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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 = ''' ### <p style="text-align: center;"> Machine   ''' + str(int(0)) + '''   VS   ''' + str(int(0)) + '''   Human </p>'''
# scroe_human = ''' # Human: ''' + str(int(0))
# scroe_robot = ''' # Robot: ''' + str(int(0))
# tooltip=["Role", "Scores"],
return 0, 0, tot_scores, gr.BarPlot.update(
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,
)
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")
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.<br />
However, they sometimes make mistakes... and it's hard to know why!<br /><br />
Are *humans* or *machines* better at understanding language?<br />
→ Play a game against AI to find out!<br /><br />
Does AI think like you or not at all?<br />
→ Check out the color highlighting to see which parts of the sentence are more important for the machine.<br /><br />
Can you outsmart the AI?<br />
→ Try to write a text that will trick it into the wrong decision<br /><br />
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():
# plot = gr.BarPlot(height=120, width=300, container=False)
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(
''' ### <p style="text-align: center;"> Machine   ''' + str(int(0)) + '''   VS   ''' + str(int(0)) + '''   Human </p>'''
)
# 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():
sample_button_en = gr.Button("Click to get a review in English.", size='sm')
gr.Markdown(''' <p style="text-align: center;"> or </p> ''')
# gr.Markdown(''' <h2 style="text-align: center;"> or </h2> ''')
sample_button_nl = gr.Button("Click to get a review in Dutch.", size='sm')
# h1 = gr.HighlightedText(label="Review/Recensie:", interactive=True, show_legend=True, combine_adjacent=False, color_map={"+": "red", "-": "green"})
input_text = gr.Textbox(label="Review/Recensie:", 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/Recensie:", interactive=True, show_legend=True, combine_adjacent=False, color_map={"+": "red", "-": "green"})
placeholder_text = gr.Textbox(label="Review/Recensie:", 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/Recensie: ", value="HELLO! Hallo!", 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/Recensie: ", 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], outputs=[checkbox_1, interpretation1, lang_selected, num_selected, interpretation2, slider_1_1, slider_1_2, chatbot1])
# sample_button_nl.click(read1, inputs=[text_nl], outputs=[checkbox_1, interpretation1, lang_selected, num_selected, interpretation2, slider_1_1, slider_1_2, chatbot1])
# chat_button_1.click(func1, inputs=[lang_selected, num_selected, slider_1_1, num1, num2, checkbox_1], outputs=[slider_1_2, chatbot1, num1, num2, tot_scores, plot])
# interpre_button.click(interpre1, inputs=[lang_selected, num_selected], outputs=[interpretation2])
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, plot])
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()
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