app.py

from datasets import load_dataset
from collections import Counter, defaultdict
from random import sample, shuffle
import datasets
from pandas import DataFrame
from huggingface_hub import list_datasets
import os
import gradio as gr

import secrets


parti_prompt_results = []
ORG = "diffusers-parti-prompts"
SUBMISSIONS = {
    "sd-v1-5": load_dataset(os.path.join(ORG, "sd-v1-5"))["train"],
    "sd-v2-1": load_dataset(os.path.join(ORG, "sd-v2.1"))["train"],
    "if-v1-0": load_dataset(os.path.join(ORG, "karlo-v1"))["train"],
    "karlo": load_dataset(os.path.join(ORG, "if-v-1.0"))["train"],
    # "Kadinsky":
}

LINKS = {
    "sd-v1-5": "https://huggingface.co/runwayml/stable-diffusion-v1-5",
    "sd-v2-1": "https://huggingface.co/stabilityai/stable-diffusion-2-1",
    "if-v1-0": "https://huggingface.co/DeepFloyd/IF-I-XL-v1.0",
    "karlo": "https://huggingface.co/kakaobrain/karlo-v1-alpha",
}
NUM_QUESTIONS = 10
MODEL_KEYS = "-".join(SUBMISSIONS.keys())
SUBMISSION_ORG = f"results-{MODEL_KEYS}"
PROMPT_FORMAT = "Pick the picture that best matches the prompt: **{}**"

submission_names = list(SUBMISSIONS.keys())
num_images = len(SUBMISSIONS[submission_names[0]])


def load_submissions():
    all_datasets = list_datasets(author=SUBMISSION_ORG)
    relevant_ids = [d.id for d in all_datasets]
    
    submitted_ids = []
    for _id in relevant_ids:
        ds = load_dataset(_id)["train"]
        submitted_ids += ds["id"]
    
    submitted_ids = Counter(submitted_ids)
    return submitted_ids


SUBMITTED_IDS = load_submissions()


def generate_random_hash(length=8):
    """
    Generates a random hash of specified length.
    
    Args:
        length (int): The length of the hash to generate.
        
    Returns:
        str: A random hash of specified length.
    """
    if length % 2 != 0:
        raise ValueError("Length should be an even number.")
    
    num_bytes = length // 2
    random_bytes = secrets.token_bytes(num_bytes)
    random_hash = secrets.token_hex(num_bytes)
    
    return random_hash
    

def refresh(row_number, dataframe):
    if row_number == NUM_QUESTIONS:
        submitted_ids = load_submissions()
        return start(submitted_ids)
    else:
        return dataframe

def start():
    ids = {id: 0 for id in range(num_images)}
    ids = {**ids, **SUBMITTED_IDS}

    # sort by count
    ids = sorted(ids.items(), key=lambda x: x[1])
    freq_ids = defaultdict(list)
    for k, v in ids:
        freq_ids[v].append(k)

    # shuffle in-between categories
    for k, v_list in freq_ids.items():
        shuffle(v_list)
        freq_ids[v] = v_list

    shuffled_ids = sum(list(freq_ids.values()), [])

    # get lowest count ids
    id_candidates = shuffled_ids[: (10 * NUM_QUESTIONS)]

    # get random `NUM_QUESTIONS` ids to check
    image_ids = sample(id_candidates, k=NUM_QUESTIONS)
    images = {}

    for i in range(NUM_QUESTIONS):
        order = list(range(len(SUBMISSIONS)))
        shuffle(order)

        id = image_ids[i]
        row = SUBMISSIONS[submission_names[0]][id]
        images[i] = {
            "prompt": row["Prompt"],
            "result": "",
            "id": id,
            "Challenge": row["Challenge"],
            "Category": row["Category"],
            "Note": row["Note"],
        }
        for n, m in enumerate(order):
            images[i][f"choice_{n}"] = m

    images_frame = DataFrame.from_dict(images, orient="index")
    return images_frame


def process(dataframe, row_number=0):
    if row_number == NUM_QUESTIONS:
        return None, "", ""

    image_id = dataframe.iloc[row_number]["id"]
    choices = [
        submission_names[dataframe.iloc[row_number][f"choice_{i}"]]
        for i in range(len(SUBMISSIONS))
    ]
    images = (SUBMISSIONS[c][int(image_id)]["images"] for c in choices)

    prompt = SUBMISSIONS[choices[0]][int(image_id)]["Prompt"]
    prompt = PROMPT_FORMAT.format(prompt)
    counter = f"{row_number + 1}/{NUM_QUESTIONS}"

    return *images, prompt, counter


def write_result(user_choice, row_number, dataframe):
    if row_number == NUM_QUESTIONS:
        return row_number, dataframe

    user_choice = int(user_choice)
    chosen_model = submission_names[dataframe.iloc[row_number][f"choice_{user_choice}"]]

    dataframe.loc[row_number, "result"] = chosen_model
    return row_number + 1, dataframe


def get_index(evt: gr.SelectData) -> int:
    return evt.index


def change_view(row_number, dataframe):
    if row_number == NUM_QUESTIONS:

        favorite_model = dataframe["result"].value_counts().idxmax()
        dataset = datasets.Dataset.from_pandas(dataframe)
        dataset = dataset.remove_columns(set(dataset.column_names) - set(["id", "result"]))
        hash = generate_random_hash()
        repo_id = os.path.join(SUBMISSION_ORG, hash)

        dataset.push_to_hub(repo_id, token=os.getenv("HF_TOKEN"))
        return {
            intro_view: gr.update(visible=True),
            result_view: gr.update(visible=True),
            gallery_view: gr.update(visible=False),
            result: f"You are of type: [**{favorite_model}**]({LINKS[favorite_model]}) 🔥",
        }
    else:
        return {
            intro_view: gr.update(visible=False),
            result_view: gr.update(visible=False),
            gallery_view: gr.update(visible=True),
            result: "",
        }


TITLE = "# Community Parti Prompts - Who is your open-source genAI model?"
DESCRIPTION = """
*This is an interactive game in which you click through pre-generated images from SD-v1-5, SD-v2.1, Karlo, and IF 
using [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) prompts.* \n
*You choices will go into the public community [genAI leaderboard](TODO).*
"""
EXPLANATION = """\n\n
## How it works 📖 \n\n

1. Click on 'Start'
2. A prompt and 4 different images are displayed
3. Select your favorite image
4. Click on 'Confirm'
5. After 10 rounds your favorite diffusion model is displayed
"""

GALLERY_COLUMN_NUM = len(SUBMISSIONS)

with gr.Blocks() as demo:
    gr.Markdown(TITLE)
    gr.Markdown(DESCRIPTION)

    with gr.Column(visible=True) as intro_view:
        gr.Markdown(EXPLANATION)
        start_button = gr.Button("Start").style(full_width=False)

    headers = ["prompt", "result", "id", "Challenge", "Category", "Note"] + [
        f"choice_{i}" for i in range(len(SUBMISSIONS))
    ]
    datatype = ["str", "str", "number", "str", "str", "str"] + len(SUBMISSIONS) * [
        "number"
    ]

    with gr.Column(visible=False):
        row_number = gr.Number(
            label="Current row selection index",
            value=0,
            precision=0,
            interactive=False,
        )

    # Create Data Frame
    with gr.Column(visible=False) as result_view:
        result = gr.Markdown("")
        dataframe = gr.Dataframe(
            headers=headers,
            datatype=datatype,
            row_count=NUM_QUESTIONS,
            col_count=(6 + len(SUBMISSIONS), "fixed"),
            interactive=False,
        )
        gr.Markdown("Click on start to play again!")

    with gr.Column(visible=False) as gallery_view:
        counter = gr.Markdown(f" ### 1/{NUM_QUESTIONS}")
        prompt = gr.Markdown(PROMPT_FORMAT.format(""))
        gallery = gr.Gallery(
            label="All images", show_label=False, elem_id="gallery"
        ).style(columns=GALLERY_COLUMN_NUM, object_fit="contain", preview=True)
        with gr.Blocks():
            with gr.Row() as images:
                image_1 = gr.Image(interactive=False)
                image_2 = gr.Image(interactive=False)
                image_3 = gr.Image(interactive=False)
                image_4 = gr.Image(interactive=False)
            with gr.Row():
                image1_button = gr.Button("1").style(full_width=False)
                image2_button = gr.Button("2").style(full_width=False)
                image3_button = gr.Button("3").style(full_width=False)
                image4_button = gr.Button("4").style(full_width=False)
                
        next_button = gr.Button("Confirm").style(full_width=True)

    with gr.Column(visible=False):
        selected_image = gr.Number(label="Selected index", value=-1, precision=0)

    start_button.click(
        fn=start,
        inputs=[],
        outputs=dataframe,
        show_progress=True
    ).then(
        fn=lambda x: 0 if x == NUM_QUESTIONS else x,
        inputs=[row_number],
        outputs=[row_number],
    ).then(
        fn=change_view,
        inputs=[row_number, dataframe],
        outputs=[intro_view, result_view, gallery_view, result]
    ).then(
        fn=process, inputs=[dataframe], outputs=[image_1, image_2, image_3, image_4, prompt, counter]
    )

    gallery.select(
        fn=get_index,
        outputs=selected_image,
        queue=False,
    )

    next_button.click(
        fn=write_result,
        inputs=[selected_image, row_number, dataframe],
        outputs=[row_number, dataframe],
    ).then(
        fn=change_view,
        inputs=[row_number, dataframe],
        outputs=[intro_view, result_view, gallery_view, result]
    ).then(
        fn=process,
        inputs=[dataframe, row_number],
        outputs=[image_1, image_2, image_3, image_4, prompt, counter]
    ).then(
        fn=lambda x: 0 if x == NUM_QUESTIONS else x,
        inputs=[row_number],
        outputs=[row_number],
    ).then(
        fn=refresh,
        inputs=[row_number, dataframe],
        outputs=[dataframe],
    )

demo.launch()