app.py

# Basic example for doing model-in-the-loop dynamic adversarial data collection
# using Gradio Blocks.
import json
import os
import threading
import time
import uuid
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import List
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


def generate_respone(chatbot: ConversationChain, input: str) -> str:
    """Generates a response for a `langchain` chatbot."""
    return chatbot.predict(input=input)

def generate_responses(chatbots: List[ConversationChain], inputs: List[str]) -> List[str]:
    """Generates parallel responses for a list of `langchain` chatbots."""
    results = []
    with ThreadPoolExecutor(max_workers=100) as executor:
        for result in executor.map(generate_respone, chatbots, inputs):
            results.append(result)
    return results


# 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")

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")

# TODO: update this list with better, instruction-trained models
MODEL_IDS = ["google/flan-t5-xl", "bigscience/T0_3B", "EleutherAI/gpt-j-6B"]
chatbots = []

for model_id in MODEL_IDS:
    chatbots.append(ConversationChain(
    llm=HuggingFaceHub(
        repo_id=model_id,
        model_kwargs={"temperature": 1},
        huggingfacehub_api_token=HF_TOKEN,
    ),
    prompt=prompt,
    verbose=False,
    memory=ConversationBufferMemory(ai_prefix="Assistant"),
))


model_id2model = {chatbot.llm.repo_id: chatbot for chatbot in chatbots}

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": [],
        }
    for idx in range(len(chatbots)):
        state_dict[f"response_{idx+1}"] = ""
    state = gr.JSON(state_dict, visible=False)

    gr.Markdown("# Talk to the assistant")

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

    # Generate model prediction
    def _predict(txt, state):
        start = time.time()
        responses = generate_responses(chatbots, [txt] * len(chatbots))
        print(f"Time taken to generate {len(chatbots)} responses : {time.time() - start:.2f} seconds")

        response2model_id = {}
        for chatbot, response in zip(chatbots, responses):
            response2model_id[response] = chatbot.llm.repo_id

        state["cnt"] += 1

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

        metadata = {"cnt": state["cnt"], "text": txt}
        for idx, response in enumerate(responses):
            metadata[f"response_{idx + 1}"] = response

        metadata["response2model_id"] =  response2model_id

        state["data"].append(metadata)
        state["past_user_inputs"].append(txt)

        past_conversation_string = "<br />".join(["<br />".join(["Human 😃: " + user_input, "Assistant 🤖: " + 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=responses, interactive=True, value=responses[0]), 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_id"][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)

        if done:
            # Wipe the memory completely because we will be starting a new hit soon.
            for chatbot in chatbots:
                chatbot.memory = ConversationBufferMemory(ai_prefix="Assistant")
        else:
            # Sync all of the model's memories with the conversation path that
            # was actually taken.
            for chatbot in chatbots:
                chatbot.memory = model_id2model[state["data"][-1]["response2model_id"][selected_response]].memory

        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, dummy

    # 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 most helpful and honest 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(select_response, state, dummy) {
            return [select_response, 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, dummy],
        _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],
    )

    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()