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1 Parent(s): 25e1b7c

Update app.py

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  1. app.py +6 -10
app.py CHANGED
@@ -1,11 +1,8 @@
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  """Requires gradio==4.27.0"""
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  import io
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- import shutil
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  import os
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  import json
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- import uuid
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  import time
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- import math
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  import datetime
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  import numpy as np
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@@ -40,16 +37,15 @@ BASE_LOCATION = [0, 23]
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  RULES = """<h1 style="margin-bottom: 0.5em">OSV-5M (plonk)</h1>
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  <center style="margin-bottom: 1em; margin-top: 1em"><img width="256" alt="Rotating globe" src="https://upload.wikimedia.org/wikipedia/commons/6/6b/Rotating_globe.gif"></center>
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  <h2 style="margin-top: 0.5em"> Instructions </h2>
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- <h3> Click on the map 🗺️ (left) to the location at which you think the image 🖼️ (right) was captured!</h3>
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- <h3 style="margin-bottom: 0.5em"> Click "Select" to finalize your selection and then "Next" to move to the next image.</h3>
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  <h2> AI Competitors </h2>
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- <h3> You will compete against two AIs: <b>Plonk-AI</b> (our best model) and Baseline-AI (a simpler approach).</h3>
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- <h3> These AIs have not been trained on any of the images you will see; in fact, they haven't seen anything within a <b>1km radius</b> of them.</h3>
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- <h3 style="margin-bottom: 0.5em"> Like you, the AIs will need to pick up on geographic clues to pinpoint the locations of the images.</h3>
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  <h2> Geoscore </h2>
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- <h3> The geoscore is calculated based on how close each guess is to the true location as in Geoguessr, with a maximum of <b>5000 points: $$\\large g(d) = 5000 \\exp\\left(\\frac{-d}{1492.7}\\right)$$ </h3>
 
 
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  """
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  css = """
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  @font-face {
@@ -575,4 +571,4 @@ if __name__ == "__main__":
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  next_button.click(next_, inputs=[state], outputs=[map_, results, image_, text_count, text, next_button, perf, coords, rules, text_end, select_button])
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  exit_button.click(exit_, inputs=[state], outputs=[map_, results, image_, text_count, text, next_button, perf, coords, rules, text_end, select_button])
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- demo.queue().launch(allowed_paths=["custom.ttf"], debug=True)
 
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  """Requires gradio==4.27.0"""
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  import io
 
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  import os
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  import json
 
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  import time
 
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  import datetime
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  import numpy as np
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  RULES = """<h1 style="margin-bottom: 0.5em">OSV-5M (plonk)</h1>
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  <center style="margin-bottom: 1em; margin-top: 1em"><img width="256" alt="Rotating globe" src="https://upload.wikimedia.org/wikipedia/commons/6/6b/Rotating_globe.gif"></center>
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  <h2 style="margin-top: 0.5em"> Instructions </h2>
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+ <h3 style="margin-bottom: 0.5em"> Click on the map 🗺️ (left) to the location at which you think the image 🖼️ (right) was captured!<br>Click "Select" to finalize your selection and then "Next" to move to the next image.</h3>
 
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  <h2> AI Competitors </h2>
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+ <h3 style="margin-bottom: 0.5em"> You will compete against two AIs: <b>Plonk-AI</b> (our best model) and Baseline-AI (a simpler approach).<br> These AIs have not been trained on any of the images you will see; in fact, they haven't seen anything within a <b>1km radius</b> of them.<br> Like you, the AIs will need to pick up on geographic clues to pinpoint the locations of the images.</h3>
 
 
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  <h2> Geoscore </h2>
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+ <h3> The geoscore is calculated based on how close each guess is to the true location as in Geoguessr, with a maximum of <b>5000 points:</b>
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+ <center style="margin-bottom: 0em; margin-top: 1em"><img src="https://latex.codecogs.com/svg.image?g(d)=5000\exp\left(\\frac{-d}{1492.7}\\right)"></img></center>
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+
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  """
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  css = """
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  @font-face {
 
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  next_button.click(next_, inputs=[state], outputs=[map_, results, image_, text_count, text, next_button, perf, coords, rules, text_end, select_button])
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  exit_button.click(exit_, inputs=[state], outputs=[map_, results, image_, text_count, text, next_button, perf, coords, rules, text_end, select_button])
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+ demo.queue().launch(allowed_paths=["custom.ttf", "geoscore.gif"], debug=True)