Update app.py

app.py

@@ -9,8 +9,11 @@ from statistics import mean
 
 HFTOKEN = os.environ["HF_TOKEN"]
 
-def load_and_analyze_csv(file, text_field, event_model):
-    df = pd.read_table(file.name)
+def load_and_classify_csv(file, text_field, event_model):
+    if ".csv" in file.name:
+        df = pd.read_csv(file.name)
+    else ".tsv" in file.name:
+        df = pd.read_table(file.name)
     
     if text_field not in df.columns:
         raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
@@ -91,7 +94,7 @@ with gr.Blocks() as demo:
         # 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
+        (1.) 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 prediction' buttton.\n
@@ -102,7 +105,7 @@ with gr.Blocks() as demo:
         """)
         with gr.Row(equal_height=True):
             with gr.Column(scale=4):
-                file_input = gr.File(label="Upload CSV File")
+                file_input = gr.File(label="Upload CSV or TSV File", file_types=['.tsv', '.csv'])
                 
             with gr.Column(scale=6):
                 text_field = gr.Textbox(label="Text field name", value="tweet_text")
@@ -150,7 +153,7 @@ with gr.Blocks() as demo:
         data_eval = gr.DataFrame(visible=False)
         
         predict_button.click(
-            load_and_analyze_csv, 
+            load_and_classify_csv, 
             inputs=[file_input, text_field, event_model], 
             outputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, model_confidence, num_posts, data])
         accuracy_button.click(