Update app.py

app.py

@@ -19,7 +19,12 @@ examples = [
   ["Lütfen yardım Akevler mahallesi Rüzgar sokak Tuncay apartmanı zemin kat Antakya akrabalarım göçük altında #hatay #Afad"]
 ]
 
-def create_record(input_text):
+def create_record(input_text, feedback):
+    # define the record status based on feedback
+    # default means it needs to be reviewed --> "Incorrect" or "Ambiguous"
+    # validated means it's correct and has been checked --> "Correct"
+    status = "Validated" if feedback == "Correct" else "Default"
+    
     # Making the prediction
     predictions = nlp(input_text, aggregation_strategy="first")
     
@@ -38,6 +43,7 @@ def create_record(input_text):
     record = rg.TokenClassificationRecord(
         text=input_text,
         tokens=words,
+        status=status,
         prediction=prediction,
         prediction_agent="deprem-ml/deprem-ner",
     )
@@ -59,7 +65,7 @@ class ArgillaLogger(FlaggingCallback):
     ) -> int:
         text = flag_data[0]
         inference = flag_data[1]
-        rg.log(name=self.dataset_name, records=create_record(text))
+        rg.log(name=self.dataset_name, records=create_record(text, flag_option))
 
 
         
@@ -68,8 +74,8 @@ gr.Interface.load(
     examples=examples,
     allow_flagging="manual",
     flagging_callback=ArgillaLogger(
-        api_url="https://merve-argilla.hf.space", 
-        api_key=os.getenv("TEAM_API_KEY"), 
+        api_url="https://dvilasuero-argilla-template-space-2.hf.space", 
+        api_key="team.apikey", 
         dataset_name="ner-flags"
     ),
     flagging_options=["Correct", "Incorrect", "Ambiguous"]