app.py

import gradio as gr
import logging
import random
import os

from datasets import load_dataset
from huggingface_hub import login

try:
    login()
except:
    pass

auth_token = os.environ.get('HF_TOKEN', None)
if not auth_token:
    raise ValueError("could not authenticate the user.")

iiw_400 = load_dataset('google/imageinwords', token=auth_token, trust_remote_code=True, name="IIW-400")
docci_test = load_dataset('google/imageinwords', token=auth_token, trust_remote_code=True, name="DOCCI_Test")
locnar_eval = load_dataset('google/imageinwords', token=auth_token, trust_remote_code=True, name="LocNar_Eval")
cm_3600 = load_dataset('google/imageinwords', token=auth_token, trust_remote_code=True, name="CM_3600")

_SELECTOR_TO_DATASET = {
    "IIW-400": iiw_400,
    "DOCCI_Test": docci_test,
    "LocNar_Eval": locnar_eval,
    "CM_3600": cm_3600
}


def display_iiw_data_with_slider_change(dataset_type, index):
    dataset_split, image_key, image_url_key = "test", "image/key", "image/url"
    if dataset_type == "LocNar_Eval":
        dataset_split = "validation"
    if dataset_type == "DOCCI_Test":
        image_url_key = "image/thumbnail_url"
        image_key = "image"

    logging.warning(f"SELECTION: {dataset_type} : {dataset_split}: {index}")
    data = _SELECTOR_TO_DATASET[dataset_type][dataset_split][index]
    image_html = f'<img src="{data[image_url_key]}" style="width:100%; max-width:800px; height:auto;">'
    image_key_html = f"<p style='font-size: 10px'>Image Key: {data[image_key]}</p>"

    iiw_text, iiw_p5b_text, ratings = "", "", ""
    if "IIW" in data:
        iiw_text = f"<h2>IIW Human-Authored Descriptions</h2><p style='font-size: 16px'>{data['IIW']}</p>"

    if "IIW-P5B" in data:
        iiw_p5b_text = f"<h2>IIW PaLI-5B Generated Descriptions</h2><p style='font-size: 16px'>{data['IIW-P5B']}</p>"
    
    if 'iiw-human-sxs-iiw-p5b' in data and data['iiw-human-sxs-iiw-p5b'] is not None:
        ratings = "<h2>Ratings</h2>"
        for key, value in data['iiw-human-sxs-iiw-p5b'].items():
            key = key.split("metrics/")[-1]
            emoji = ""
            if key == "Comprehensiveness":
                emoji = "📚"  # Book
            elif key == "Specificity":
                emoji = "🎯"  # Bullseye
            elif key == "Hallucination":
                emoji = "👻"  # Ghost
            elif key == "First few line(s) as tldr":
                emoji = "🔍"  # Magnifying Glass Tilted Left
            elif key == "Human Like":
                emoji = "👤"  # Bust in Silhouette
            ratings += f"<p style='font-size: 16px'>{emoji} <strong>{key}</strong>: {value}</p>"
    return image_key_html, image_html, iiw_text, iiw_p5b_text, ratings


def display_iiw_data_with_dataset_change(dataset_type, index):
    slider = gr.Slider(minimum=0, maximum=max_index(dataset_type)-1, label="Dataset Size", value=0)
    image_key_html, image_html, iiw_text, iiw_p5b_text, ratings = display_iiw_data_with_slider_change(dataset_type, index=0)
    return slider, image_key_html, image_html, iiw_text, iiw_p5b_text, ratings


def max_index(dataset_type):
    dataset_split = "test"
    if dataset_type == "LocNar_Eval":
        dataset_split = "validation"

    logging.warning(f"SELECTION: {dataset_type} : {dataset_split}")
    dataset_instance =_SELECTOR_TO_DATASET[dataset_type][dataset_split]
    return len(dataset_instance)


with gr.Blocks() as demo:
    gr.Markdown("# ImageInWords: Unlocking Hyper-Detailed Image Descriptions")
    gr.Markdown("Slide across the slider to see various examples across the different IIW datasets.")

    with gr.Row():
        dataset_selector = gr.Radio(["IIW-400", "DOCCI_Test", "LocNar_Eval", "CM_3600"], value="IIW-400", label="IIW Datasets")
        slider, image_key_html, image_html, iiw_text, iiw_p5b_text, ratings = display_iiw_data_with_dataset_change(dataset_selector.value, index=0)

    with gr.Row():
        with gr.Column():
            image_output = gr.HTML(image_html)
        
        with gr.Column():
            image_key_output = gr.HTML(image_key_html)
            if iiw_text:
                iiw_text_output = gr.HTML(iiw_text)
            if iiw_p5b_text:
                iiw_p5b_text_output = gr.HTML(iiw_p5b_text)
            if ratings:
                ratings_output = gr.HTML(ratings)

    slider.change(display_iiw_data_with_slider_change, inputs=[dataset_selector, slider], outputs=[image_key_output, image_output, iiw_text_output, iiw_p5b_text_output, ratings_output])
    dataset_selector.change(display_iiw_data_with_dataset_change, inputs=[dataset_selector, slider], outputs=[slider, image_key_output, image_output, iiw_text_output, iiw_p5b_text_output, ratings_output])

demo.launch(debug=True)