import gradio as gr
import requests
import random
import time
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from game1 import read1, func1, interpre1, read1_written, func1_written, change_lang
from game2 import func2
from game3 import func3
def ret_en():
return 'en'
def ret_nl():
return 'nl'
def reset_scores():
data = pd.DataFrame(
{
"Role": ["AI 🤖", "HUMAN 👨👩"],
"Scores": [0, 0],
}
)
tot_scores = ''' ###
Machine ''' + str(int(0)) + ''' VS ''' + str(int(0)) + ''' Human
'''
# scroe_human = ''' # Human: ''' + str(int(0))
# scroe_robot = ''' # Robot: ''' + str(int(0))
# tooltip=["Role", "Scores"],
return 0, 0, tot_scores
def reset_modules():
res_empty = {"original": "", "interpretation": []}
return res_empty, 0, 0, [], ""
with gr.Blocks(theme=gr.themes.Default(text_size=gr.themes.sizes.text_md)) as demo:
pre_load_1 = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
pre_load_2 = pipeline("text-classification", model='DTAI-KULeuven/robbert-v2-dutch-sentiment')
pre_load_3 = pipeline("text-classification", model='distilbert-base-uncased-finetuned-sst-2-english')
with gr.Row():
num1 = gr.Number(value=0, container=False, show_label=False, visible=False)
num2 = gr.Number(value=0, container=False, show_label=False, visible=False)
placeholder = gr.Markdown(
''' ## Welcome to the Language Model Explanation Challenge!
Language Models (LMs) are powerful AI tools to understand and generate human language.
However, they sometimes make mistakes... and it's hard to know why!
Are *humans* or *machines* better at understanding language?
→ Play a game against AI to find out!
Does AI think like you or not at all?
→ Check out the color highlighting to see which parts of the sentence are more important for the machine.
Can you outsmart the AI?
→ Try to write a text that will trick it into the wrong decision
Choose one of the three tasks below ... and start to play!
'''
#* **Like or Dislike** provides a movie/food/book review. You (and AI) are required to guess its score.
#The one with the correct or close answer win the score.
#* **Human or Machine** provides a paragraph. You (and AI) need to judge if it is written by humans or machines.
#The one with the correct or close answer win the score.
#* **Man or Woman** allows you to write a text.
#If you could successfully trick the AI into guessing the wrong gender, you get the score.
)
with gr.Column():
# data = pd.DataFrame(
# {
# "Role": ["AI 🤖", "HUMAN 👨👩"],
# "Scores": [0, 0],
# }
# )
# plot = gr.BarPlot(
# data,
# x="Role",
# y="Scores",
# color="Role",
# vertical=False,
# y_lim=[0,10],
# color_legend_position='none',
# height=250,
# width=500,
# show_label=False,
# container=False,
# )
# tooltip=["Role", "Scores"],
# button_reset = gr.Button("Reset Scores")
gr.Markdown(
''' ## Today's Scores
'''
)
tot_scores = gr.Markdown(
''' ### Machine ''' + str(int(0)) + ''' VS ''' + str(int(0)) + ''' Human
'''
)
# score_robot = gr.Markdown(
# ''' ## Robot: ''' + str(int(num2.value))
# )
# score_human = gr.Markdown(
# ''' ## Human: ''' + str(int(num1.value))
# )
# button_reset.click(reset_scores, outputs=[num1, num2, tot_scores, plot])
with gr.Tab("Like or Dislike"):
text_en = gr.Textbox(label="", value="en", visible=False)
text_nl = gr.Textbox(label="", value="nl", visible=False)
lang_selected = gr.Textbox(label="", value="", visible=False)
num_selected = gr.Number(value=0, container=False, show_label=False, visible=False)
with gr.Row():
with gr.Column():
with gr.Row():
sample_button_en = gr.Button("Click to get a review in English.", size='sm')
# gr.Markdown(''' or
''')
# gr.Markdown(''' or
''')
sample_button_nl = gr.Button("Click to get a review in Dutch.", size='sm')
# h1 = gr.HighlightedText(label="Review:", interactive=True, show_legend=True, combine_adjacent=False, color_map={"+": "red", "-": "green"})
input_text = gr.Textbox(label="Review:", value="HELLO! Hallo!", visible=False, container=False)
interpretation1 = gr.components.Interpretation(input_text)
# image_1_1 = gr.Image('icon_user.png', height=80, width=80, min_width=80, show_label=False, show_share_button=False, interactive=False)
slider_1_1 = gr.Slider(label="Human: Dislike ——> Like", container=True, min_width=200, height=80, show_label=True, interactive=True)
# checkbox_1 = gr.CheckboxGroup(label="Which words are the guesses based on?", interactive=True)
user_important = gr.Textbox(label="Which words are the guesses based on?")
gr.Markdown(
''' ## Like or Dislike
You're given a short review of a movie, book or restaurant.
The goal of this game is to guess how *positive* the review is, from 0 (=extremely bad) to 100 (=fantastic).
* Step 1. Get an English or Dutch review and guess the corresponding score.
* Step 2. Check the score guessed by AI. Who gets the most correct answer wins.
* Step 3. Check the word highlighting to understand how AI made its decision.
'''
)
# gr.Markdown(
# ''' ## Like or Dislike
# In this game, you will fight against AI in guessing the scores of the reviews:
# * Step 1. Get an English/Dutch review and guess the corresponding score.
# * Step 2. Check the score guessed by AI. The one with the correct/close answer wins.
# * Step 3. (See how AI made the decision.)
# Simple enough? Let's have fun!
# '''
# )
with gr.Row():
with gr.Column():
chat_button_1 = gr.Button("Click to see AI's answer.", size='sm')
slider_1_2 = gr.Slider(label="AI: Dislike ——> Like", container=True, min_width=200, height=80, show_label=True, interactive=True)
interpre_button = gr.Button("See how AI gets the answer.", size='sm')
# h2 = gr.HighlightedText(label="Review:", interactive=True, show_legend=True, combine_adjacent=False, color_map={"+": "red", "-": "green"})
placeholder_text = gr.Textbox(label="Review:", value="HELLO! Hallo!", visible=False)
interpretation2 = gr.components.Interpretation(placeholder_text)
# image_1_2 = gr.Image('icon_robot.png', height=80, width=80, min_width=80, show_label=False, show_share_button=False, interactive=False)
chatbot1 = gr.Chatbot(height=200, min_width=50, container=False) # height=300
####################################################################################################
# gr.Markdown(''' --- ''')
gr.Markdown(''' *** ''')
gr.Markdown(
''' # Now try your own reviews!
'''
)
with gr.Row():
with gr.Column():
text_written = gr.Textbox(label="Review: ", placeholder="Input your own review about a movie/restaurant/book.", visible=True)
# image_1_3 = gr.Image('icon_user.png', height=80, width=80, min_width=80, show_label=False, show_share_button=False, interactive=False)
slider_1_3 = gr.Slider(label="Human: Dislike ——> Like", container=True, min_width=200, height=80, show_label=True, interactive=True)
lang_written = gr.Radio(["English", "Dutch"], label="Language:", info="In which language is the review written?")
chat_button_2 = gr.Button("Click to see AI's answer.", size='sm')
placeholder_written_text = gr.Textbox(label="Review: ", value="HELLO! Hallo!", visible=False)
interpretation4 = gr.components.Interpretation(placeholder_written_text)
slider_1_4 = gr.Slider(label="AI: Dislike ——> Like", container=True, min_width=200, height=80, show_label=True, interactive=True)
chatbot2 = gr.Chatbot(height=350, min_width=50, container=False) # height=300
sample_button_en.click(read1, inputs=[text_en, num_selected], outputs=[interpretation1, lang_selected, num_selected])
sample_button_nl.click(read1, inputs=[text_nl, num_selected], outputs=[interpretation1, lang_selected, num_selected])
num_selected.change(reset_modules, outputs=[interpretation2, slider_1_1, slider_1_2, chatbot1, user_important])
chat_button_1.click(func1, inputs=[lang_selected, num_selected, slider_1_1, num1, num2, user_important], outputs=[slider_1_2, chatbot1, num1, num2, tot_scores])
interpre_button.click(interpre1, inputs=[lang_selected, num_selected], outputs=[interpretation2])
# sample_button_en_written.click(read1_written, inputs=[text_en], outputs=[text_written])
# sample_button_nl_written.click(read1_written, inputs=[text_nl], outputs=[text_written])
# lang_written.change(fn=change_lang, inputs=radio, outputs=lang_written_text)
chat_button_2.click(func1_written, inputs=[text_written, slider_1_3, lang_written], outputs=[interpretation4, slider_1_4, chatbot2])
with gr.Tab("Human or Machine"):
with gr.Row():
text_input_2 = gr.Textbox()
text_output_2 = gr.Label()
text_button_2 = gr.Button("Check")
with gr.Tab("Man or Woman"):
with gr.Row():
text_input_3 = gr.Textbox()
text_output_3 = gr.Label()
text_button_3 = gr.Button("Guess")
if __name__ == "__main__":
demo.launch()