import io
import os
import re
import tarfile

import anthropic
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import openai
import pandas as pd
import requests
import seaborn as sns
from tqdm import tqdm


def download_arxiv_source(paper_id):
    url = f"https://arxiv.org/e-print/{paper_id}"

    # Get the tar file
    response = requests.get(url)
    response.raise_for_status()

    # Open the tar file
    tar = tarfile.open(fileobj=io.BytesIO(response.content), mode="r")

    # Load all .tex files into memory, including their subdirectories
    tex_files = {
        member.name: tar.extractfile(member).read().decode("utf-8")
        for member in tar.getmembers()
        if member.name.endswith(".tex")
    }

    # Pattern to match \input{filename} and \include{filename}
    pattern = re.compile(r"\\(input|include){(.*?)}")

    # Function to replace \input{filename} and \include{filename} with file contents
    def replace_includes(text):
        output = []
        for line in text.split("\n"):
            match = re.search(pattern, line)
            if match:
                command, filename = match.groups()
                # LaTeX automatically adds .tex extension for \include command
                if command == "include":
                    filename += ".tex"
                if filename in tex_files:
                    output.append(replace_includes(tex_files[filename]))
                else:
                    output.append(f"% {line} % FILE NOT FOUND")
            else:
                output.append(line)
        return "\n".join(output)

    if "main.tex" in tex_files:
        # Start with the contents of main.tex
        main_tex = replace_includes(tex_files["main.tex"])
    else:
        # No main.tex, concatenate all .tex files
        main_tex = "\n".join(replace_includes(text) for text in tex_files.values())

    return main_tex


class ContextualQA:
    def __init__(self, client, model="claude-v1.3-100k"):
        self.client = client
        self.model = model
        self.context = ""
        self.questions = []
        self.responses = []

    def load_text(self, text):
        self.context = text

    def ask_question(self, question):
        leading_prompt = "Consider the text document below:"
        trailing_prompt = (
            "Now answer the following question, use Markdown to format your answer."
        )
        prompt = f"{anthropic.HUMAN_PROMPT} {leading_prompt}\n\n{self.context}\n\n{trailing_prompt}\n\n{anthropic.HUMAN_PROMPT} {question} {anthropic.AI_PROMPT}"
        response = self.client.completion_stream(
            prompt=prompt,
            stop_sequences=[anthropic.HUMAN_PROMPT],
            max_tokens_to_sample=6000,
            model=self.model,
            stream=False,
        )
        responses = [data for data in response]
        self.questions.append(question)
        self.responses.append(responses)
        return responses

    def clear_context(self):
        self.context = ""
        self.questions = []
        self.responses = []

    def __getstate__(self):
        state = self.__dict__.copy()
        del state["client"]
        return state

    def __setstate__(self, state):
        self.__dict__.update(state)
        self.client = None


def load_context(paper_id):
    latex_source = download_arxiv_source(paper_id)
    client = anthropic.Client(api_key=os.environ["ANTHROPIC_API_KEY"])
    model = ContextualQA(client, model="claude-v1.3-100k")
    model.load_text(latex_source)
    return (
        model,
        [
            (
                f"Load the paper with id {paper_id}.",
                "Paper loaded, You can now ask questions.",
            )
        ],
    )


def answer_fn(model, question, chat_history):
    # if question is empty, tell user that they need to ask a question
    if question == "":
        chat_history.append(("No Question Asked", "Please ask a question."))
        return model, chat_history, ""

    client = anthropic.Client(api_key=os.environ["ANTHROPIC_API_KEY"])
    model.client = client

    response = model.ask_question(question)

    chat_history.append((question, response[0]["completion"]))
    return model, chat_history, ""


def clear_context():
    return []


with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        "# Explore ArXiv Papers in Depth with `claude-v1.3-100k` - Ask Questions and Receive Detailed Answers Instantly"
    )
    gr.Markdown(
        "Dive into the world of academic papers with our dynamic app, powered by the cutting-edge `claude-v1.3-100k` model. This app allows you to ask detailed questions about any ArXiv paper and receive direct answers from the paper's content. Utilizing a context length of 100k tokens, it provides an efficient and comprehensive exploration of complex research studies, making knowledge acquisition simpler and more interactive. (This text is generated by GPT-4 )"
    )

    with gr.Column():
        with gr.Row():
            paper_id_input = gr.Textbox(label="Enter Paper ID", value="2108.07258")
            btn_load = gr.Button("Load Paper")
            qa_model = gr.State()

        with gr.Column():
            chatbot = gr.Chatbot().style(color_map=("blue", "yellow"))
            question_txt = gr.Textbox(
                label="Question", lines=1, placeholder="Type your question here..."
            )
            btn_answer = gr.Button("Answer Question")

            btn_clear = gr.Button("Clear Chat")

    btn_load.click(load_context, inputs=[paper_id_input], outputs=[qa_model, chatbot])

    btn_answer.click(
        answer_fn,
        inputs=[qa_model, question_txt, chatbot],
        outputs=[qa_model, chatbot, question_txt],
    )

    question_txt.submit(
        answer_fn,
        inputs=[qa_model, question_txt, chatbot],
        outputs=[qa_model, chatbot, question_txt],
    )

    btn_clear.click(clear_context, outputs=[chatbot])

demo.launch()