Add some beauty

app.py

@@ -17,7 +17,7 @@ def process_tweet(tweet):
     tweet = re.sub(r'#([^\s]+)', r'\1', tweet)
     # trim
     tweet = tweet.strip('\'"')
-    return tweet #if len(tweet) > 0 else ""
+    return tweet
 
 tokenizer = AutoTokenizer.from_pretrained(
     "azamat/geocoder_coordinates_model"
@@ -77,10 +77,10 @@ def predict(text):
 with gr.Blocks() as demo:
 
     gr.Markdown("# **<p align='center'>Twitter geocoding with 🤗 Transformers</p>**")
-    gr.Markdown("# **<div align='center'>Pipeline consists of:</div>**")
-    gr.Markdown("# **<div align='center'>1) Relevancy scoring model</div>**")
-    gr.Markdown("# **<div align='center'>2) Coordinate predicting model</div>**")
-    gr.Markdown("# **<div align='center'>3) Nominatim API for reverse geocoding lat/lon</div>**")
+    gr.Markdown("## **<div align='left'>Pipeline consists of:</div>**")
+    gr.Markdown("## **<div align='left'>1) Relevancy scoring model</div>**")
+    gr.Markdown("## **<div align='left'>2) Coordinate predicting model</div>**")
+    gr.Markdown("## **<div align='center'>3) Nominatim API for reverse geocoding lat/lon</div>**")
 
     inputs = gr.Textbox(placeholder="Enter the tweet")
     outputs = [gr.Dataframe(label="Geocoded data")]