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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, 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 = gr.Markdown( | |
| ''' | |
| #### <p style="text-align: center;"> Today's Scores:</p> | |
| #### <p style="text-align: center;"> 🤖 Machine   <span style="color: red;">''' + str(int(0)) + '''</span>   VS   <span style="color: green;">''' + str(int(0)) + '''</span>   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, [], "" | |
| # theme = gr.themes.Default(text_size=gr.themes.sizes.text_md).set( | |
| # input_text_size="24px", | |
| # ) | |
| 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") | |
| num1 = gr.Number(value=0, container=False, show_label=False, visible=False) | |
| num2 = gr.Number(value=0, container=False, show_label=False, visible=False) | |
| num3 = gr.Number(value=0, container=False, show_label=False, visible=False) | |
| num4 = gr.Number(value=0, container=False, show_label=False, visible=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| placeholder = gr.Markdown( | |
| ''' ## Welcome to the Language Model Explanation Challenge! <br /> | |
| #### Language Models are powerful AI tools to understand and generate human language.<br /> | |
| #### However, they sometimes make mistakes... and it's hard to know why!<br /> | |
| #### Choose one of the tasks below ... and start to play!''' | |
| ) | |
| with gr.Column(): | |
| # gr.Markdown( | |
| # ''' | |
| # ### Built by [ADD GroNLP logo here] | |
| # ''' | |
| # ) | |
| gr.Image('logo.png', height=30, width=90, min_width=80, show_label=False, show_share_button=False, interactive=False, container=False) | |
| placeholder = gr.Markdown( | |
| ''' | |
| Are *humans* or *machines* better at understanding language?<br /> | |
| → Play a game against AI to find out!<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 /> | |
| Can you outsmart the AI?<br /> | |
| → Try to write a text that will trick it into the wrong decision<br /><br /> | |
| ''' | |
| ) | |
| 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=2): | |
| 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="Your rating: Dislike(0) —> Like(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True) | |
| user_important = gr.Textbox(label="Which words are your guesses based on?", placeholder="Enter words that you think are important for the task") | |
| 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 10 (=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( | |
| #''' ## Today's Scores | |
| #''' | |
| #) | |
| tot_scores_1 = gr.Markdown( | |
| ''' | |
| #### <p style="text-align: center;"> Today's Scores:</p> | |
| #### <p style="text-align: center;"> 🤖 Machine   <span style="color: red;">''' + str(int(0)) + '''</span>   VS   <span style="color: green;">''' + str(int(0)) + '''</span>   Human 🙋 </p>''' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| chat_button_1 = gr.Button("Click to see AI's rating", size='sm') | |
| slider_1_2 = gr.Slider(label="AI rating: Dislike(0) —> Like(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True) | |
| interpre_button = gr.Button("See how AI got its rating", size='sm') | |
| placeholder_text = gr.Textbox(label="Red higlights: Positive / Blue higlights: Negative", value="HELLO! Hallo!", visible=False) | |
| interpretation2 = gr.components.Interpretation(placeholder_text) | |
| with gr.Column(scale=1): | |
| chatbot1 = gr.Chatbot(height=230, min_width=50, container=False) # height=300 | |
| #################################################################################################### | |
| gr.Markdown(''' *** ''') | |
| gr.Markdown( | |
| ''' # Now try with your own review! | |
| ''' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| 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="Your rating: Dislike(0) —> Like(10)", maximum=10, step=1, 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 rating", size='sm') | |
| placeholder_written_text = gr.Textbox(label="Red higlights: Positive / Blue higlights: Negative", value="HELLO! Hallo!", visible=False) | |
| interpretation4 = gr.components.Interpretation(placeholder_written_text) | |
| slider_1_4 = gr.Slider(label="AI rating: Dislike(0) —> Like(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True) | |
| with gr.Column(scale=1): | |
| 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_1]) | |
| 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(scale=2): | |
| 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="Your guess of author gender: Male(0) ——> Female(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True) | |
| user_important_mf = gr.Textbox(label="Which words are your guesses based on?", placeholder="Enter words that you think are important for the task") | |
| with gr.Column(scale=1): | |
| gr.Markdown( | |
| ''' ## Male or Female | |
| You're given a sentence written by a person. | |
| The goal of the game is to guess the gender of that person, from 0 (=Male) to 10 (=Female). | |
| - Step 1: Get a sentence and guess the gender of its author. | |
| - 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. | |
| ''' | |
| ) | |
| # gr.Markdown( | |
| # ''' ## Today's Scores | |
| # ''' | |
| # ) | |
| # tot_scores_2 = gr.Markdown( | |
| # ''' ### <p style="text-align: center;"> 🤖 Machine   ''' + str(int(0)) + '''   VS   ''' + str(int(0)) + '''   Human 🙋 </p>''' | |
| # ) | |
| tot_scores_2 = gr.Markdown( | |
| ''' | |
| #### <p style="text-align: center;"> Today's Scores:</p> | |
| #### <p style="text-align: center;"> 🤖 Machine   <span style="color: red;">''' + str(int(0)) + '''</span>   VS   <span style="color: green;">''' + str(int(0)) + '''</span>   Human 🙋 </p>''' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| chat_button_mf = gr.Button("Click to see AI's guess", size='sm') | |
| slider_3_2 = gr.Slider(label="AI guess on author gender: Male(0) ——> Female(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True) | |
| interpre_button_mf = gr.Button("See how AI made its guess", size='sm') | |
| placeholder_text_mf = gr.Textbox(label="Red higlights: Female / Blue higlights: Male", value="HELLO! Hallo!", visible=False) | |
| interpretation_mf_2 = gr.components.Interpretation(placeholder_text_mf) | |
| with gr.Column(scale=1): | |
| chatbot_mf_1 = gr.Chatbot(height=230, min_width=50, container=False) | |
| #################################################################################################### | |
| gr.Markdown(''' *** ''') | |
| gr.Markdown( | |
| ''' # Now try with your own sentence! | |
| ''' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| text_written_mf = gr.Textbox(label="Sentence: ", placeholder="Enter a sentence.", visible=True) | |
| slider_3_3 = gr.Slider(label="Your guess of author gender: Male(0) ——> Female(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True) | |
| chat_button_mf_2 = gr.Button("Click to see AI's guess", size='sm') | |
| placeholder_written_text_mf = gr.Textbox(label="Red higlights: Female / Blue higlights: Male", value="HELLO! Hallo!", visible=False) | |
| interpretation_mf_4 = gr.components.Interpretation(placeholder_written_text_mf) | |
| slider_3_4 = gr.Slider(label="AI guess on author gender: Male(0) ——> Female(10)", maximum=10, container=True, min_width=200, height=80, show_label=True, interactive=True) | |
| with gr.Column(scale=1): | |
| 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, num3, num4, user_important_mf], outputs=[slider_3_2, chatbot_mf_1, num3, num4, tot_scores_2]) | |
| 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() | |