import json
import os
import shutil
import requests

import gradio as gr
from huggingface_hub import Repository, InferenceClient

HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-180B-chat"
BOT_NAME = "Falcon"

STOP_SEQUENCES = ["\nUser:", "<|endoftext|>", " User:", "###"]

EXAMPLES = [["climate change"], ["2308.15699"], ["hallucination"], ["2308.00205"], ["large language model"], ["2308.05204"], ["2308.10873"], ["2308.06355"],["2308.01684"],["2308.00352"],["2308.07773"]]

client = InferenceClient(
    API_URL,
    headers={"Authorization": f"Bearer {HF_TOKEN}"},
)

id_dict = {}
for i in range(0,4):
    fname = "arxiv_2023_" + str(i)
    with open(fname, "r") as f:
        for line in f:
            D = json.loads(line)
            id_dict[D['id']] = D


def format_prompt_summarize(message, history, system_prompt, keyword):

  prompt = ""
  prompt += "System: You are scholarly RESEARCH ASSISTANT who can read the ARXIV scholarly article.\n"
  prompt += "User: READ ALL THE TITLEs and ABSTRACTs of various article below\n"
  prompt += "Generate a SUMMARY of all the articles below relevant to the research for the field of \"" + keyword + "\"\n"
  prompt += "SUGGEST FIVE IMPORTANT FINDINGS or ORIGINAL CONTRIBUTIONS of OBSERVATIONs for the field of \"" + keyword + "\" that summarizes the work.\n"
  prompt += "Each BULLET POINT must be be less than 15 WORDS. \n"
  prompt += "Output the FIVE KEY FINDINGS as BULLET POINTS with UNDERLINE OR BOLDEN KEY PHRASES.\n"
  prompt += "Propose ONE CREATIVE ACTIONABLE IDEA for FUTURE extension of the RESEARCH\n. You MUST output the CREATIVE IDEA with a BULB OR IDEA OR THINKING emoji.\n"
  prompt += "Output ONE CREATIVE IDEA for FUTURE extension with a RANDOM emoji\n"
  prompt += "Choose an UNRELATED or ORTHOGONAL field where the FINDINGS of the article can be applied.\n"
  prompt += "In a new line, OUTPUT ONE CRAZY IDEA  in 20 WORDS how the KEY FINDINGS of RESEARCH article can be applied in an ORTHOGONAL or UNRELATED FIELD with a CRAZY IDEA emoji \n"
  prompt +=  message + "\n"

  mock_prompt = ""
  if system_prompt == "":
    mock_prompt += f"System: {system_prompt}\n"
  for user_prompt, bot_response in history:
    mock_prompt += f"User: {user_prompt}\n"
    mock_prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: "
  mock_prompt += f"""User: {message}
Falcon:"""
  return prompt



def format_prompt(message, history, system_prompt):

  prompt = ""
  prompt += "System: You are scholarly RESEARCH ASSISTANT who can read the ARXIV scholarly article.\n"
  prompt += "READ THE TITLE and ABSTRACT of the article below\n"
  prompt += "After understanding the ABSTRACT, SUGGEST 4 IMPORTANT FINDINGS or ORIGINAL CONTRIBUTIONS of OBSERVATIONs that summarizes the work.\n"
  prompt += "Each BULLET POINT must be be less than 15 WORDS. \n"
  prompt += "Output the FOUR KEY FINDINGS as BULLET POINTS with UNDERLINE OR BOLDEN KEY PHRASES.\n"
  prompt += "Propose ONE CREATIVE ACTIONABLE IDEA for FUTURE extension of the RESEARCH\n. You MUST output the CREATIVE IDEA with a BULB OR IDEA OR THINKING emoji.\n"
  prompt += "Output ONE CREATIVE IDEA for FUTURE extension with a RANDOM emoji\n"
  prompt += "Choose an UNRELATED or ORTHOGONAL field where the FINDINGS of the article can be applied.\n"
  prompt += "In a new line, OUTPUT ONE CRAZY IDEA  in 20 WORDS how the KEY FINDINGS of RESEARCH article can be applied in an ORTHOGONAL or UNRELATED FIELD with a CRAZY IDEA emoji \n"
  prompt += "User:" + message + "\n"
  mock_prompt = ""
  if system_prompt == "":
    mock_prompt += f"System: {system_prompt}\n"
  for user_prompt, bot_response in history:
    mock_prompt += f"User: {user_prompt}\n"
    mock_prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: "
  mock_prompt += f"""User: {message}
Falcon:"""
  return prompt

seed = 42

def generate(
    prompt, history, system_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    global seed
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        stop_sequences=STOP_SEQUENCES,
        do_sample=True,
        seed=seed,
    )
    seed = seed + 1

    title = "INPUT ARXI ID"
    abstract = ""
    if prompt in id_dict:
        title = id_dict[prompt]['title']
        abstract = id_dict[prompt]['abstract']
        prompt = f"TITLE: {title} ABSTRACT: {abstract}\n"
        output = f"<b>Title: </b> {title} \n <br>"
        formatted_prompt = format_prompt(prompt, history, system_prompt)
    else:
        keyword = prompt
        counter= 0
        for d in id_dict:
            title = id_dict[d]['title']
            abstract = id_dict[d]['abstract']
            if keyword in title or keyword in abstract:
               counter+=1## its a hit
               prompt += "ARTICLE " + str(counter) + "\n"
               prompt += f"TITLE: {title} ABSTRACT: {abstract}\n"
               if counter >= 4:
                   break

        prompt += "Keyword: " + keyword + "\n"
        formatted_prompt = format_prompt_summarize(prompt, history, system_prompt, keyword)
        output = "Articles related to the keyword " + keyword + "\n"
    

    
    

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    #output = ""

    for response in stream:
        output += response.token.text

        for stop_str in STOP_SEQUENCES:
            if output.endswith(stop_str):
                output = output[:-len(stop_str)]
                output = output.rstrip()
                yield output
        yield output
    return output


additional_inputs=[
    gr.Textbox("", label="Optional system prompt"),
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=8192,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=0.4):
            gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False)
        with gr.Column():
            gr.Markdown(
                """
                # 
                ** The idea is inspired by CREATIVE WHACK PACK https://apps.apple.com/us/app/creative-whack-pack/id307306326
                
                ** ##Researchers need INSPIRATION to come up with CREATIVE IDEAS.
                ** ###We use Falcon 180B to 
                    <br> - generate a <b>SUMMARY</b> of the arxiv articles (only August articles are supported)
                    <br> - generate a <b>CREATIVE IDEA </b> for future extension
                    <br> - generate a </b>CRAZY IDEA</b> for application in an orthogonal field.

                 This should hopefully CONNECT unrelated fields and inspire researchers to come up with CREATIVE IDEAS.
                ## Please input ARXIV ID or a query, see examples below (limited to 15K articles from August 2023)
                ➡️️ **Intended Use**: this demo is intended to showcase how LLMs can be used to generate creative ideas for future extension and application in orthogonal field.
                
                ⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.
                """
            )

    gr.ChatInterface(
        generate, 
        examples=EXAMPLES,
        additional_inputs=additional_inputs,
    ) 

demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)