import streamlit as st
import os
import tempfile
from dotenv import load_dotenv

from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

# Ortam değişkenlerini yükle
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")

st.set_page_config(page_title="ChatGPT PDF Botu", page_icon="💬", layout="wide")
st.markdown(
    """
    <h2 style='text-align: center;'>📄 Customer Service Bot</h2>
    <p style='text-align: center; color: #888;'>PDF içeriğinden ChatGPT tarzı sohbet ile bilgi alın.</p>
    """,
    unsafe_allow_html=True
)

# Sayfa ikiye böl: Solda PDF yükleme, sağda sohbet
col1, col2 = st.columns([1, 2])

with col1:
    st.subheader("📄 PDF Yükle")
    uploaded_file = st.file_uploader("PDF dosyanızı yükleyin", type="pdf", key="pdf_uploader")

    if uploaded_file is not None:
        if "last_uploaded_name" not in st.session_state or uploaded_file.name != st.session_state.last_uploaded_name:
            with st.spinner("PDF işleniyor..."):
                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
                    tmp.write(uploaded_file.read())
                    tmp_path = tmp.name

                loader = PyPDFLoader(tmp_path)
                documents = loader.load()

                splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
                docs = splitter.split_documents(documents)

                embedding = OpenAIEmbeddings(model="text-embedding-3-large")
                vectordb = FAISS.from_documents(docs, embedding)

                memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
                llm = ChatOpenAI(model_name="gpt-4", temperature=0)

                qa_chain = ConversationalRetrievalChain.from_llm(
                    llm=llm,
                    retriever=vectordb.as_retriever(search_kwargs={"k": 3}),
                    memory=memory
                )

                st.session_state.qa_chain = qa_chain
                st.session_state.chat_history = []
                st.session_state.last_uploaded_name = uploaded_file.name

            st.success("✅ PDF başarıyla işlendi!")

with col2:
    st.subheader("💬 Sohbet Alanı")

    if "qa_chain" not in st.session_state:
        st.info("Lütfen sol taraftan bir PDF yükleyin.")
    else:
        if "chat_history" not in st.session_state:
            st.session_state.chat_history = []

        # Sohbeti chat balonları olarak göster
        chat_placeholder = st.container()
        with chat_placeholder:
            for sender, message in st.session_state.chat_history:
                align = "flex-end" if sender == "🧑‍💼 Siz" else "flex-start"
                color = "#DCF8C6" if sender == "🧑‍💼 Siz" else "#F1F0F0"
                st.markdown(
                    f"""
                    <div style='display: flex; justify-content: {align};'>
                        <div style='background: {color}; border-radius: 12px; padding: 8px 14px; margin: 6px 0; max-width: 70%;'>
                            <b>{sender}:</b> {message}
                        </div>
                    </div>
                    """,
                    unsafe_allow_html=True
                )

        # Sohbet giriş kutusu aşağıda sabit
        with st.form(key="user_input_form", clear_on_submit=True):
            user_input = st.text_input("Soru sorun veya yazışmaya devam edin...", key="input_field")
            submit = st.form_submit_button("Gönder")

        if submit and user_input:
            with st.spinner("Yanıtlanıyor..."):
                response = st.session_state.qa_chain.run(user_input)
                st.session_state.chat_history.append(("🧑‍💼 Siz", user_input))
                st.session_state.chat_history.append(("🤖 Bot", response))
            st.rerun()