Update app.py

app.py

@@ -44,6 +44,21 @@ 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"):
         with gr.Row(equal_height=True):