Update app.py

app.py

@@ -45,22 +45,21 @@ def analyze_selected_texts(selections):
 with gr.Blocks() as demo:
     event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier"]
 
-    gr.Markdown(
-    """
-    # T4.5 Relevance Classifier Demo
-    This is a demo created to explore floods and wildfire classification in social media posts.
-    Usage:
-        - Upload .tsv data file (must contain a text column with social media posts).
-        - Next, type the name of the text column.
-        - Then, choose a BERT classifier model from the drop down.
-        - Finally, click the 'start classification' buttton.
-    Evaluation:
-        - To evaluate the model's accuracy select the INCORRECT classifications using the checkboxes in front of each post.
-        - Then, click on the 'Calculate Accuracy' button.
-        - Then, click on the 'Download data as CSV' to get the classifications and evaluation data as a .csv file.
-    """)
-    
     with gr.Tab("Event Type Classification"):
+        gr.Markdown(
+        """
+        # T4.5 Relevance Classifier Demo 
+        This is a demo created to explore floods and wildfire classification in social media posts.\n
+        Usage:\n
+            - Upload .tsv data file (must contain a text column with social media posts).\n
+            - Next, type the name of the text column.\n
+            - Then, choose a BERT classifier model from the drop down.\n
+            - Finally, click the 'start classification' buttton.\n
+        Evaluation:\n
+            - To evaluate the model's accuracy select the INCORRECT classifications using the checkboxes in front of each post.\n
+            - Then, click on the 'Calculate Accuracy' button.\n
+            - Then, click on the 'Download data as CSV' to get the classifications and evaluation data as a .csv file.
+        """)
         with gr.Row(equal_height=True):
             with gr.Column(scale=4):
                 file_input = gr.File(label="Upload CSV File")