import base64
import os
import re
from hashlib import blake2b
from tempfile import NamedTemporaryFile

import dotenv
from grobid_quantities.quantities import QuantitiesAPI
from langchain.llms.huggingface_hub import HuggingFaceHub
from langchain.memory import ConversationBufferWindowMemory

dotenv.load_dotenv(override=True)

import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings

from document_qa.document_qa_engine import DocumentQAEngine
from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_text_with_annotations
from grobid_client_generic import GrobidClientGeneric

if 'rqa' not in st.session_state:
    st.session_state['rqa'] = {}

if 'model' not in st.session_state:
    st.session_state['model'] = None

if 'api_keys' not in st.session_state:
    st.session_state['api_keys'] = {}

if 'doc_id' not in st.session_state:
    st.session_state['doc_id'] = None

if 'loaded_embeddings' not in st.session_state:
    st.session_state['loaded_embeddings'] = None

if 'hash' not in st.session_state:
    st.session_state['hash'] = None

if 'git_rev' not in st.session_state:
    st.session_state['git_rev'] = "unknown"
    if os.path.exists("revision.txt"):
        with open("revision.txt", 'r') as fr:
            from_file = fr.read()
            st.session_state['git_rev'] = from_file if len(from_file) > 0 else "unknown"

if "messages" not in st.session_state:
    st.session_state.messages = []

if 'ner_processing' not in st.session_state:
    st.session_state['ner_processing'] = False

if 'uploaded' not in st.session_state:
    st.session_state['uploaded'] = False

if 'memory' not in st.session_state:
    st.session_state['memory'] = ConversationBufferWindowMemory(k=4)

if 'binary' not in st.session_state:
    st.session_state['binary'] = None

st.set_page_config(
    page_title="Scientific Document Insights Q/A",
    page_icon="📝",
    initial_sidebar_state="expanded",
    layout="wide",
    menu_items={
        'Get Help': 'https://github.com/lfoppiano/document-qa',
        'Report a bug': "https://github.com/lfoppiano/document-qa/issues",
        'About': "Upload a scientific article in PDF, ask questions, get insights."
    }
)

css_modify_left_column = '''
<style>                 
    [data-testid="stHorizontalBlock"] > div:nth-child(1) {
        overflow: hidden;
        background-color: red;
        height: 70vh;
    }
</style>
'''
css_modify_right_column = '''
<style>                 
    [data-testid="stHorizontalBlock"]> div:first-child {
        background-color: red;
        position: fixed
        height: 70vh;
    }
</style>
'''
css_disable_scrolling_container = '''
<style>
    [data-testid="ScrollToBottomContainer"] {
        overflow: hidden;
    }
</style>
'''


# st.markdown(css_lock_column_fixed, unsafe_allow_html=True)
# st.markdown(css2, unsafe_allow_html=True)


def new_file():
    st.session_state['loaded_embeddings'] = None
    st.session_state['doc_id'] = None
    st.session_state['uploaded'] = True
    st.session_state['memory'].clear()


def clear_memory():
    st.session_state['memory'].clear()


# @st.cache_resource
def init_qa(model, api_key=None):
    ## For debug add: callbacks=[PromptLayerCallbackHandler(pl_tags=["langchain", "chatgpt", "document-qa"])])
    if model == 'chatgpt-3.5-turbo':
        if api_key:
            chat = ChatOpenAI(model_name="gpt-3.5-turbo",
                              temperature=0,
                              openai_api_key=api_key,
                              frequency_penalty=0.1)
            embeddings = OpenAIEmbeddings(openai_api_key=api_key)
        else:
            chat = ChatOpenAI(model_name="gpt-3.5-turbo",
                              temperature=0,
                              frequency_penalty=0.1)
            embeddings = OpenAIEmbeddings()

    elif model == 'mistral-7b-instruct-v0.1':
        chat = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.1",
                              model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
        embeddings = HuggingFaceEmbeddings(
            model_name="all-MiniLM-L6-v2")

    elif model == 'zephyr-7b-beta':
        chat = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta",
                              model_kwargs={"temperature": 0.01, "max_length": 4096, "max_new_tokens": 2048})
        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    else:
        st.error("The model was not loaded properly. Try reloading. ")
        st.stop()
        return

    return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'], memory=st.session_state['memory'])


@st.cache_resource
def init_ner():
    quantities_client = QuantitiesAPI(os.environ['GROBID_QUANTITIES_URL'], check_server=True)

    materials_client = GrobidClientGeneric(ping=True)
    config_materials = {
        'grobid': {
            "server": os.environ['GROBID_MATERIALS_URL'],
            'sleep_time': 5,
            'timeout': 60,
            'url_mapping': {
                'processText_disable_linking': "/service/process/text?disableLinking=True",
                # 'processText_disable_linking': "/service/process/text"
            }
        }
    }

    materials_client.set_config(config_materials)

    gqa = GrobidAggregationProcessor(None,
                                     grobid_quantities_client=quantities_client,
                                     grobid_superconductors_client=materials_client
                                     )
    return gqa


gqa = init_ner()


def get_file_hash(fname):
    hash_md5 = blake2b()
    with open(fname, "rb") as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hash_md5.update(chunk)
    return hash_md5.hexdigest()


def play_old_messages():
    if st.session_state['messages']:
        for message in st.session_state['messages']:
            if message['role'] == 'user':
                with st.chat_message("user"):
                    st.markdown(message['content'])
            elif message['role'] == 'assistant':
                with st.chat_message("assistant"):
                    if mode == "LLM":
                        st.markdown(message['content'], unsafe_allow_html=True)
                    else:
                        st.write(message['content'])


# is_api_key_provided = st.session_state['api_key']

with st.sidebar:
    st.session_state['model'] = model = st.radio(
        "Model",
        ("chatgpt-3.5-turbo", "mistral-7b-instruct-v0.1", "zephyr-7b-beta"),
        index=2,
        captions=[
            "ChatGPT 3.5 Turbo + Ada-002-text (embeddings)",
            "Mistral-7B-Instruct-V0.1 + Sentence BERT (embeddings) :free:",
            "Zephyr-7B-beta + Sentence BERT (embeddings) :free:"
        ],
        help="Select the LLM model and embeddings you want to use.",
        disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded'])

    st.markdown(
        ":warning: Mistral and Zephyr are **FREE** to use. Requests might fail anytime. Use at your own risk. :warning: ")

    if (model == 'mistral-7b-instruct-v0.1' or model == 'zephyr-7b-beta') and model not in st.session_state['api_keys']:
        if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ:
            api_key = st.text_input('Huggingface API Key', type="password")

            st.markdown("Get it [here](https://huggingface.co/docs/hub/security-tokens)")
        else:
            api_key = os.environ['HUGGINGFACEHUB_API_TOKEN']

        if api_key:
            # st.session_state['api_key'] = is_api_key_provided = True
            if model not in st.session_state['rqa'] or model not in st.session_state['api_keys']:
                with st.spinner("Preparing environment"):
                    st.session_state['api_keys'][model] = api_key
                    # if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ:
                    #     os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
                    st.session_state['rqa'][model] = init_qa(model)

    elif model == 'chatgpt-3.5-turbo' and model not in st.session_state['api_keys']:
        if 'OPENAI_API_KEY' not in os.environ:
            api_key = st.text_input('OpenAI API Key', type="password")
            st.markdown("Get it [here](https://platform.openai.com/account/api-keys)")
        else:
            api_key = os.environ['OPENAI_API_KEY']

        if api_key:
            if model not in st.session_state['rqa'] or model not in st.session_state['api_keys']:
                with st.spinner("Preparing environment"):
                    st.session_state['api_keys'][model] = api_key
                    if 'OPENAI_API_KEY' not in os.environ:
                        st.session_state['rqa'][model] = init_qa(model, api_key)
                    else:
                        st.session_state['rqa'][model] = init_qa(model)
    # else:
    #     is_api_key_provided = st.session_state['api_key']

    st.button(
        'Reset chat memory.',
        key="reset-memory-button",
        on_click=clear_memory,
        help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.")

left_column, right_column = st.columns([1, 1])

with right_column:
    st.title("📝 Scientific Document Insights Q/A")
    st.subheader("Upload a scientific article in PDF, ask questions, get insights.")

    st.markdown(
        ":warning: Do not upload sensitive data. We **temporarily** store text from the uploaded PDF documents solely for the purpose of processing your request, and we **do not assume responsibility** for any subsequent use or handling of the data submitted to third parties LLMs.")

    uploaded_file = st.file_uploader("Upload an article",
                                 type=("pdf", "txt"),
                                 on_change=new_file,
                                     disabled=st.session_state['model'] is not None and st.session_state['model'] not in
                                              st.session_state['api_keys'],
                                     help="The full-text is extracted using Grobid. ")

question = st.chat_input(
    "Ask something about the article",
    # placeholder="Can you give me a short summary?",
    disabled=not uploaded_file
)

with st.sidebar:
    st.header("Settings")
    mode = st.radio("Query mode", ("LLM", "Embeddings"), disabled=not uploaded_file, index=0, horizontal=True,
                    help="LLM will respond the question, Embedding will show the "
                         "paragraphs relevant to the question in the paper.")
    chunk_size = st.slider("Chunks size", 100, 2000, value=250,
                           help="Size of chunks in which the document is partitioned",
                           disabled=uploaded_file is not None)
    context_size = st.slider("Context size", 3, 10, value=4,
                             help="Number of chunks to consider when answering a question",
                             disabled=not uploaded_file)

    st.session_state['ner_processing'] = st.checkbox("Named Entities Recognition (NER) processing on LLM response")
    st.markdown(
        '**NER on LLM responses**: The responses from the LLMs are post-processed to extract <span style="color:orange">physical quantities, measurements</span> and <span style="color:green">materials</span> mentions.',
        unsafe_allow_html=True)

    st.divider()

    st.header("Documentation")
    st.markdown("https://github.com/lfoppiano/document-qa")
    st.markdown(
        """Upload a scientific article as PDF document. Once the spinner stops, you can proceed to ask your questions.""")

    if st.session_state['git_rev'] != "unknown":
        st.markdown("**Revision number**: [" + st.session_state[
            'git_rev'] + "](https://github.com/lfoppiano/document-qa/commit/" + st.session_state['git_rev'] + ")")

    st.header("Query mode (Advanced use)")
    st.markdown(
        """By default, the mode is set to LLM (Language Model) which enables question/answering. You can directly ask questions related to the document content, and the system will answer the question using content from the document.""")

    st.markdown(
        """If you switch the mode to "Embedding," the system will return specific chunks from the document that are semantically related to your query. This mode helps to test why sometimes the answers are not satisfying or incomplete. """)


@st.cache_resource
def get_pdf_display(binary):
    base64_pdf = base64.b64encode(binary).decode('utf-8')
    return F'<embed src="data:application/pdf;base64,{base64_pdf}" width="100%" height="700" type="application/pdf"></embed>'


if uploaded_file and not st.session_state.loaded_embeddings:
    if model not in st.session_state['api_keys']:
        st.error("Before uploading a document, you must enter the API key. ")
        st.stop()

    with right_column:
        with st.spinner('Reading file, calling Grobid, and creating memory embeddings...'):
            binary = uploaded_file.getvalue()
            tmp_file = NamedTemporaryFile()
            tmp_file.write(bytearray(binary))
            st.session_state['binary'] = binary

            st.session_state['doc_id'] = hash = st.session_state['rqa'][model].create_memory_embeddings(tmp_file.name,
                                                                                                        chunk_size=chunk_size,
                                                                                                    perc_overlap=0.1,
                                                                                                    include_biblio=True)
            st.session_state['loaded_embeddings'] = True
            st.session_state.messages = []

    # timestamp = datetime.utcnow()

with left_column:
    if st.session_state['binary']:
        left_column.markdown(get_pdf_display(st.session_state['binary']), unsafe_allow_html=True)

with right_column:
    # css = '''
    #     <style>
    #         [data-testid="column"] {
    #             overflow: auto;
    #             height: 70vh;
    #         }
    #     </style>
    #     '''
    # st.markdown(css, unsafe_allow_html=True)

    # st.markdown(
    #     """
    #     <script>
    #     document.querySelectorAll('[data-testid="column"]').scrollIntoView({behavior: "smooth"});
    #     </script>
    #     """,
    #     unsafe_allow_html=True,
    # )

    if st.session_state.loaded_embeddings and question and len(question) > 0 and st.session_state.doc_id:
        for message in st.session_state.messages:
            with st.chat_message(message["role"]):
                if message['mode'] == "LLM":
                    st.markdown(message["content"], unsafe_allow_html=True)
                elif message['mode'] == "Embeddings":
                    st.write(message["content"])
        if model not in st.session_state['rqa']:
            st.error("The API Key for the " + model + " is  missing. Please add it before sending any query. `")
            st.stop()

        with st.chat_message("user"):
            st.markdown(question)
            st.session_state.messages.append({"role": "user", "mode": mode, "content": question})

        text_response = None
        if mode == "Embeddings":
            with st.spinner("Generating LLM response..."):
                text_response = st.session_state['rqa'][model].query_storage(question, st.session_state.doc_id,
                                                                             context_size=context_size)
        elif mode == "LLM":
            with st.spinner("Generating response..."):
                _, text_response = st.session_state['rqa'][model].query_document(question, st.session_state.doc_id,
                                                                             context_size=context_size)

        if not text_response:
            st.error("Something went wrong. Contact Luca Foppiano (Foppiano.Luca@nims.co.jp) to report the issue.")

        with st.chat_message("assistant"):
            if mode == "LLM":
                if st.session_state['ner_processing']:
                    with st.spinner("Processing NER on LLM response..."):
                        entities = gqa.process_single_text(text_response)
                        decorated_text = decorate_text_with_annotations(text_response.strip(), entities)
                        decorated_text = decorated_text.replace('class="label material"', 'style="color:green"')
                        decorated_text = re.sub(r'class="label[^"]+"', 'style="color:orange"', decorated_text)
                        text_response = decorated_text
                st.markdown(text_response, unsafe_allow_html=True)
            else:
                st.write(text_response)
            st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response})

        # if len(st.session_state.messages) > 1:
        #     last_answer = st.session_state.messages[len(st.session_state.messages)-1]
        #     if last_answer['role'] == "assistant":
        #         last_question = st.session_state.messages[len(st.session_state.messages)-2]
        #         st.session_state.memory.save_context({"input": last_question['content']}, {"output": last_answer['content']})

    elif st.session_state.loaded_embeddings and st.session_state.doc_id:
        play_old_messages()