Update app.py

app.py

@@ -38,18 +38,18 @@ IMAGE_FOLDER = './images'
 CSV_FILE = './select.csv'
 BASE_LOCATION = [0, 23]
 RULES = """<h1>OSV-5M (plonk)</h1>
-<center><img width="256" alt="Rotating globe" src="https://upload.wikimedia.org/wikipedia/commons/6/6b/Rotating_globe.gif"></center>
+<center><img width="256" alt="Rotating globe" src="https://upload.wikimedia.org/wikipedia/commons/6/6b/Rotating_globe.gif" style="margin-bottom: 10px;"></center>
 <h2> Instructions </h2>
 <h3> Click on the map 🗺️ (left) to the location at which you think the image 🖼️ (right) was captured!</h3>
 <h3> Click "Select" to finalize your selection and then "Next" to move to the next image.</h3>
 
 <h2> AI Competitors </h2>
-<h3> You will compete against two AIs: Plonk-AI (our best model) and Baseline-AI (a simpler approach).</h3>
-<h3> These AIs have not been trained on any of the images you will see; in fact, they haven't seen anything within a 1km radius of them.</h3>
+<h3> You will compete against two AIs: <b>Plonk-AI</b> (our best model) and Baseline-AI (a simpler approach).</h3>
+<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>
 <h3> Like you, the AIs will need to pick up on geographic clues to pinpoint the locations of the images.</h3>
 
 <h2> Geoscore </h2>
-<h3> The geoscore is calculated based on how close each guess is to the true location as in Geoguessr, with a maximum of 5000 points. We use the formula $g(d) = 5000 \\exp(\\frac{-d}/{1492.7})$ </h3>
+<h3> The geoscore is calculated based on how close each guess is to the true location as in Geoguessr, with a maximum of 5000 points. We use the formula $$g(d) = 5000 \\exp(\\frac{-d}/{1492.7})$$ </h3>
 """
 css = """
 @font-face {