import os
import pickle
from typing import Optional, Tuple
import gradio as gr
from threading import Lock

from langchain.llms import OpenAI
from langchain.chains import ChatVectorDBChain
from template import QA_PROMPT, CONDENSE_QUESTION_PROMPT
from pdf2vectorstore import convert_to_vectorstore

def get_chain(api_key, vectorstore, model_name):
    llm = OpenAI(model_name = model_name, temperature=0,  openai_api_key=api_key)
    qa_chain = ChatVectorDBChain.from_llm(
        llm,
        vectorstore,
        qa_prompt=QA_PROMPT,
        condense_question_prompt=CONDENSE_QUESTION_PROMPT,
    )
    return qa_chain

def set_openai_api_key(api_key: str, vectorstore, model_name: str):
    if api_key:
        chain = get_chain(api_key, vectorstore, model_name) 
        return chain

class ChatWrapper:

    def __init__(self):
        self.lock = Lock()
        self.previous_url = ""
        self.vectorstore_state = None
        self.chain = None

    def __call__(
        self, 
        api_key: str, 
        arxiv_url: str, 
        inp: str, 
        history: Optional[Tuple[str, str]],
        model_name: str,
    ):
        if not arxiv_url or not api_key:
            history = history or []
            history.append((inp, "Please provide both arXiv URL and API key to begin"))
            return history, history

        if arxiv_url != self.previous_url:
            history = []
            vectorstore = convert_to_vectorstore(arxiv_url, api_key)
            self.previous_url = arxiv_url
            self.chain  = set_openai_api_key(api_key, vectorstore, model_name)
            self.vectorstore_state = vectorstore
        
        if self.chain  is None:
            self.chain  = set_openai_api_key(api_key, self.vectorstore_state, model_name)
        
        self.lock.acquire()
        try:
            history = history or []
            if self.chain  is None:
                history.append((inp, "Please paste your OpenAI key to use"))
                return history, history
            import openai
            openai.api_key = api_key
            output = self.chain ({"question": inp, "chat_history": history})["answer"]
            history.append((inp, output))
        except Exception as e:
            raise e
        finally:
            api_key = ""
            self.lock.release()
        return history, history

chat = ChatWrapper()

block = gr.Blocks(css=".gradio-container {background-color: #f8f8f8; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif}")

with block:
    gr.HTML("""
    <style>
        body {
            background-color: #f5f5f5;
            font-family: 'Roboto', sans-serif;
            padding: 30px;
        }
    </style>
    """)
    
    gr.HTML("<h1 style='text-align: center;'>ArxivGPT</h1>")
    gr.HTML("<h3 style='text-align: center;'>Ask questions about research papers</h3>")
    
    with gr.Row():
        with gr.Column(width="auto"):
            openai_api_key_textbox = gr.Textbox(
                label="OpenAI API Key",
                placeholder="Paste your OpenAI API key (sk-...)",
                show_label=True,
                lines=1,
                type="password",
            )
        with gr.Column(width="auto"):
            arxiv_url_textbox = gr.Textbox(
                label="Arxiv URL",
                placeholder="Enter the arXiv URL",
                show_label=True,
                lines=1,
            )
        with gr.Column(width="auto"):
            model_dropdown = gr.Dropdown(
                label="Choose a model (GPT-4 coming soon!)",
                choices=["gpt-3.5-turbo"],
            )

    chatbot = gr.Chatbot()

    with gr.Row():
        message = gr.Textbox(
            label="What's your question?",
            placeholder="Ask questions about the paper you just linked",
            lines=1,
        )
        submit = gr.Button(value="Send", variant="secondary").style(full_width=False)

    gr.Examples(
        examples=[
            "Please give me a brief summary about this paper",
            "Are there any interesting correlations in the given paper?",
            "How can this paper be applied in the real world?",
            "What are the limitations of this paper?",
        ],
        inputs=message,
    )
    gr.HTML("""
            <div style="text-align:center">
                <p>Developed by <a href='https://www.linkedin.com/in/dekay/'>Github and Huggingface: Volkopat</a></p>
                <p>Powered by <a href='https://openai.com/'>OpenAI</a>, <a href='https://arxiv.org/'>arXiv</a> and <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></p>
                <p>ArxivGPT is a chatbot that answers questions about research papers. It uses a pretrained GPT-3.5 model to generate answers.</p>
                <p>Currently, it can answer questions about the paper you just linked.</p>
                <p>It's still in development, so please report any bugs you find. It can take up to a minute to start a conversation for every new paper as there is a parsing delay.</p>
                <p>The answers can be quite limited as there is a 4096 token limit for GPT-3.5, hence waiting for GPT-4 access to upgrade.</p>
                <p>Possible upgrades coming up: GPT-4, faster parsing, status messages, other research paper hubs.</p>
            </div>
            <style>
                p {
                    margin-bottom: 10px;
                    font-size: 16px;
                }
                a {
                    color: #3867d6;
                    text-decoration: none;
                }
                a:hover {
                    text-decoration: underline;
                }
            </style>
            """)

    state = gr.State()

    submit.click(chat, 
                 inputs=[openai_api_key_textbox, arxiv_url_textbox, message, state, model_dropdown], 
                 outputs=[chatbot, state])
    message.submit(chat, 
                   inputs=[openai_api_key_textbox, arxiv_url_textbox, message, state, model_dropdown], 
                   outputs=[chatbot, state])

block.launch(width=800)