update flagging options

app.py

@@ -16,6 +16,9 @@ clf = pipeline("text-classification", model=model.to("cpu"), tokenizer=tokenizer
 
 def clf_result(text_input: str) -> str:
 
+    if "best" not in text_input.lower():
+        res = "Please enter a sentence with the word `best`"
+        return res
     model_res = clf(text_input)[0]
     label_map = {"LABEL_0": "NOT RISKY", "LABEL_1": "RISKY"}
     label_res = label_map.get(model_res["label"])
@@ -29,10 +32,10 @@ demo = gr.Interface(
     title="Test High Risk Words model v2",
     examples=["All the best lenders and rates for car loans in one AI powered marketplace", "Caregiver burnout can happen to your best employees."],
     description="DistilBert for text classification model fine tuned on 70% of annotated RM production data combined with industry-specific webscrape data",
-    inputs=gr.Textbox(placeholder="Enter sentence here and press Submit", label="Sentence to check"),
+    inputs=gr.Textbox(placeholder="Enter sentence containing the word `best` here and press Submit", label="Sentence to check"),
     outputs="textbox",
     allow_flagging="manual",
-    flagging_options=["wrong result", "inconsistent result", "debatable input", "other"],
+    flagging_options=["wrong result :(", "correct result :)", "inconsistent result", "debatable input", "other"],
     flagging_callback=hf_writer,
 )