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import matplotlib.pyplot as plt
import torch, torchvision, clip, random
import random, math, time, os
from PIL import Image
import gradio as gr
import numpy as np
from sentence import *
from game import *
##### Initialize new game
title, _,_,var_dict = new_game(None, None, first_game=True)
var_dict["start_time"] = -1
##### Display & Events
demo = gr.Blocks()
with demo:
### All game variables are stored here
variables = gr.Variable(var_dict)
### Target Sentence
title = gr.HTML(title)
### Canvas & Prediction
with gr.Column():
with gr.Row():
image_input = gr.Image(image_mode='L', label="", show_label=False, source='canvas', shape=None, streaming=False, invert_colors=False, tool="editor")
with gr.Column():
html_pred = gr.HTML(value=getHTML(var_dict,""))
html_loading = gr.HTML("")
### 'New Sentence' Button
with gr.Row():
button_new = gr.Button("New Sentence",variant="primary")
button_mode = gr.Button("Switch Difficulty")
block = gr.HTML("<div style=\"width:50vw;\"> </div>")
### Informations
gr.HTML("<div style=\"display:block; height:30px;\"> </div>")
with gr.Row():
gr.HTML("<div style=\"display:block; position:relative; bottom:10%; border-top: 1px solid grey; padding:10px; \"><span style=\"font-size:30px;\">✏️</span><span style=\"font-size:40px; font-weight:bold;\">CLIPictionary!</span><br>Draw to make the model guess the target sentence displayed at the top!<br>Made by <a href=\"https://yoann-lemesle.notion.site/Yoann-Lemesle-63b8120764284794b275d2967be710da\" style=\"text-decoration: underline;\">Yoann Lemesle</a> using OpenAI's <a href=\"https://github.com/openai/CLIP\" style=\"text-decoration: underline;\">CLIP model</a>.</div>")
### Events
button_mode.click(switch_difficulty,inputs=[variables,html_loading],outputs=[variables,title,html_pred,html_loading])
button_new.click(loading,inputs=html_loading,outputs=[title,html_pred,html_loading]) # Button -> triggers Loading
html_loading.change(new_game,inputs=[variables,html_loading],outputs=[title,html_pred,image_input,variables]) # Loading -> triggers New game
image_input.change(process_img, inputs=[variables,image_input,title], outputs=[html_pred,title,variables])
demo.launch(share=False)
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