import json
import gradio as gr
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from llama_cpp import Llama
from llama_index.core import 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 PKB.json
# ===================================
def read_pkb_json():
    try:
        with open("pkb.json", "r", encoding="utf-8") as file:
            data = json.load(file)
        
        pkb_text = "=== Perjanjian Kerja Bersama ===\n"
        for bab, content in data["perjanjian_kerja_bersama"].items():
            pkb_text += f"\n## {content['judul']} ##\n"
            for pasal, pasal_data in content.items():
                if pasal != "judul":
                    pkb_text += f"\n### {pasal_data['judul']} ###\n"
                    for item in pasal_data["isi"]:
                        if isinstance(item, dict):
                            pkb_text += f"- {item['istilah']}: {item['definisi']}\n"
                        else:
                            pkb_text += f"- {item}\n"
        return pkb_text
    except Exception as e:
        return f"❌ ERROR membaca PKB.json: {str(e)}"

# ===================================
# 2️⃣ Fungsi Membaca Data Google Spreadsheet
# ===================================
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)
    except Exception as e:
        return f"❌ ERROR: {str(e)}"

# ===================================
# 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 Index Data PKB & Spreadsheet
# ===================================
def initialize_index():
    text_data = read_google_sheets() + "\n" + read_pkb_json()
    document = Document(text=text_data)
    parser = SentenceSplitter(chunk_size=150, chunk_overlap=10)
    nodes = parser.get_nodes_from_documents([document])
    embedding = HuggingFaceEmbedding("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
    Settings.embed_model = embedding
    index = VectorStoreIndex(nodes)
    return index

# ===================================
# 5️⃣ Inisialisasi Chatbot
# ===================================
def initialize_chat_engine(index):
    retriever = index.as_retriever(similarity_top_k=3)
    chat_engine = CondensePlusContextChatEngine.from_defaults(
        retriever=retriever,
        verbose=True,
    )
    return chat_engine

# ===================================
# 6️⃣ Fungsi untuk Respons Chatbot
# ===================================
def generate_response(message, history, chat_engine):
    if history is None:
        history = []
    
    chat_messages = [
        ChatMessage(
            role="system",
            content=(
                "Anda adalah chatbot yang dirancang khusus untuk berbicara dalam Bahasa Indonesia. "
                "Anda adalah chatbot HRD yang membantu karyawan dalam memahami administrasi dan data perusahaan. "
                "Anda tidak diperbolehkan menjawab dalam bahasa lain, termasuk Inggris. "
                "Gunakan gaya bahasa profesional tetapi tetap ramah. "
                "Jika informasi tidak tersedia dalam dokumen, katakan dengan sopan bahwa Anda tidak tahu. "
                "Pastikan setiap jawaban diberikan secara ringkas, jelas, dan sesuai konteks."
                "Jawaban harus singkat, jelas, dan dalam Bahasa Indonesia."
            ),
        ),
    ]
    
    response = chat_engine.stream_chat(message)
    text = "".join(response.response_gen)
    
    history.append((message, text))
    return history

# ===================================
# 7️⃣ Fungsi Utama untuk Menjalankan Aplikasi
# ===================================
def main():
    model_path = initialize_llama_model()
    Settings.llm = LlamaCPP(
        model_path=model_path,
        temperature=0.7,
    )
    
    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()