import logging
import time
from pathlib import Path

import gradio as gr
import nltk
from cleantext import clean

from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
from utils import load_example_filenames, truncate_word_count

_here = Path(__file__).parent

nltk.download("stopwords")  # TODO=find where this requirement originates from

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)


def proc_submission(
    input_text: str,
    model_size: str,
    num_beams,
    token_batch_length,
    length_penalty,
    repetition_penalty,
    no_repeat_ngram_size,
    max_input_length: int = 768,
):
    """
    proc_submission - a helper function for the gradio module to process submissions

    Args:
        input_text (str): the input text to summarize
        model_size (str): the size of the model to use
        num_beams (int): the number of beams to use
        token_batch_length (int): the length of the token batches to use
        length_penalty (float): the length penalty to use
        repetition_penalty (float): the repetition penalty to use
        no_repeat_ngram_size (int): the no repeat ngram size to use
        max_input_length (int, optional): the maximum input length to use. Defaults to 768.

    Returns:
        str in HTML format, string of the summary, str of score
    """

    settings = {
        "length_penalty": float(length_penalty),
        "repetition_penalty": float(repetition_penalty),
        "no_repeat_ngram_size": int(no_repeat_ngram_size),
        "encoder_no_repeat_ngram_size": 4,
        "num_beams": int(num_beams),
        "min_length": 4,
        "max_length": int(token_batch_length // 4),
        "early_stopping": True,
        "do_sample": False,
    }
    st = time.perf_counter()
    history = {}
    clean_text = clean(input_text, lower=False)
    max_input_length = 2048 if model_size == "base" else max_input_length
    processed = truncate_word_count(clean_text, max_input_length)

    if processed["was_truncated"]:
        tr_in = processed["truncated_text"]
        msg = f"Input text was truncated to {max_input_length} words (based on whitespace)"
        logging.warning(msg)
        history["WARNING"] = msg
    else:
        tr_in = input_text
        msg = None

    _summaries = summarize_via_tokenbatches(
        tr_in,
        model_sm if model_size == "base" else model,
        tokenizer_sm if model_size == "base" else tokenizer,
        batch_length=token_batch_length,
        **settings,
    )
    sum_text = [f"Section {i}: " + s["summary"][0] for i, s in enumerate(_summaries)]
    sum_scores = [
        f" - Section {i}: {round(s['summary_score'],4)}"
        for i, s in enumerate(_summaries)
    ]

    sum_text_out = "\n".join(sum_text)
    history["Summary Scores"] = "<br><br>"
    scores_out = "\n".join(sum_scores)
    rt = round((time.perf_counter() - st) / 60, 2)
    print(f"Runtime: {rt} minutes")
    html = ""
    html += f"<p>Runtime: {rt} minutes on CPU</p>"
    if msg is not None:
        html += f"<h2>WARNING:</h2><hr><b>{msg}</b><br><br>"

    html += ""

    return html, sum_text_out, scores_out


def load_single_example_text(
    example_path: str or Path,
):
    """
    load_single_example - a helper function for the gradio module to load examples
    Returns:
        list of str, the examples
    """
    global name_to_path
    full_ex_path = name_to_path[example_path]
    full_ex_path = Path(full_ex_path)
    # load the examples into a list
    with open(full_ex_path, "r", encoding="utf-8", errors="ignore") as f:
        raw_text = f.read()
        text = clean(raw_text, lower=False)
    return text


def load_uploaded_file(file_obj):
    """
    load_uploaded_file - process an uploaded file

    Args:
        file_obj (POTENTIALLY list): Gradio file object inside a list

    Returns:
        str, the uploaded file contents
    """

    # file_path = Path(file_obj[0].name)

    # check if mysterious file object is a list
    if isinstance(file_obj, list):
        file_obj = file_obj[0]
    file_path = Path(file_obj.name)
    try:
        with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
            raw_text = f.read()
        text = clean(raw_text, lower=False)
        return text
    except Exception as e:
        logging.info(f"Trying to load file with path {file_path}, error: {e}")
        return "Error: Could not read file. Ensure that it is a valid text file with encoding UTF-8."


if __name__ == "__main__":

    model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-book-summary")
    model_sm, tokenizer_sm = load_model_and_tokenizer("pszemraj/led-base-book-summary")

    name_to_path = load_example_filenames(_here / "examples")
    logging.info(f"Loaded {len(name_to_path)} examples")
    demo = gr.Blocks()

    with demo:

        gr.Markdown("# Long-Form Summarization: LED & BookSum")
        gr.Markdown(
            "A simple demo using a fine-tuned LED model to summarize long-form text. See [model card](https://huggingface.co/pszemraj/led-large-book-summary) for a notebook with GPU inference (much faster) on Colab."
        )
        with gr.Column():

            gr.Markdown("## Load Inputs & Select Parameters")
            gr.Markdown(
                "Enter text below in the text area. The text will be summarized [using the selected parameters](https://huggingface.co/blog/how-to-generate). Optionally load an example from the list below or upload a file."
            )
            gr.Markdown("_The base model is less performant than the large model, but is faster and will accept up to 2048 words per input (Large model accepts up to 768)._")
            model_size = gr.Radio(
                choices=["base", "large"], label="model size", value="large"
            )
            num_beams = gr.Radio(
                choices=[2, 3, 4],
                label="num beams",
                value=2,
            )
            token_batch_length = gr.Radio(
                choices=[512, 768, 1024],
                label="token batch length",
                value=512,
            )
            length_penalty = gr.inputs.Slider(
                minimum=0.5, maximum=1.0, label="length penalty", default=0.7, step=0.05
            )
            repetition_penalty = gr.inputs.Slider(
                minimum=1.0,
                maximum=5.0,
                label="repetition penalty",
                default=3.5,
                step=0.1,
            )
            no_repeat_ngram_size = gr.Radio(
                choices=[2, 3, 4],
                label="no repeat ngram size",
                value=3,
            )
            example_name = gr.Dropdown(
                list(name_to_path.keys()),
                label="Choose an Example",
            )
            load_examples_button = gr.Button(
                "Load Example",
            )
            input_text = gr.Textbox(
                lines=6,
                label="input text",
                placeholder="Enter text to summarize, the text will be cleaned and truncated on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well. May take a bit to generate depending on the input text :)",
            )
            gr.Markdown("Upload your own file:")
            uploaded_file = gr.File(
                label="Upload a text file",
                file_count="single",
                type="file",
            )
            load_file_button = gr.Button("Load Uploaded File")

            gr.Markdown("---")

        with gr.Column():
            gr.Markdown("## Generate Summary")
            gr.Markdown(
                "Summary generation should take approximately 1-2 minutes for most settings."
            )
            summarize_button = gr.Button("Summarize!")

            output_text = gr.HTML("<p><em>Output will appear below:</em></p>")
            gr.Markdown("### Summary Output")
            summary_text = gr.Textbox(
                label="Summary", placeholder="The generated summary will appear here"
            )
            gr.Markdown(
                "The summary scores can be thought of as representing the quality of the summary. less-negative numbers (closer to 0) are better:"
            )
            summary_scores = gr.Textbox(
                label="Summary Scores", placeholder="Summary scores will appear here"
            )

            gr.Markdown("---")

        with gr.Column():
            gr.Markdown("## About the Model")
            gr.Markdown(
                "- [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned checkpoint of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209).The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage."
            )
            gr.Markdown(
                "- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. However, increasing these will also increase the amount of time it takes to generate a summary. The `length_penalty` and `repetition_penalty` parameters are also important for the model to generate good summaries."
            )
            gr.Markdown(
                "- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial."
            )
            gr.Markdown("---")

        load_examples_button.click(
            fn=load_single_example_text, inputs=[example_name], outputs=[input_text]
        )

        load_file_button.click(
            fn=load_uploaded_file, inputs=uploaded_file, outputs=[input_text]
        )

        summarize_button.click(
            fn=proc_submission,
            inputs=[
                input_text,
                model_size,
                num_beams,
                token_batch_length,
                length_penalty,
                repetition_penalty,
                no_repeat_ngram_size,
            ],
            outputs=[output_text, summary_text, summary_scores],
        )

    demo.launch(enable_queue=True, share=True)