# Basic example for doing model-in-the-loop dynamic adversarial data collection
# using Gradio Blocks.
import json
import os
import threading
import uuid
from pathlib import Path
from urllib.parse import parse_qs

import gradio as gr
from dotenv import load_dotenv
from huggingface_hub import Repository
from langchain import ConversationChain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.llms import HuggingFaceHub
from langchain.prompts import load_prompt

from utils import force_git_push

# These variables are for storing the mturk HITs in a Hugging Face dataset.
if Path(".env").is_file():
    load_dotenv(".env")
DATASET_REPO_URL = os.getenv("DATASET_REPO_URL")
FORCE_PUSH = os.getenv("FORCE_PUSH")
HF_TOKEN = os.getenv("HF_TOKEN")
PROMPT_TEMPLATES = Path("prompt_templates")
# Set env variable for langchain to communicate with Hugging Face Hub
os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN

DATA_FILENAME = "data.jsonl"
DATA_FILE = os.path.join("data", DATA_FILENAME)
repo = Repository(
    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
)

TOTAL_CNT = 3 # How many user inputs per HIT

# This function pushes the HIT data written in data.jsonl to our Hugging Face
# dataset every minute. Adjust the frequency to suit your needs.
PUSH_FREQUENCY = 60
def asynchronous_push(f_stop):
    if repo.is_repo_clean():
        print("Repo currently clean. Ignoring push_to_hub")
    else:
        repo.git_add(auto_lfs_track=True)
        repo.git_commit("Auto commit by space")
        if FORCE_PUSH == "yes":
            force_git_push(repo)
        else:
            repo.git_push()
    if not f_stop.is_set():
        # call again in 60 seconds
        threading.Timer(PUSH_FREQUENCY, asynchronous_push, [f_stop]).start()

f_stop = threading.Event()
asynchronous_push(f_stop)

# Now let's run the app!
prompt = load_prompt(PROMPT_TEMPLATES / "openai_chatgpt.json")

chatbot_1 = ConversationChain(
    llm=HuggingFaceHub(
        repo_id="google/flan-t5-xl",
        model_kwargs={"temperature": 1}
    ),
    prompt=prompt,
    verbose=False,
    memory=ConversationBufferMemory(ai_prefix="Assistant"),
)

chatbot_2 = ConversationChain(
    llm=HuggingFaceHub(
        repo_id="bigscience/bloom",
        model_kwargs={"temperature": 0.7}
    ),
    prompt=prompt,
    verbose=False,
    memory=ConversationBufferMemory(ai_prefix="Assistant"),
)

chatbot_3 = ConversationChain(
    llm=HuggingFaceHub(
        repo_id="bigscience/T0_3B",
        model_kwargs={"temperature": 1}
    ),
    prompt=prompt,
    verbose=False,
    memory=ConversationBufferMemory(ai_prefix="Assistant"),
)

chatbot_4 = ConversationChain(
    llm=HuggingFaceHub(
        repo_id="EleutherAI/gpt-j-6B",
        model_kwargs={"temperature": 1}
    ),
    prompt=prompt,
    verbose=False,
    memory=ConversationBufferMemory(ai_prefix="Assistant"),
)

demo = gr.Blocks()

with demo:
    dummy = gr.Textbox(visible=False)  # dummy for passing assignmentId

    # We keep track of state as a JSON
    state_dict = {
        "conversation_id": str(uuid.uuid4()),
        "assignmentId": "",
        "cnt": 0, "data": [],
        "past_user_inputs": [],
        "generated_responses": [],
        "response_1": "",
        "response_2": "",
        "response_3": "",
        "response_4": "",
        }
    state = gr.JSON(state_dict, visible=False)

    gr.Markdown("# RLHF Interface")
    gr.Markdown("Choose the best model output")

    state_display = gr.Markdown(f"Your messages: 0/{TOTAL_CNT}")

    # Generate model prediction
    def _predict(txt, state):
        # TODO: parallelize this!
        response_1 = chatbot_1.predict(input=txt)
        response_2 = chatbot_2.predict(input=txt)
        response_3 = chatbot_3.predict(input=txt)
        response_4 = chatbot_4.predict(input=txt)

        response2model = {}
        response2model[response_1] = chatbot_1.llm.repo_id
        response2model[response_2] = chatbot_2.llm.repo_id
        response2model[response_3] = chatbot_3.llm.repo_id
        response2model[response_4] = chatbot_4.llm.repo_id

        state["cnt"] += 1

        new_state_md = f"Inputs remaining in HIT: {state['cnt']}/{TOTAL_CNT}"

        state["data"].append({"cnt": state["cnt"], "text": txt, "response_1": response_1,  "response_2": response_2, "response_3": response_3, "response_4": response_4,"response2model": response2model})
        state["past_user_inputs"].append(txt)

        past_conversation_string = "<br />".join(["<br />".join(["😃: " + user_input, "🤖: " + model_response]) for user_input, model_response in zip(state["past_user_inputs"], state["generated_responses"] + [""])])
        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True, choices=[response_1, response_2, response_3, response_4], interactive=True, value=response_1), gr.update(value=past_conversation_string), state, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), new_state_md, dummy

    def _select_response(selected_response, state, dummy):
        done = state["cnt"] == TOTAL_CNT
        state["generated_responses"].append(selected_response)
        state["data"][-1]["selected_response"] = selected_response
        state["data"][-1]["selected_model"] = state["data"][-1]["response2model"][selected_response]
        if state["cnt"] == TOTAL_CNT:
            # Write the HIT data to our local dataset because the worker has
            # submitted everything now.
            with open(DATA_FILE, "a") as jsonlfile:
                json_data_with_assignment_id =\
                    [json.dumps(dict({"assignmentId": state["assignmentId"], "conversation_id": state["conversation_id"]}, **datum)) for datum in state["data"]]
                jsonlfile.write("\n".join(json_data_with_assignment_id) + "\n")
        toggle_example_submit = gr.update(visible=not done)
        past_conversation_string = "<br />".join(["<br />".join(["😃: " + user_input, "🤖: " + model_response]) for user_input, model_response in zip(state["past_user_inputs"], state["generated_responses"])])
        query = parse_qs(dummy[1:])
        if "assignmentId" in query and query["assignmentId"][0] != "ASSIGNMENT_ID_NOT_AVAILABLE":
            # It seems that someone is using this app on mturk. We need to
            # store the assignmentId in the state before submit_hit_button
            # is clicked. We can do this here in _predict. We need to save the
            # assignmentId so that the turker can get credit for their HIT.
            state["assignmentId"] = query["assignmentId"][0]
            toggle_final_submit = gr.update(visible=done)
            toggle_final_submit_preview = gr.update(visible=False)
        else:
            toggle_final_submit_preview = gr.update(visible=done)
            toggle_final_submit = gr.update(visible=False)
        text_input = gr.update(visible=False) if done else gr.update(visible=True)
        return gr.update(visible=False), gr.update(visible=True), text_input, gr.update(visible=False), state, gr.update(value=past_conversation_string), toggle_example_submit, toggle_final_submit, toggle_final_submit_preview,

    # Input fields
    past_conversation = gr.Markdown()
    text_input = gr.Textbox(placeholder="Enter a statement", show_label=False)
    select_response = gr.Radio(choices=[None, None], visible=False, label="Choose the best response")
    select_response_button = gr.Button("Select Response", visible=False)
    with gr.Column() as example_submit:
        submit_ex_button = gr.Button("Submit")
    with gr.Column(visible=False) as final_submit:
        submit_hit_button = gr.Button("Submit HIT")
    with gr.Column(visible=False) as final_submit_preview:
        submit_hit_button_preview = gr.Button("Submit Work (preview mode; no mturk HIT credit, but your examples will still be stored)")

    # Button event handlers
    get_window_location_search_js = """
        function(text_input, label_input, state, dummy) {
            return [text_input, label_input, state, window.location.search];
        }
        """

    select_response_button.click(
        _select_response,
        inputs=[select_response, state, dummy],
        outputs=[select_response, example_submit, text_input, select_response_button, state, past_conversation, example_submit, final_submit, final_submit_preview],
        _js=get_window_location_search_js,
    )

    submit_ex_button.click(
        _predict,
        inputs=[text_input, state],
        outputs=[text_input, select_response_button, select_response, past_conversation, state, example_submit, final_submit, final_submit_preview, state_display, dummy],
        _js=get_window_location_search_js,
    )

    post_hit_js = """
        function(state) {
            // If there is an assignmentId, then the submitter is on mturk
            // and has accepted the HIT. So, we need to submit their HIT.
            const form = document.createElement('form');
            form.action = 'https://workersandbox.mturk.com/mturk/externalSubmit';
            form.method = 'post';
            for (const key in state) {
                const hiddenField = document.createElement('input');
                hiddenField.type = 'hidden';
                hiddenField.name = key;
                hiddenField.value = state[key];
                form.appendChild(hiddenField);
            };
            document.body.appendChild(form);
            form.submit();
            return state;
        }
        """

    submit_hit_button.click(
        lambda state: state,
        inputs=[state],
        outputs=[state],
        _js=post_hit_js,
    )

    refresh_app_js = """
        function(state) {
            // The following line here loads the app again so the user can
            // enter in another preview-mode "HIT".
            window.location.href = window.location.href;
            return state;
        }
        """

    submit_hit_button_preview.click(
        lambda state: state,
        inputs=[state],
        outputs=[state],
        _js=refresh_app_js,
    )

demo.launch()