app.py

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

import gradio as gr
import requests
from transformers import pipeline
from huggingface_hub import Repository
from dotenv import load_dotenv
from pathlib import Path
import json

# 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")
HF_TOKEN = os.getenv("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
)

# Now let's run the app!
pipe = pipeline("sentiment-analysis")

demo = gr.Blocks()

with demo:
    total_cnt = 2 # How many examples per HIT
    dummy = gr.Textbox(visible=False)  # dummy for passing assignmentId

    # We keep track of state as a JSON
    state_dict = {"assignmentId": "", "cnt": 0, "fooled": 0, "data": [], "metadata": {}}
    state = gr.JSON(state_dict, visible=False)

    gr.Markdown("# DADC in Gradio example")
    gr.Markdown("Try to fool the model and find an example where it predicts the wrong label!")

    state_display = gr.Markdown(f"State: 0/{total_cnt} (0 fooled)")

    # Generate model prediction
    # Default model: distilbert-base-uncased-finetuned-sst-2-english
    def _predict(txt, tgt, state, dummy):
        pred = pipe(txt)[0]
        other_label = 'negative' if pred['label'].lower() == "positive" else "positive"
        pred_confidences = {pred['label'].lower(): pred['score'], other_label: 1 - pred['score']}

        pred["label"] = pred["label"].title()
        ret = f"Target: **{tgt}**. Model prediction: **{pred['label']}**\n\n"
        if pred["label"] != tgt:
            state["fooled"] += 1
            ret += " You fooled the model! Well done!"
        else:
            ret += " You did not fool the model! Too bad, try again!"
        state["data"].append(ret)
        state["cnt"] += 1

        done = state["cnt"] == total_cnt
        toggle_final_submit = gr.update(visible=done)
        toggle_example_submit = gr.update(visible=not done)
        new_state_md = f"State: {state['cnt']}/{total_cnt} ({state['fooled']} fooled)"

        query = parse_qs(dummy[1:])
        if "assignmentId" in query:
            # 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]

        return pred_confidences, ret, state, toggle_example_submit, toggle_final_submit, new_state_md, dummy

    # Input fields
    text_input = gr.Textbox(placeholder="Enter model-fooling statement", show_label=False)
    labels = ["Positive", "Negative"]
    random.shuffle(labels)
    label_input = gr.Radio(choices=labels, label="Target (correct) label")
    label_output = gr.Label()
    text_output = gr.Markdown()
    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")

    # Store the HIT data into a Hugging Face dataset.
    # The HIT is also stored and logged on mturk when post_hit_js is run below.
    # This _store_in_huggingface_dataset function just demonstrates how easy it is
    # to automatically create a Hugging Face dataset from mturk.
    def _store_in_huggingface_dataset(state):
        with open(DATA_FILE, "a") as jsonlfile:
            jsonlfile.write(json.dumps(state) + "\n")
        repo.push_to_hub()

        if state["assignmentId"] == "":
            # If assignmentId is not set, then someone is using this app on
            # huggingface.co, and we can reset the app to it's initial state
            # after they submit their fake "HIT".
            state = {"assignmentId": "", "cnt": 0, "fooled": 0, "data": [], "metadata": {}}
            toggle_final_submit = gr.update(visible=False)
            toggle_example_submit = gr.update(visible=True)
            new_state_md = gr.Markdown(f"State: 0/{total_cnt} (0 fooled)")

        return state, toggle_final_submit, toggle_example_submit, new_state_md

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

    submit_ex_button.click(
        _predict,
        inputs=[text_input, label_input, state, dummy],
        outputs=[label_output, text_output, state, example_submit, final_submit, state_display, dummy],
        _js=get_window_location_search_js,
    )

    post_hit_js = """
        function(state, toggle_final_submit, toggle_example_submit, new_state_md) {
            if (state["assignmentId"] !== "") {
                //It seems that someone is using this app on mturk, so submit the 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(
        _store_in_huggingface_dataset,
        inputs=[state],
        outputs=[state, final_submit, example_submit, state_display],
        _js=post_hit_js,
    )

demo.launch()