changed emoji and cleaned up the article

README.md

@@ -1,6 +1,6 @@
 ---
 title: Salary Predictor
-emoji: 🏆
+emoji: 💰
 colorFrom: blue
 colorTo: yellow
 sdk: gradio

app.py

@@ -31,14 +31,24 @@ article = """ This project was built by Marie Pelletier <br/>
             <a href="https://www.linkedin.com/in/marie-pelletier-14837441/">LinkedIn</a><br/>
             <a href="https://github.com/Marie000">Github</a>
         </p>
-        <p>This is a work in progress and is not meant to be used as an accurate predictor of salary. It is limited by the <a
-            href="https://www.kaggle.com/datasets/arshkon/linkedin-job-postings/data">data that was
-            used</a>, which is US-based, from 2023. It also does not take into account regions or fluctuations in the
-        market over time. The dataset included over 33,000 job postings, but only 13,000 had salary information.</p>
-        <p>For all you statistic nerds, the r-squared score of the model was around 0.6. For the rest of you, that level of accuracy can probably be described as "better than nothing". It might have been better if I had
-        more data to work with. Or maybe there is just so much we can infer from a description alone.</p>
-        <p>For this first test, only the description of the job is taken into account. Adding other information, including the date of the posting and the location, could improve the prediction.</p>
-        <p>The google colab notebook used to generate this model can be found <a href="https://github.com/Marie000/Linkedin-predictor-model">here</a>"""
+        <p>This is a work in progress and is not meant to be used as an accurate predictor of 
+        salary. It is limited by the <a
+            href="https://www.kaggle.com/datasets/arshkon/linkedin-job-postings/data">
+            data that was
+            used</a>, 
+            which is US-based, from 2023. It also does not take into account regions or 
+            fluctuations in the market over time. The dataset included over 33,000 job postings,
+              but only 13,000 had salary information.</p>
+        <p>For all you statistic nerds, the r-squared score of the model was around 0.6. 
+        For the rest of you, that level of accuracy can probably be described as "better than 
+        nothing". It might have been better if I had more data to work with. Or maybe there is 
+        just so much we can infer from a description alone.</p>
+
+        <p>For this first test, only the description of the job is taken into account. 
+        Adding other information, including the date of the posting and the location, 
+        could improve the prediction.</p>
+        <p>The google colab notebook used to generate this model can be found 
+        <a href="https://github.com/Marie000/Linkedin-predictor-model">here</a>"""
 
 demo = gr.Interface(
     fn=predict,