app.py

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, func1_written
from game2 import func2
from game3 import read3, func3, interpre3, func3_written


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 &ensp; ''' + str(int(0)) + ''' &ensp; VS &ensp; ''' + str(int(0)) + ''' &ensp; Human </p>'''

    # 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')
    pre_load_4 = pipeline("text-classification", model="padmajabfrl/Gender-Classification")

    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)

        with gr.Column(scale=3):
            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 />
    &rarr; Play a game against AI to find out!<br /><br />
    Does AI think like you or not at all?<br />
    &rarr; 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 />
    &rarr; 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(scale=1):
            logo = gr.Image('logo.png', height=230, width=600, min_width=80, show_label=False, show_share_button=False, interactive=False, container=False)

            gr.Markdown(
                ''' ## Today's Scores
                '''
            )
            tot_scores = gr.Markdown(
                ''' ### <p style="text-align: center;"> Machine &ensp; ''' + str(int(0)) + ''' &ensp; VS &ensp; ''' + str(int(0)) + ''' &ensp; Human </p>'''
            )
            
    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_1 = gr.Number(value=0, container=False, show_label=False, visible=False)

        with gr.Row():
            with gr.Column(scale=1):
                with gr.Row():
                    sample_button_en = gr.Button("Click to get a review in English.", size='sm')
                    # gr.Markdown(''' <p style="text-align: center;"> or </p> ''')
                    sample_button_nl = gr.Button("Click to get a review in Dutch.", size='sm')

                input_text = gr.Textbox(label="Review:", value="HELLO! Hallo!", visible=False, container=False)
                interpretation1 = gr.components.Interpretation(input_text)

                slider_1_1 = gr.Slider(label="Human: Dislike ——> Like", container=True, min_width=200, height=80, show_label=True, interactive=True)
                user_important = gr.Textbox(label="Which words are the guesses based on?", placeholder="Enter words that you think are important.")

            with gr.Column(scale=1):
                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.
                '''
                )      
        
        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')
                placeholder_text = gr.Textbox(label="Review:", value="HELLO! Hallo!", visible=False)
                interpretation2 = gr.components.Interpretation(placeholder_text)
            chatbot1 = gr.Chatbot(height=200, min_width=50, container=False) # height=300
        ####################################################################################################
        gr.Markdown(''' *** ''')

        gr.Markdown(
                ''' # Now try your own reviews!
                '''
        )

        with gr.Row():
            with gr.Column():
                text_written = gr.Textbox(label="Review: ", placeholder="Enter 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_1], outputs=[interpretation1, lang_selected, num_selected_1])
    sample_button_nl.click(read1, inputs=[text_nl, num_selected_1], outputs=[interpretation1, lang_selected, num_selected_1])
    num_selected_1.change(reset_modules, outputs=[interpretation2, slider_1_1, slider_1_2, chatbot1, user_important])
    chat_button_1.click(func1, inputs=[lang_selected, num_selected_1, 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_1], outputs=[interpretation2])

    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("Male or Female"):
        num_selected_3 = gr.Number(value=0, container=False, show_label=False, visible=False)

        with gr.Row():
            with gr.Column():
                with gr.Row():
                    # gr.Markdown(''' <p style="text-align: center;"> or </p> ''')
                    sample_button_en_3 = gr.Button("Click to get a sentence.", size='sm')
                input_text_mf = gr.Textbox(label="Sentence:", value="HELLO! Hallo!", visible=False, container=False)
                interpretation_mf_1 = gr.components.Interpretation(input_text_mf)
                slider_3_1 = gr.Slider(label="Human: Male ——> Female", container=True, min_width=200, height=80, show_label=True, interactive=True)
                user_important_mf = gr.Textbox(label="Which words are the guesses based on?", placeholder="Enter words that you think are important.")
                
            gr.Markdown(
            ''' ## Male or Female
            
            You're given a sentence spoken by a speaker.
            The goal of this game is to guess the gender of the speaker, from 0 (=Male) to 100 (=Female).
            
            * Step 1. Get a sentence and guess the gender of the speaker.
            
            * Step 2. Check the gender guessed by AI. Who gets the most correct answer wins.
            
            * Step 3. Check the word highlighting to understand how AI made its decision.
            '''
            )      
        
        with gr.Row():
            with gr.Column():
                chat_button_mf = gr.Button("Click to see AI's answer.", size='sm')
                slider_3_2 = gr.Slider(label="AI: Male ——> Female", container=True, min_width=200, height=80, show_label=True, interactive=True)
                interpre_button_mf = gr.Button("See how AI gets the answer.", size='sm')
                placeholder_text_mf = gr.Textbox(label="Sentence:", value="HELLO! Hallo!", visible=False)
                interpretation_mf_2 = gr.components.Interpretation(placeholder_text_mf)
            chatbot_mf_1 = gr.Chatbot(height=200, min_width=50, container=False) # height=300
        ####################################################################################################
        gr.Markdown(''' *** ''')

        gr.Markdown(
                ''' # Now try your own sentence!
                '''
        )

        with gr.Row():
            with gr.Column():
                text_written_mf = gr.Textbox(label="Sentence: ", placeholder="Enter your sentence.", visible=True)
                slider_3_3 = gr.Slider(label="Human: Male ——> Female", container=True, min_width=200, height=80, show_label=True, interactive=True)
                chat_button_mf_2 = gr.Button("Click to see AI's answer.", size='sm')
                placeholder_written_text_mf = gr.Textbox(label="Sentence: ", value="HELLO! Hallo!", visible=False)
                interpretation_mf_4 = gr.components.Interpretation(placeholder_written_text_mf)
                slider_3_4 = gr.Slider(label="AI: Male ——> Female", container=True, min_width=200, height=80, show_label=True, interactive=True)
            chatbot_mf_2 = gr.Chatbot(height=350, min_width=50, container=False) # height=300

    sample_button_en_3.click(read3, inputs=[num_selected_3], outputs=[interpretation_mf_1, num_selected_3])
    num_selected_3.change(reset_modules, outputs=[interpretation_mf_2, slider_3_1, slider_3_2, chatbot_mf_1, user_important_mf])
    chat_button_mf.click(func3, inputs=[num_selected_3, slider_3_1, num1, num2, user_important_mf], outputs=[slider_3_2, chatbot_mf_1, num1, num2, tot_scores])    
    interpre_button_mf.click(interpre3, inputs=[num_selected_3], outputs=[interpretation_mf_2])

    chat_button_mf_2.click(func3_written, inputs=[text_written_mf, slider_3_3], outputs=[interpretation_mf_4, slider_3_4, chatbot_mf_2])

if __name__ == "__main__":
    demo.launch()