app.py

import streamlit as st
import os
from dotenv import load_dotenv
from langsmith import traceable
from datetime import datetime
from typing import List, Dict, Optional

from app.chat import initialize_session_state, display_chat_history
from app.data_loader import get_data, list_all_files, load_docs
from app.document_processor import process_documents, save_vector_store_to_supabase, load_vector_store_from_supabase
from app.prompts import sahabat_prompt
from app.db import supabase
from langchain_community.llms import Replicate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_community.document_transformers import LongContextReorder

load_dotenv()

# ---------------------------------------------------------
# ⚡️ CONFIG
# ---------------------------------------------------------
BUCKET_NAME = "pnp-bot-storage-archive"
VECTOR_STORE_PREFIX = "vector_store"

# ---------------------------------------------------------
# ⚡️ UTILITY
# ---------------------------------------------------------
def get_latest_data_timestamp_from_files(bucket_name: str) -> float:
    """Get the latest timestamp from files in a Supabase storage bucket."""
    files = list_all_files(bucket_name)
    latest_time = 0.0
    for file in files:
        iso_time = file.get("updated_at") or file.get("created_at")
        if iso_time:
            try:
                timestamp = datetime.fromisoformat(iso_time.replace('Z', '+00:00')).timestamp()
                latest_time = max(latest_time, timestamp)
            except Exception as e:
                print(f"Gagal parsing waktu dari {file.get('name')}: {e}")
    return latest_time


def get_supabase_vector_store_timestamp() -> Optional[str]:
    """Get the latest timestamp of vector store files in the Supabase storage."""
    try:
        response = supabase.storage.from_(BUCKET_NAME).list()
        timestamps = []
        for file in response:
            if file["name"].startswith(VECTOR_STORE_PREFIX) and (
                file["name"].endswith(".faiss") or file["name"].endswith(".pkl")
            ):
                timestamps.append(file["updated_at"])
        if len(timestamps) >= 2:
            return max(timestamps)
        return None
    except Exception as e:
        print(f"Error getting Supabase timestamp: {e}")
        return None


def vector_store_is_outdated() -> bool:
    """Check if vector store needs to be updated based on files in Supabase storage."""
    supabase_timestamp = get_supabase_vector_store_timestamp()
    if supabase_timestamp is None:
        return True
    supabase_time = datetime.fromisoformat(supabase_timestamp.replace("Z", "+00:00")).timestamp()
    data_time = get_latest_data_timestamp_from_files("pnp-bot-storage")

    return data_time > supabase_time


def reorder_embedding(docs):
    """Reorder documents for long context retrieval."""
    reordering = LongContextReorder()
    return reordering.transform_documents(docs)


# ---------------------------------------------------------
# ⚡️ RAG CHAIN
# ---------------------------------------------------------
@traceable(name="Create RAG Conversational Chain")
def create_conversational_chain(vector_store):
    """Create a Conversational Retrieval Chain for RAG."""
    llm = Replicate(
        model="fauziisyrinapridal/sahabat-ai-v1:afb9fa89fe786362f619fd4fef34bd1f7a4a4da23073d8a6fbf54dcbe458f216",
        model_kwargs={"temperature": 0.1, "top_p": 0.9, "max_new_tokens": 6000}
    )
    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key="answer")
    chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=vector_store.as_retriever(search_kwargs={"k": 6}),
        combine_docs_chain_kwargs={"prompt": sahabat_prompt},
        return_source_documents=True,
        memory=memory,
    )
    return chain


def get_rag_chain(vector_store):
    """Return a Conversational Retrieval Chain for external use."""
    return create_conversational_chain(vector_store)


# ---------------------------------------------------------
# ⚡️ MAIN FUNCTION
# ---------------------------------------------------------
@traceable(name="Main Chatbot RAG App")
def main():
    initialize_session_state()
    vector_store = None

    if len(st.session_state["history"]) == 0:
        if vector_store_is_outdated():
            with st.spinner("Memuat dan memproses dokumen..."):
                get_data()
                docs = load_docs()
                if len(docs) > 0:
                    reordered_docs = reorder_embedding(docs)
                    vector_store = process_documents(reordered_docs)

                    with st.spinner("Mengunggah vector store ke Supabase..."):
                        success = save_vector_store_to_supabase(vector_store, supabase, BUCKET_NAME, VECTOR_STORE_PREFIX)
                        if success:
                            st.success("✅ Vector store berhasil diunggah ke Supabase!")
                        else:
                            st.error("❌ Gagal mengunggah vector store ke Supabase.")
                else:
                    st.warning("⚠️ Folder 'data/' kosong. Chatbot tetap bisa digunakan, tetapi tanpa konteks dokumen.")
                    vector_store = None
        else:
            with st.spinner("Memuat vector store dari Supabase..."):
                vector_store = load_vector_store_from_supabase(supabase, BUCKET_NAME, VECTOR_STORE_PREFIX)
                if vector_store:
                    st.success("✅ Vector store berhasil dimuat dari Supabase!")
                else:
                    st.error("❌ Gagal memuat vector store dari Supabase.")
    else:
        vector_store = st.session_state.get("vector_store") or load_vector_store_from_supabase(supabase, BUCKET_NAME, VECTOR_STORE_PREFIX)

    st.session_state["vector_store"] = vector_store

    if st.session_state["vector_store"] is not None:
        chain = create_conversational_chain(st.session_state["vector_store"])
        display_chat_history(chain)


if __name__ == "__main__":
    main()