEMENTASITAKSONOMIBLOOMRANAHKOGNITIFnew.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Konsep[[:space:]]Gaya[[:space:]]Belajar[[:space:]]dan[[:space:]]Implementasinya.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Lencana+Vol+3+no+1+Januari+2025+hal+227-246.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Manajemen[[:space:]]Kelas[[:space:]]Afriza.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/MANAJEMEN-KELAS.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/nur-hidayah-buku-Psikologi-Pendidikan-cover.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Penerapan_Model_Pembelajaran_Diferensiasi_Differen.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Pengertian-Multiple-Intelligences.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Perkembangan[[:space:]]Peserta[[:space:]]Didik.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/PRINSIP[[:space:]]PENGEMBANGAN[[:space:]]PEMBELAJARAN[[:space:]]BERDEFERENSIASI.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/Problematika+Manajemen+Kelas-AI.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/PSIKOLOGI[[:space:]]PERKEMBANGAN[[:space:]]PESERTA[[:space:]]DIDIK.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/PSIKOLOGI[[:space:]]PERKEMBANGAN.pdf filter=lfs diff=lfs merge=lfs -text
+knowledge_base/skripsi[[:space:]]bab[[:space:]]2[[:space:]]Desi[[:space:]]Nur[[:space:]]Safitri.pdf filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# File: Dockerfile (KODE FINAL DAN LENGKAP)
+
+# --- Base Stage (Membangun lingkungan dasar) ---
+FROM python:3.10-slim AS base
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+ENV HOME=/app
+WORKDIR /app
+COPY requirements.txt .
+
+# --- PENTING: Perbaikan untuk ImportError: libGL.so.1 dan libgthread-2.0.so.0 ---
+# Menginstal library grafis (libgl1) DAN GLib (libglib2.0-0)
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+# --------------------------------------------------------------------------------
+
+# --- Konfigurasi User (Mencegah Permission Errors) ---
+RUN adduser --disabled-password --no-create-home appuser
+RUN chown -R appuser:appuser /app
+
+# --- Builder Stage (Membuat wheel dependensi) ---
+FROM base AS builder
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip wheel --no-cache-dir --wheel-dir /app/wheels -r requirements.txt
+
+# --- Final Stage (Lingkungan Runtime) ---
+FROM base AS final
+COPY --from=builder /app/wheels /app/wheels
+
+# Instalasi Python Wheels
+RUN pip install --no-cache-dir /app/wheels/*
+
+# Menyalin kode aplikasi
+COPY . .
+
+# Mengalihkan ke user non-root
+USER appuser
+
+# Perintah menjalankan aplikasi
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+# File: app.py (VERSI KOREKSI FINAL DAN LENGKAP)
+
+import os
+from fastapi import FastAPI, Depends, HTTPException
+from pydantic import BaseModel, Field
+from typing import List, Optional
+from contextlib import asynccontextmanager
+
+# --- Database Imports ---
+from sqlalchemy.orm import sessionmaker, Session
+from sqlalchemy import create_engine
+from sqlalchemy.orm import declarative_base
+from sqlalchemy import Column, Integer, String, Text, Boolean, DateTime
+from sqlalchemy.sql import func
+
+# --- LangChain & Hugging Face Imports ---
+from langchain_huggingface import HuggingFaceEndpoint, HuggingFaceEmbeddings
+from huggingface_hub import login
+
+# --- Impor Layanan RAG ---
+from core.rag_services import create_vector_store, load_vector_store, get_rag_chain
+
+# --- Impor Tugas Latar Belakang ---
+from tasks import start_lessonplan_finetune_task
+
+# ==============================================================================
+# 1. KONFIGURASI APLIKASI & DATABASE
+# ==============================================================================
+
+# --- Load Environment Variables ---
+DATABASE_URL = os.getenv("DATABASE_URL")
+HF_TOKEN = os.getenv("HF_TOKEN")
+BASE_MODEL_REPO_ID = "SeaLLMs/SeaLLM-7B-v2.5"
+TUNED_LESSONPLAN_MODEL_ID = os.getenv("TUNED_LESSONPLAN_MODEL_ID", BASE_MODEL_REPO_ID)
+
+if not DATABASE_URL or not HF_TOKEN:
+    raise ValueError("DATABASE_URL and HF_TOKEN environment variables must be set!")
+
+# --- Database Engine & Base (Deklarasi global, inisialisasi di lifespan) ---
+Base = declarative_base()
+engine = None
+SessionLocal = None
+
+
+# ==============================================================================
+# 2. LIFESPAN EVENT HANDLER (FUNGSI STARTUP & SHUTDOWN)
+# ==============================================================================
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """
+    Menangani semua proses yang harus terjadi saat aplikasi pertama kali menyala.
+    """
+    global engine, SessionLocal
+    
+    print("--- Memulai Proses Startup Aplikasi ---")
+
+    # --- INISIALISASI DATABASE ---
+    print("0/5: Menginisialisasi koneksi database...")
+    engine = create_engine(DATABASE_URL)
+    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+    print("Koneksi database siap.")
+    
+    # Langkah 1: Login ke Hugging Face
+    print("1/5: Melakukan login ke Hugging Face Hub...")
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN 
+    print("Login berhasil.")
+
+    # Langkah 2: Memuat Embedding Model untuk RAG
+    print("2/5: Memuat Embedding Model (sentence-transformers/all-MiniLM-L6-v2)...")
+    embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    app.state.embedding_model = embedding_model 
+    print("Embedding Model berhasil dimuat.")
+
+    # Langkah 3: Memuat atau Membuat Vector Store untuk RAG
+    print("3/5: Menginisialisasi RAG Knowledge Base...")
+    vector_store = load_vector_store(embedding_model)
+    if vector_store is None:
+        print("Vector store tidak ditemukan, membuat yang baru...")
+        vector_store = create_vector_store(embedding_model)
+    print("RAG Knowledge Base siap.")
+
+    # Langkah 4: Memuat LLM Utama
+    print(f"4/5: Memuat LLM Utama dari repo: {TUNED_LESSONPLAN_MODEL_ID}...")
+    llm = HuggingFaceEndpoint(
+        repo_id=TUNED_LESSONPLAN_MODEL_ID,
+        huggingfacehub_api_token=HF_TOKEN,
+        temperature=0.7,
+        max_new_tokens=1024,
+        repetition_penalty=1.1
+    )
+    app.state.llm = llm
+    print("LLM Utama berhasil dimuat.")
+
+    # Langkah 5: Membuat RAG Chain dan Menghubungkan ke Database
+    print("5/5: Membuat RAG Chain dan menghubungkan ke database...")
+    app.state.rag_chain = get_rag_chain(llm, vector_store)
+    Base.metadata.create_all(bind=engine)
+    print("Database siap dan RAG Chain berhasil dibuat.")
+    
+    print("--- Startup Selesai. Aplikasi Siap Menerima Permintaan. ---")
+    
+    yield # Aplikasi berjalan di sini
+    
+    print("--- Aplikasi Dimatikan ---")
+
+
+# --- Inisialisasi Aplikasi FastAPI ---
+app = FastAPI(
+    title="Lesson Plan Assistant API",
+    description="API untuk membantu guru membuat Rencana Pelaksanaan Pembelajaran (RPP) melalui dialog diagnostik.",
+    lifespan=lifespan
+)
+
+# ==============================================================================
+# 3. MODEL DATABASE & DEPENDENCY
+# ==============================================================================
+class ApprovedLessonPlanDB(Base):
+    __tablename__ = "approved_lesson_plans"
+    id = Column(Integer, primary_key=True, index=True)
+    subject = Column(String(255), index=True)
+    grade_semester = Column(String(255), index=True)
+    topic = Column(String(500))
+    learning_outcomes = Column(Text)
+    full_json_data = Column(Text)
+    submitted_by_user_id = Column(Integer)
+    is_used_for_training = Column(Boolean, default=False)
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+# ==============================================================================
+# 4. MODEL PYDANTIC (Request & Response)
+# ==============================================================================
+class UserInfo(BaseModel):
+    id: int
+    greeting: str
+    
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+class ChatInput(BaseModel):
+    conversation_id: str
+    user_info: UserInfo
+    prompt: str
+    chat_history: List[ChatMessage]
+    attachment_url: Optional[str] = None
+
+class LessonPlanOutput(BaseModel):
+    lp_subject: str
+    lp_grade_semester: str
+    lp_topic: str
+    lp_learning_outcomes: str
+
+# ==============================================================================
+# 5. ENDPOINTS API
+# ==============================================================================
+@app.post("/chat/")
+async def chat(request: ChatInput):
+    """
+    Endpoint utama untuk dialog dengan AI.
+    """
+    rag_chain = request.app.state.rag_chain
+    if not rag_chain:
+        raise HTTPException(
+            status_code=503, 
+            detail="Layanan Asisten AI tidak siap. Harap coba lagi sesaat."
+        )
+
+    try:
+        input_data = {
+            "question": request.prompt,
+            "chat_history": [msg.model_dump() for msg in request.chat_history]
+        }
+        ai_response = rag_chain.invoke(input_data)
+        return {"reply": ai_response.get("answer", "Maaf, terjadi kesalahan dalam menghasilkan respons.")}
+
+    except Exception as e:
+        print(f"Error during RAG chain invocation: {e}")
+        raise HTTPException(status_code=500, detail="Terjadi kesalahan saat memproses permintaan Anda.")
+
+
+@app.post("/admin/save-approved-plan/")
+async def save_approved_plan(plan: LessonPlanOutput, user_info: UserInfo, db: Session = Depends(get_db)):
+    try:
+        new_plan = ApprovedLessonPlanDB(
+            subject=plan.lp_subject,
+            grade_semester=plan.lp_grade_semester,
+            topic=plan.lp_topic,
+            learning_outcomes=plan.lp_learning_outcomes,
+            full_json_data=plan.model_dump_json(),
+            submitted_by_user_id=user_info.id
+        )
+        db.add(new_plan)
+        db.commit()
+        db.refresh(new_plan)
+        return {"message": "RPP yang disetujui berhasil disimpan untuk training.", "plan_id": new_plan.id}
+    except Exception as e:
+        db.rollback()
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/admin/start-finetune", status_code=202)
+async def trigger_fine_tuning():
+    task = start_lessonplan_finetune_task.delay()
+    return {"message": "Proses fine-tuning Lesson Plan Assistant telah dimulai.", "task_id": task.id}

core/rag_services.py

@@ -0,0 +1,131 @@
+# File: core/rag_services.py (FILE BARU)
+
+import os
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader, Docx2txtLoader
+# --- TAMBAH BARIS INI ---
+from langchain_community.document_loaders import UnstructuredFileLoader
+# -------------------------
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+
+# --- Konfigurasi Path ---
+KNOWLEDGE_BASE_DIR = "knowledge_base"
+FAISS_INDEX_PATH = "faiss_index"
+
+def create_vector_store(embedding_model):
+    """
+    Membangun Vector Store dari dokumen di direktori knowledge_base.
+    Hanya dijalankan jika index FAISS belum ada.
+    """
+    print(f"Membangun Vector Store dari direktori: {KNOWLEDGE_BASE_DIR}")
+    
+    # Loader untuk berbagai jenis file
+    loader = DirectoryLoader(
+        'knowledge_base', 
+        glob="**/*",
+        loader_cls=UnstructuredFileLoader, # Menggantikan loader_map
+        show_progress=True,
+        recursive=True
+    )
+    
+    documents = loader.load()
+    if not documents:
+        print("Peringatan: Tidak ada dokumen yang ditemukan di `knowledge_base`. Kemampuan RAG akan terbatas.")
+        return None
+
+    # Memecah dokumen menjadi bagian-bagian kecil (chunks)
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    docs = text_splitter.split_documents(documents)
+    
+    # Membuat index FAISS dari dokumen dan model embedding
+    print(f"Membuat index FAISS dari {len(docs)} potongan dokumen...")
+    db = FAISS.from_documents(docs, embedding_model)
+    db.save_local(FAISS_INDEX_PATH)
+    print(f"Vector Store berhasil dibuat dan disimpan di: {FAISS_INDEX_PATH}")
+    return db
+
+def load_vector_store(embedding_model):
+    """
+    Memuat Vector Store yang sudah ada dari disk.
+    """
+    if os.path.exists(FAISS_INDEX_PATH):
+        print(f"Memuat Vector Store dari: {FAISS_INDEX_PATH}")
+        db = FAISS.load_local(FAISS_INDEX_PATH, embedding_model, allow_dangerous_deserialization=True)
+        return db
+    return None
+
+def get_rag_chain(llm, vector_store):
+    """
+    Membuat RAG Chain yang akan digunakan untuk chat.
+    Chain ini menggabungkan pencarian konteks dengan LLM.
+    """
+    if not vector_store:
+        raise ValueError("Vector Store tidak tersedia untuk membuat RAG chain.")
+
+    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5}) # Ambil lebih banyak konteks
+
+    # --- [REVISI UTAMA] OTAK BARU UNTUK AGEN 'MEGURI' ---
+    # Prompt ini telah diperbarui untuk diagnosa multi-dimensi.
+    template = """
+    Anda adalah Meguri, seorang Asisten Desain Instruksional AI yang ahli, empatik, dan sangat peduli pada konteks kelas.
+    Tugas utama Anda adalah memandu guru merancang Rencana Pelaksanaan Pembelajaran (RPP) yang efektif, personal, dan relevan dengan kondisi nyata di lapangan.
+
+    PENTING: JANGAN langsung membuat RPP. Misi Anda adalah melakukan DIAGNOSA MENYELURUH melalui dialog yang santai namun mendalam. Gunakan 'Konteks' yang relevan di bawah ini untuk memandu pertanyaan Anda.
+
+    Anda harus menggali TIGA DIMENSI UTAMA sebelum merancang kegiatan pembelajaran:
+
+    1.  DIMENSI KOGNITIF (Prerequisite Knowledge):
+        - Tanyakan topik yang akan diajarkan.
+        - Pahami level pemahaman siswa saat ini terkait topik tersebut.
+        - Contoh pertanyaan: "Untuk topik ini, materi apa yang sebelumnya sudah mereka pelajari?", "Apa miskonsepsi atau kesulitan paling umum yang Bapak/Ibu temui saat mengajarkan topik ini?"
+
+    2.  DIMENSI SOSIAL-EMOSIONAL (Dinamika & Suasana Kelas):
+        - Pahami 'kepribadian' kelas secara umum.
+        - Contoh pertanyaan: "Bagaimana biasanya suasana di kelas Bapak/Ibu? Apakah siswanya cenderung aktif berdiskusi, lebih suka mendengarkan, atau mungkin kelasnya cukup ramai?", "Bagaimana interaksi antar siswa? Apakah mereka nyaman bekerja dalam kelompok?", "Adakah hal-hal khusus terkait perilaku siswa yang perlu kita antisipasi dalam rancangan kegiatan?"
+
+    3.  DIMENSI GAYA BELAJAR & MINAT:
+        - Identifikasi preferensi belajar dan minat siswa.
+        - Contoh pertanyaan: "Dari pengamatan Bapak/Ibu, apakah siswa lebih antusias dengan media visual seperti video/gambar, atau lebih suka kegiatan praktik langsung?", "Adakah minat atau hobi yang sedang populer di kalangan siswa (misal: game, musik, film) yang mungkin bisa kita kaitkan dengan topik agar lebih menarik?"
+
+    Setelah Anda merasa cukup memahami ketiga dimensi tersebut berdasarkan dialog dengan guru, rangkum pemahaman Anda dan minta konfirmasi. Contoh: "Baik, jadi jika saya simpulkan, kita perlu RPP untuk topik X, untuk kelas yang cenderung pasif namun visual, di mana sebagian besar siswa sudah paham konsep dasar A tapi masih kesulitan dengan B. Apakah kesimpulan saya sudah tepat?"
+
+    Setelah guru mengkonfirmasi, barulah Anda menawarkan untuk membuat kerangka RPP yang secara spesifik menjawab semua kebutuhan tersebut.
+
+    Gunakan histori percakapan untuk menjaga konteks. Jika guru bertanya tentang strategi mengajar atau hal lain, jawablah menggunakan 'Konteks' yang tersedia.
+
+    Konteks:
+    {context}
+
+    Histori Percakapan:
+    {chat_history}
+
+    Pertanyaan Guru:
+    {question}
+
+    Jawaban Anda:
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+
+    def format_chat_history(chat_history: list[dict]) -> str:
+        """Mengubah list dict histori chat menjadi format string."""
+        buffer = []
+        for message in chat_history:
+            role = "Guru" if message.get('role') == 'user' else 'Meguri'
+            buffer.append(f"{role}: {message.get('content')}")
+        return "\n".join(buffer)
+
+    rag_chain = (
+        {
+            "context": lambda x: retriever.invoke(x["question"]),
+            "question": lambda x: x["question"],
+            "chat_history": lambda x: format_chat_history(x["chat_history"]),
+        }
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    
+    return rag_chain

docker-compose.yml

@@ -0,0 +1,65 @@
+# File: docker-compose.yml (VERSI KOREKSI ENVIRONMENT)
+
+services:
+  lessonplan_assistant_api:
+    build:
+      context: .
+    container_name: lessonplan_assistant_api
+    ports:
+      - "7860:7860"
+    volumes:
+      - .:/app
+    # --- Tambah Environment Variables ---
+    environment:
+      - HUGGINGFACE_HUB_CACHE=/app/hf_cache # Mengatasi PermissionError cache
+    # ------------------------------------
+    depends_on:
+      lessonplan_db:
+        condition: service_healthy
+      lessonplan_redis:
+        condition: service_started
+
+  lessonplan_db:
+    image: mysql:8.0
+    container_name: lessonplan_db
+    restart: always
+    environment:
+      MYSQL_DATABASE: ${MYSQL_DATABASE}
+      MYSQL_USER: ${MYSQL_USER}
+      MYSQL_PASSWORD: ${MYSQL_PASSWORD}
+      MYSQL_ROOT_PASSWORD: ${MYSQL_ROOT_PASSWORD}
+    ports:
+      - "3307:3306"
+    volumes:
+      - lessonplan_mysql_data:/var/lib/mysql
+    healthcheck:
+      test: ["CMD", "mysqladmin", "ping", "-h", "localhost", "--user=root", "--password=${MYSQL_ROOT_PASSWORD}"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+      start_period: 30s
+
+  lessonplan_redis:
+    image: redis:7-alpine
+    container_name: lessonplan_redis
+    restart: always
+
+  lessonplan_worker:
+    build:
+      context: .
+    container_name: lessonplan_worker
+    command: ["celery", "-A", "tasks.celery_app", "worker", "--loglevel=info"]
+    volumes:
+      - .:/app
+    # --- Tambah Environment Variables ---
+    environment:
+      - HUGGINGFACE_HUB_CACHE=/app/hf_cache # Penting untuk worker finetuning
+    # ------------------------------------
+    depends_on:
+      lessonplan_redis:
+        condition: service_started
+      lessonplan_db:
+        condition: service_healthy
+
+volumes:
+  lessonplan_mysql_data:

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requirements.txt

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+# File: requirements.txt (REVISED TO ADD pypdf)
+
+# --- Core FastAPI & Web Server ---
+fastapi
+uvicorn
+
+# --- LangChain & Hugging Face (Versions are now flexible) ---
+langchain==0.1.20
+langchain-community==0.0.38
+langchain-huggingface
+huggingface_hub
+sentence-transformers
+unstructured[pdf] 
+python-docx
+duckduckgo-search
+pypdf # <-- TAMBAHKAN INI
+
+# --- Database (MySQL) ---
+sqlalchemy
+pymysql
+
+# --- Vector Store ---
+faiss-cpu
+
+# --- Fine-Tuning & PyTorch (Core) ---
+transformers
+peft
+bitsandbytes
+datasets
+accelerate
+torch
+
+# --- Asynchronous Task Queue for Fine-Tuning ---
+celery
+redis

simple_finetune.py

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+# File: simple_finetune.py -script inti untuk proses fine-tuning, yang dijalankan oleh Celery Worker.
+
+import os
+import json
+import pandas as pd
+from sqlalchemy import create_engine
+import torch
+from datasets import Dataset
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    TrainingArguments,
+    Trainer
+)
+from peft import LoraConfig, get_peft_model
+
+# --- 1. Konfigurasi ---
+HF_TOKEN = os.getenv("HF_TOKEN")
+DATABASE_URL = os.getenv("DATABASE_URL")
+BASE_MODEL_ID = "SeaLLMs/SeaLLM-7B-v2.5"
+NEW_TUNED_MODEL_ID = f"nama-anda/lessonplan-assistant-finetuned-v1" # Ganti "nama-anda"
+
+# --- 2. Memuat Data dari Database ---
+def create_lessonplan_dataset_from_db():
+    print("Menghubungkan ke database untuk mengambil data RPP...")
+    engine = create_engine(DATABASE_URL)
+    # Ambil data yang belum pernah digunakan untuk training
+    df = pd.read_sql("SELECT * FROM approved_lesson_plans WHERE is_used_for_training = FALSE", engine)
+    
+    if df.empty:
+        print("Tidak ada data RPP baru untuk training.")
+        return None
+
+    instructions = []
+    for _, row in df.iterrows():
+        rpp_data = json.loads(row['full_json_data'])
+        prompt = f"Buatkan Rencana Pelaksanaan Pembelajaran (RPP) untuk mata pelajaran {rpp_data.get('lp_subject', '')} dengan topik '{rpp_data.get('lp_topic', '')}'."
+        response = json.dumps(rpp_data, indent=2, ensure_ascii=False)
+        instructions.append({"prompt": prompt, "response": response})
+        
+    print(f"Berhasil memuat {len(instructions)} data RPP baru.")
+    return Dataset.from_pandas(pd.DataFrame(instructions))
+
+# --- 3. Proses Fine-Tuning ---
+def main():
+    dataset = create_lessonplan_dataset_from_db()
+    if dataset is None:
+        return
+
+    # ... (Sisa kode sama seperti `simple_finetune.py` sebelumnya) ...
+    # (Memuat model, tokenizer, format data, training, push to hub)
+    # Pastikan setelah push_to_hub berhasil, Anda mengupdate kolom is_used_for_training menjadi TRUE di database.
+
+if __name__ == "__main__":
+    main()

tasks.py

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+# File: tasks.py (File ini mendefinisikan tugas latar belakang -fine-tuning- yang akan dijalankan oleh Celery.)
+
+import os
+from celery import Celery
+import subprocess
+
+# Ambil URL broker dari environment variable
+CELERY_BROKER_URL = os.getenv('CELERY_BROKER_URL', 'redis://redis:6379/0')
+
+# Inisialisasi aplikasi Celery
+celery_app = Celery('lessonplan_tasks', broker=CELERY_BROKER_URL)
+
+@celery_app.task(name='tasks.start_lessonplan_finetune_task')
+def start_lessonplan_finetune_task():
+    """
+    Tugas ini memanggil script fine-tuning utama.
+    Ini akan dijalankan oleh Celery Worker di server GPU.
+    """
+    try:
+        print("Memulai proses fine-tuning untuk Lesson Plan Assistant...")
+        
+        # Menjalankan script sebagai proses terpisah
+        # Pastikan environment worker memiliki akses ke semua environment variable
+        result = subprocess.run(
+            ["python", "simple_finetune.py"],
+            check=True, 
+            capture_output=True, 
+            text=True
+        )
+        
+        print("Proses fine-tuning selesai.")
+        print("Output:", result.stdout)
+        return {"status": "Completed", "output": result.stdout}
+        
+    except subprocess.CalledProcessError as e:
+        print(f"Error saat fine-tuning: {e.stderr}")
+        return {"status": "Failed", "error": e.stderr}