app.py

import gradio as gr
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from llama_cpp import Llama
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.llama_cpp import LlamaCPP
from huggingface_hub import hf_hub_download
from llama_index.core.llms import ChatMessage
from llama_index.core.chat_engine.condense_plus_context import CondensePlusContextChatEngine
from llama_index.core.schema import Document

# ==============================
# 1️⃣ Fungsi Membaca Data Google Spreadsheet (Cuti, Lembur, Absen, Target)
# ==============================
def read_google_sheets():
    try:
        scope = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
        creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.json", scope)
        client = gspread.authorize(creds)
        
        SPREADSHEET_ID = "1e_cNMhwF-QYpyYUpqQh-XCw-OdhWS6EuYsoBUsVtdNg"
        sheet_names = ["datatarget", "datacuti", "dataabsen", "datalembur"]

        all_data = []
        spreadsheet = client.open_by_key(SPREADSHEET_ID)
        
        for sheet_name in sheet_names:
            try:
                sheet = spreadsheet.worksheet(sheet_name)
                data = sheet.get_all_values()
                all_data.append(f"=== Data dari {sheet_name.upper()} ===")
                all_data.extend([" | ".join(row) for row in data])
                all_data.append("\n")
            except gspread.exceptions.WorksheetNotFound:
                all_data.append(f"❌ ERROR: Worksheet {sheet_name} tidak ditemukan.")

        return "\n".join(all_data).strip()
    
    except gspread.exceptions.SpreadsheetNotFound:
        return "❌ ERROR: Spreadsheet tidak ditemukan!"
    
    except Exception as e:
        return f"❌ ERROR: {str(e)}"

# ==============================
# 2️⃣ Fungsi Membaca Data dari File TXT (PKB dan Dokumen Lainnya)
# ==============================
def read_txt_documents():
    documents = SimpleDirectoryReader(input_files=[
        "bahandokumen/K3.txt", "bahandokumen/bonus.txt", "bahandokumen/absensi.txt", 
        "bahandokumen/cuti.txt", "bahandokumen/disiplinkerja.txt", "bahandokumen/fasilitas&bantuan.txt", 
        "bahandokumen/fasilitaskerja.txt", "bahandokumen/hak.txt", "bahandokumen/hubunganpengusaha&serikat.txt", 
        "bahandokumen/istilah.txt", "bahandokumen/jaminanserikat.txt", "bahandokumen/jamkes.txt", 
        "bahandokumen/jamsos.txt", "bahandokumen/keluhkesah.txt", "bahandokumen/kenaikanupah.txt", 
        "bahandokumen/kewajiban.txt", "bahandokumen/kompensasi.txt", "bahandokumen/larangan.txt", 
        "bahandokumen/lembur.txt", "bahandokumen/luaskesepakatan.txt", "bahandokumen/mogok.txt", 
        "bahandokumen/pelanggaran&sanksi.txt", "bahandokumen/pendidikan.txt", "bahandokumen/pengangkatan.txt", 
        "bahandokumen/penilaian&promosi.txt", "bahandokumen/pensiun.txt", "bahandokumen/perjadin.txt", 
        "bahandokumen/pesangon.txt", "bahandokumen/phk.txt", "bahandokumen/pihak.txt", "bahandokumen/pkb.txt", 
        "bahandokumen/resign.txt", "bahandokumen/sanksi.txt", "bahandokumen/shift.txt", "bahandokumen/syaratkerja.txt", 
        "bahandokumen/sisacuti.txt", "bahandokumen/target.txt", "bahandokumen/tatacara.txt", "bahandokumen/tka.txt", 
        "bahandokumen/tunjangan.txt", "bahandokumen/uangpisah.txt", "bahandokumen/upah.txt", "bahandokumen/upahlembur.txt", 
        "bahandokumen/waktukerja.txt"
    ]).load_data()
    return documents

# ==============================
# 3️⃣ Inisialisasi Model Llama
# ==============================
def initialize_llama_model():
    model_path = hf_hub_download(
        repo_id="TheBLoke/zephyr-7b-beta-GGUF",
        filename="zephyr-7b-beta.Q4_K_M.gguf",
        cache_dir="./models"
    )
    return model_path

# ==============================
# 4️⃣ Inisialisasi Pengaturan Model
# ==============================
def initialize_settings(model_path):
    Settings.llm = LlamaCPP(model_path=model_path, temperature=0.7)

# ==============================
# 5️⃣ Inisialisasi Index
# ==============================
def initialize_index():
    text_data = read_google_sheets()
    document = Document(text=text_data)
    txt_documents = read_txt_documents()
    
    all_documents = [document] + txt_documents  # Gabungkan dokumen CSV dan TXT
    parser = SentenceSplitter(chunk_size=150, chunk_overlap=10)
    nodes = parser.get_nodes_from_documents(all_documents)
    
    embedding = HuggingFaceEmbedding("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
    Settings.embed_model = embedding
    
    index = VectorStoreIndex(nodes)
    return index

# ==============================
# 6️⃣ Inisialisasi Chat Engine
# ==============================
def initialize_chat_engine(index):
    retriever = index.as_retriever(similarity_top_k=3)
    chat_engine = CondensePlusContextChatEngine.from_defaults(retriever=retriever, verbose=False)
    return chat_engine

# ==============================
# 7️⃣ Fungsi untuk Menjawab Chat
# ==============================
def generate_response(message, history, chat_engine):
    if history is None:
        history = []

    chat_messages = [
        ChatMessage(
            role="system",
            content=(
                "Anda adalah chatbot HRD yang membantu karyawan memahami administrasi perusahaan. "
                "Gunakan Bahasa Indonesia dengan gaya profesional dan ramah. "
                "Jika informasi tidak tersedia dalam dokumen, katakan dengan sopan bahwa Anda tidak tahu. "
                "Jawaban harus singkat, jelas, dan sesuai konteks."
            ),
        ),
    ]

    response = chat_engine.stream_chat(message)
    cleaned_text = clean_response(response)  # 🔹 Gunakan fungsi clean_response()

    history.append((message, cleaned_text))  # 🔹 Pastikan hanya teks yang masuk ke history
    return cleaned_text

# ==============================
# 8️⃣ Fungsi Utama Menjalankan Aplikasi
# ==============================
def main():
    model_path = initialize_llama_model()
    initialize_settings(model_path)
    
    index = initialize_index()
    chat_engine = initialize_chat_engine(index)
    
    def chatbot_response(message, history):
        return generate_response(message, history, chat_engine)
    
    gr.Interface(fn=chatbot_response, inputs="text", outputs="text").launch()

if __name__ == "__main__":
    main()