# app.py
from typing import List, Union, Optional

from dotenv import load_dotenv, find_dotenv
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.schema import (SystemMessage, HumanMessage, AIMessage)
from langchain.llms import LlamaCpp
from langchain.embeddings import LlamaCppEmbeddings
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.text_splitter import TokenTextSplitter
from langchain.prompts import PromptTemplate
from langchain.vectorstores import Qdrant
from PyPDF2 import PdfReader
import streamlit as st

# Use a pipeline as a high-level helper
#from transformers import pipeline
#pipe = pipeline("text-generation", model="TheBloke/Llama-2-7B-Chat-GGML")
# Load model directly
#from transformers import AutoModel
#model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML")

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id="TheBloke/Llama-2-7B-Chat-GGML"
tokenizer=AutoTokenizer.from_pretrained(model_id)
model=AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

PROMPT_TEMPLATE = """
Use the following pieces of context enclosed by triple backquotes to answer the question at the end.
\n\n
Context:
```
{context}
```
\n\n
Question: [][][][]{question}[][][][]
\n
Answer:"""


def init_page() -> None:
    st.set_page_config(
        page_title="Personal ChatGPT"
    )
    st.sidebar.title("Options")


def init_messages() -> None:
    clear_button = st.sidebar.button("Clear Conversation", key="clear")
    if clear_button or "messages" not in st.session_state:
        st.session_state.messages = [
            SystemMessage(
                content=(
                    "You are a helpful AI QA assistant. "
                    "When answering questions, use the context enclosed by triple backquotes if it is relevant. "
                    "If you don't know the answer, just say that you don't know, "
                    "don't try to make up an answer. "
                    "Reply your answer in mardkown format.")
            )
        ]
        st.session_state.costs = []


def get_pdf_text() -> Optional[str]:
    """
    Function to load PDF text and split it into chunks.
    """
    st.header("Document Upload")
    uploaded_file = st.file_uploader(
        label="Here, upload your PDF file you want ChatGPT to use to answer",
        type="pdf"
    )
    if uploaded_file:
        pdf_reader = PdfReader(uploaded_file)
        text = "\n\n".join([page.extract_text() for page in pdf_reader.pages])
        text_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=0)
        return text_splitter.split_text(text)
    else:
        return None


def build_vectore_store(
    texts: str, embeddings: Union[OpenAIEmbeddings, LlamaCppEmbeddings]) \
        -> Optional[Qdrant]:
    """
    Store the embedding vectors of text chunks into vector store (Qdrant).
    """
    if texts:
        with st.spinner("Loading PDF ..."):
            qdrant = Qdrant.from_texts(
                texts,
                embeddings,
                path=":memory:",
                collection_name="my_collection",
                force_recreate=True
            )
        st.success("File Loaded Successfully!!")
    else:
        qdrant = None
    return qdrant


def select_llm() -> Union[ChatOpenAI, LlamaCpp]:
    """
    Read user selection of parameters in Streamlit sidebar.
    """
    model_name = st.sidebar.radio("Choose LLM:",
                                  ("llama-2-7b-chat.ggmlv3.q2_K",
                                   "gpt-3.5-turbo-0613",
                                   "gpt-3.5-turbo-16k-0613",
                                   "gpt-4"))
    temperature = st.sidebar.slider("Temperature:", min_value=0.0,
                                    max_value=1.0, value=0.0, step=0.01)
    return model_name, temperature


def load_llm(model_name: str, temperature: float) -> Union[ChatOpenAI, LlamaCpp]:
    """
    Load LLM.
    """
    if model_name.startswith("gpt-"):
        return ChatOpenAI(temperature=temperature, model_name=model_name)
    elif model_name.startswith("llama-2-"):
        callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
        
        '''return LlamaCpp(
            model_path=f"./models/{model_name}.bin",
            input={"temperature": temperature,
                   "max_length": 2048,
                   "top_p": 1
                   },
            n_ctx=2048,
            callback_manager=callback_manager,
            verbose=False,  # True
        )'''
        return model


def load_embeddings(model_name: str) -> Union[OpenAIEmbeddings, LlamaCppEmbeddings]:
    """
    Load embedding model.
    """
    if model_name.startswith("gpt-"):
        return OpenAIEmbeddings()
    elif model_name.startswith("llama-2-"):
        return LlamaCppEmbeddings(model_path=f"./models/{model_name}.bin")


def get_answer(llm, messages) -> tuple[str, float]:
    """
    Get the AI answer to user questions.
    """
    if isinstance(llm, ChatOpenAI):
        with get_openai_callback() as cb:
            answer = llm(messages)
        return answer.content, cb.total_cost
    if isinstance(llm, LlamaCpp):
        return llm(llama_v2_prompt(convert_langchainschema_to_dict(messages))), 0.0


def find_role(message: Union[SystemMessage, HumanMessage, AIMessage]) -> str:
    """
    Identify role name from langchain.schema object.
    """
    if isinstance(message, SystemMessage):
        return "system"
    if isinstance(message, HumanMessage):
        return "user"
    if isinstance(message, AIMessage):
        return "assistant"
    raise TypeError("Unknown message type.")


def convert_langchainschema_to_dict(
        messages: List[Union[SystemMessage, HumanMessage, AIMessage]]) \
        -> List[dict]:
    """
    Convert the chain of chat messages in list of langchain.schema format to
    list of dictionary format.
    """
    return [{"role": find_role(message),
             "content": message.content
             } for message in messages]


def llama_v2_prompt(messages: List[dict]) -> str:
    """
    Convert the messages in list of dictionary format to Llama2 compliant
    format.
    """
    B_INST, E_INST = "[INST]", "[/INST]"
    B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
    BOS, EOS = "<s>", "</s>"
    DEFAULT_SYSTEM_PROMPT = f"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""

    if messages[0]["role"] != "system":
        messages = [
            {
                "role": "system",
                "content": DEFAULT_SYSTEM_PROMPT,
            }
        ] + messages
    messages = [
        {
            "role": messages[1]["role"],
            "content": B_SYS + messages[0]["content"] + E_SYS + messages[1]["content"],
        }
    ] + messages[2:]

    messages_list = [
        f"{BOS}{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} {EOS}"
        for prompt, answer in zip(messages[::2], messages[1::2])
    ]
    messages_list.append(
        f"{BOS}{B_INST} {(messages[-1]['content']).strip()} {E_INST}")

    return "".join(messages_list)


def extract_userquesion_part_only(content):
    """
    Function to extract only the user question part from the entire question
    content combining user question and pdf context.
    """
    content_split = content.split("[][][][]")
    if len(content_split) == 3:
        return content_split[1]
    return content


def main() -> None:
    _ = load_dotenv(find_dotenv())

    init_page()

    model_name, temperature = select_llm()
    llm = load_llm(model_name, temperature)
    embeddings = load_embeddings(model_name)

    texts = get_pdf_text()
    qdrant = build_vectore_store(texts, embeddings)

    init_messages()

    st.header("Personal ChatGPT")
    # Supervise user input
    if user_input := st.chat_input("Input your question!"):
        if qdrant:
            context = [c.page_content for c in qdrant.similarity_search(
                user_input, k=10)]
            user_input_w_context = PromptTemplate(
                template=PROMPT_TEMPLATE,
                input_variables=["context", "question"]) \
                .format(
                    context=context, question=user_input)
        else:
            user_input_w_context = user_input
        st.session_state.messages.append(
            HumanMessage(content=user_input_w_context))
        with st.spinner("ChatGPT is typing ..."):
            answer, cost = get_answer(llm, st.session_state.messages)
        st.session_state.messages.append(AIMessage(content=answer))
        st.session_state.costs.append(cost)

    # Display chat history
    messages = st.session_state.get("messages", [])
    for message in messages:
        if isinstance(message, AIMessage):
            with st.chat_message("assistant"):
                st.markdown(message.content)
        elif isinstance(message, HumanMessage):
            with st.chat_message("user"):
                st.markdown(extract_userquesion_part_only(message.content))

    costs = st.session_state.get("costs", [])
    st.sidebar.markdown("## Costs")
    st.sidebar.markdown(f"**Total cost: ${sum(costs):.5f}**")
    for cost in costs:
        st.sidebar.markdown(f"- ${cost:.5f}")


# streamlit run app.py
if __name__ == "__main__":
    main()