"""
Streamlit app containing the UI and the application logic.
"""
import datetime
import logging
import pathlib
import random
import sys
import tempfile
from typing import List, Union

import huggingface_hub
import json5
import requests
import streamlit as st
from langchain_community.chat_message_histories import StreamlitChatMessageHistory
from langchain_core.messages import HumanMessage
from langchain_core.prompts import ChatPromptTemplate

sys.path.append('..')
sys.path.append('../..')

from global_config import GlobalConfig
from helpers import llm_helper, pptx_helper, text_helper


@st.cache_data
def _load_strings() -> dict:
    """
    Load various strings to be displayed in the app.
    :return: The dictionary of strings.
    """

    with open(GlobalConfig.APP_STRINGS_FILE, 'r', encoding='utf-8') as in_file:
        return json5.loads(in_file.read())


@st.cache_data
def _get_prompt_template(is_refinement: bool) -> str:
    """
    Return a prompt template.

    :param is_refinement: Whether this is the initial or refinement prompt.
    :return: The prompt template as f-string.
    """

    if is_refinement:
        with open(GlobalConfig.REFINEMENT_PROMPT_TEMPLATE, 'r', encoding='utf-8') as in_file:
            template = in_file.read()
    else:
        with open(GlobalConfig.INITIAL_PROMPT_TEMPLATE, 'r', encoding='utf-8') as in_file:
            template = in_file.read()

    return template


@st.cache_resource
def _get_llm(repo_id: str, max_new_tokens: int):
    """
    Get an LLM instance.

    :param repo_id: The model name.
    :param max_new_tokens: The max new tokens to generate.
    :return: The LLM.
    """

    return llm_helper.get_hf_endpoint(repo_id, max_new_tokens)


APP_TEXT = _load_strings()

# Session variables
CHAT_MESSAGES = 'chat_messages'
DOWNLOAD_FILE_KEY = 'download_file_name'
IS_IT_REFINEMENT = 'is_it_refinement'


logger = logging.getLogger(__name__)

texts = list(GlobalConfig.PPTX_TEMPLATE_FILES.keys())
captions = [GlobalConfig.PPTX_TEMPLATE_FILES[x]['caption'] for x in texts]

with st.sidebar:
    pptx_template = st.sidebar.radio(
        'Select a presentation template:',
        texts,
        captions=captions,
        horizontal=True
    )
    st.divider()
    llm_to_use = st.sidebar.selectbox(
        'Select an LLM to use:',
        [f'{k} ({v["description"]})' for k, v in GlobalConfig.HF_MODELS.items()]
    ).split(' ')[0]


def build_ui():
    """
    Display the input elements for content generation.
    """

    st.title(APP_TEXT['app_name'])
    st.subheader(APP_TEXT['caption'])
    st.markdown(
        '![Visitors](https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fbarunsaha%2Fslide-deck-ai&countColor=%23263759)'  # noqa: E501
    )

    with st.expander('Usage Policies and Limitations'):
        st.text(APP_TEXT['tos'] + '\n\n' + APP_TEXT['tos2'])

    set_up_chat_ui()


def set_up_chat_ui():
    """
    Prepare the chat interface and related functionality.
    """

    with st.expander('Usage Instructions'):
        st.markdown(GlobalConfig.CHAT_USAGE_INSTRUCTIONS)
        st.markdown(
            '[SlideDeck AI](https://github.com/barun-saha/slide-deck-ai) is an Open-Source project.'  # noqa: E501
            ' It is is powered by'  # noqa: E501
            ' [Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407).'  # noqa: E501
        )

    st.info(
        'If you like SlideDeck AI, please consider leaving a heart ❤️ on the'
        ' [Hugging Face Space](https://huggingface.co/spaces/barunsaha/slide-deck-ai/) or'
        ' a star ⭐ on [GitHub](https://github.com/barun-saha/slide-deck-ai).'
        ' Your [feedback](https://forms.gle/JECFBGhjvSj7moBx9) is appreciated.'
    )

    # view_messages = st.expander('View the messages in the session state')

    st.chat_message('ai').write(
        random.choice(APP_TEXT['ai_greetings'])
    )

    history = StreamlitChatMessageHistory(key=CHAT_MESSAGES)

    if _is_it_refinement():
        template = _get_prompt_template(is_refinement=True)
    else:
        template = _get_prompt_template(is_refinement=False)

    prompt_template = ChatPromptTemplate.from_template(template)

    # Since Streamlit app reloads at every interaction, display the chat history
    # from the save session state
    for msg in history.messages:
        msg_type = msg.type
        if msg_type == 'user':
            st.chat_message(msg_type).write(msg.content)
        else:
            st.chat_message(msg_type).code(msg.content, language='json')

    if prompt := st.chat_input(
        placeholder=APP_TEXT['chat_placeholder'],
        max_chars=GlobalConfig.LLM_MODEL_MAX_INPUT_LENGTH
    ):
        if not text_helper.is_valid_prompt(prompt):
            st.error(
                'Not enough information provided!'
                ' Please be a little more descriptive and type a few words'
                ' with a few characters :)'
            )
            return

        logger.info(
            'User input: %s | #characters: %d | LLM: %s',
            prompt, len(prompt), llm_to_use
        )
        st.chat_message('user').write(prompt)

        user_messages = _get_user_messages()
        user_messages.append(prompt)
        list_of_msgs = [
            f'{idx + 1}. {msg}' for idx, msg in enumerate(user_messages)
        ]
        list_of_msgs = '\n'.join(list_of_msgs)

        if _is_it_refinement():
            formatted_template = prompt_template.format(
                **{
                    'instructions': list_of_msgs,
                    'previous_content': _get_last_response(),
                }
            )
        else:
            formatted_template = prompt_template.format(
                **{
                    'question': prompt,
                }
            )

        progress_bar = st.progress(0, 'Preparing to call LLM...')
        response = ''

        try:
            for chunk in _get_llm(
                    repo_id=llm_to_use,
                    max_new_tokens=GlobalConfig.HF_MODELS[llm_to_use]['max_new_tokens']
            ).stream(formatted_template):
                response += chunk

                # Update the progress bar
                progress_percentage = min(
                    len(response) / GlobalConfig.HF_MODELS[llm_to_use]['max_new_tokens'], 0.95
                )
                progress_bar.progress(
                    progress_percentage,
                    text='Streaming content...this might take a while...'
                )
        except requests.exceptions.ConnectionError:
            msg = (
                'A connection error occurred while streaming content from the LLM endpoint.'
                ' Unfortunately, the slide deck cannot be generated. Please try again later.'
                ' Alternatively, try selecting a different LLM from the dropdown list.'
            )
            logger.error(msg)
            st.error(msg)
            return
        except huggingface_hub.errors.ValidationError as ve:
            msg = (
                f'An error occurred while trying to generate the content: {ve}'
                '\nPlease try again with a significantly shorter input text.'
            )
            logger.error(msg)
            st.error(msg)
            return
        except Exception as ex:
            msg = (
                f'An unexpected error occurred while generating the content: {ex}'
                '\nPlease try again later, possibly with different inputs.'
                ' Alternatively, try selecting a different LLM from the dropdown list.'
            )
            logger.error(msg)
            st.error(msg)
            return

        history.add_user_message(prompt)
        history.add_ai_message(response)

        # The content has been generated as JSON
        # There maybe trailing ``` at the end of the response -- remove them
        # To be careful: ``` may be part of the content as well when code is generated
        response_cleaned = text_helper.get_clean_json(response)

        logger.info(
            'Cleaned JSON response:: original length: %d | cleaned length: %d',
            len(response), len(response_cleaned)
        )
        # logger.debug('Cleaned JSON: %s', response_cleaned)

        # Now create the PPT file
        progress_bar.progress(
            GlobalConfig.LLM_PROGRESS_MAX,
            text='Finding photos online and generating the slide deck...'
        )
        path = generate_slide_deck(response_cleaned)
        progress_bar.progress(1.0, text='Done!')

        st.chat_message('ai').code(response, language='json')

        if path:
            _display_download_button(path)

        logger.info(
            '#messages in history / 2: %d',
            len(st.session_state[CHAT_MESSAGES]) / 2
        )


def generate_slide_deck(json_str: str) -> Union[pathlib.Path, None]:
    """
    Create a slide deck and return the file path. In case there is any error creating the slide
    deck, the path may be to an empty file.

    :param json_str: The content in *valid* JSON format.
    :return: The path to the .pptx file or `None` in case of error.
    """

    try:
        parsed_data = json5.loads(json_str)
    except ValueError:
        st.error(
            'Encountered error while parsing JSON...will fix it and retry'
        )
        logger.error(
            'Caught ValueError: trying again after repairing JSON...'
        )
        try:
            parsed_data = json5.loads(text_helper.fix_malformed_json(json_str))
        except ValueError:
            st.error(
                'Encountered an error again while fixing JSON...'
                'the slide deck cannot be created, unfortunately ☹'
                '\nPlease try again later.'
            )
            logger.error(
                'Caught ValueError: failed to repair JSON!'
            )

            return None
    except RecursionError:
        st.error(
            'Encountered an error while parsing JSON...'
            'the slide deck cannot be created, unfortunately ☹'
            '\nPlease try again later.'
        )
        logger.error('Caught RecursionError while parsing JSON. Cannot generate the slide deck!')

        return None
    except Exception:
        st.error(
            'Encountered an error while parsing JSON...'
            'the slide deck cannot be created, unfortunately ☹'
            '\nPlease try again later.'
        )
        logger.error(
            'Caught ValueError: failed to parse JSON!'
        )

        return None

    if DOWNLOAD_FILE_KEY in st.session_state:
        path = pathlib.Path(st.session_state[DOWNLOAD_FILE_KEY])
    else:
        temp = tempfile.NamedTemporaryFile(delete=False, suffix='.pptx')
        path = pathlib.Path(temp.name)
        st.session_state[DOWNLOAD_FILE_KEY] = str(path)

        if temp:
            temp.close()

    try:
        logger.debug('Creating PPTX file: %s...', st.session_state[DOWNLOAD_FILE_KEY])
        pptx_helper.generate_powerpoint_presentation(
            parsed_data,
            slides_template=pptx_template,
            output_file_path=path
        )
    except Exception as ex:
        st.error(APP_TEXT['content_generation_error'])
        logger.error('Caught a generic exception: %s', str(ex))

    return path


def _is_it_refinement() -> bool:
    """
    Whether it is the initial prompt or a refinement.

    :return: True if it is the initial prompt; False otherwise.
    """

    if IS_IT_REFINEMENT in st.session_state:
        return True

    if len(st.session_state[CHAT_MESSAGES]) >= 2:
        # Prepare for the next call
        st.session_state[IS_IT_REFINEMENT] = True
        return True

    return False


def _get_user_messages() -> List[str]:
    """
    Get a list of user messages submitted until now from the session state.

    :return: The list of user messages.
    """

    return [
        msg.content for msg in st.session_state[CHAT_MESSAGES] if isinstance(msg, HumanMessage)
    ]


def _get_last_response() -> str:
    """
    Get the last response generated by AI.

    :return: The response text.
    """

    return st.session_state[CHAT_MESSAGES][-1].content


def _display_messages_history(view_messages: st.expander):
    """
    Display the history of messages.

    :param view_messages: The list of AI and Human messages.
    """

    with view_messages:
        view_messages.json(st.session_state[CHAT_MESSAGES])


def _display_download_button(file_path: pathlib.Path):
    """
    Display a download button to download a slide deck.

    :param file_path: The path of the .pptx file.
    """

    with open(file_path, 'rb') as download_file:
        st.download_button(
            'Download PPTX file ⬇️',
            data=download_file,
            file_name='Presentation.pptx',
            key=datetime.datetime.now()
        )


def main():
    """
    Trigger application run.
    """

    build_ui()


if __name__ == '__main__':
    main()