Update app.py

app.py

@@ -39,20 +39,6 @@ beto_sqac_model_spanish = 'salsarra/Beto-Spanish-Cased-SQAC'
 beto_sqac_model_spanish_qa = TFAutoModelForQuestionAnswering.from_pretrained(beto_sqac_model_spanish)
 beto_sqac_tokenizer_spanish = AutoTokenizer.from_pretrained(beto_sqac_model_spanish)
 
-# Define question_answering_v1 for ConfliBERT English
-def question_answering_v1(context, question):
-    try:
-        inputs = qa_tokenizer_v1(question, context, return_tensors='tf', truncation=True)
-        outputs = qa_model_v1(inputs)
-        answer_start = tf.argmax(outputs.start_logits, axis=1).numpy()[0]
-        answer_end = tf.argmax(outputs.end_logits, axis=1).numpy()[0] + 1
-        answer = qa_tokenizer_v1.convert_tokens_to_string(
-            qa_tokenizer_v1.convert_ids_to_tokens(inputs['input_ids'].numpy()[0][answer_start:answer_end])
-        )
-        return f"<span style='color: green; font-weight: bold;'>{answer}</span>"
-    except Exception as e:
-        return handle_error_message(e)
-        
 # Define error handling to separate input size errors from other issues
 def handle_error_message(e, default_limit=512):
     error_message = str(e)
@@ -70,6 +56,33 @@ def handle_error_message(e, default_limit=512):
 
     return f"<span style='color: red; font-weight: bold;'>Error: {error_message}</span>"
 
+# Define question_answering_v1 for ConfliBERT English
+def question_answering_v1(context, question):
+    try:
+        inputs = qa_tokenizer_v1(question, context, return_tensors='tf', truncation=True)
+        outputs = qa_model_v1(inputs)
+        answer_start = tf.argmax(outputs.start_logits, axis=1).numpy()[0]
+        answer_end = tf.argmax(outputs.end_logits, axis=1).numpy()[0] + 1
+        answer = qa_tokenizer_v1.convert_tokens_to_string(
+            qa_tokenizer_v1.convert_ids_to_tokens(inputs['input_ids'].numpy()[0][answer_start:answer_end])
+        )
+        return f"<span style='color: green; font-weight: bold;'>{answer}</span>"
+    except Exception as e:
+        return handle_error_message(e)
+        
+# Define bert_question_answering_v1 for BERT English
+def bert_question_answering_v1(context, question):
+    try:
+        inputs = bert_qa_tokenizer_v1(question, context, return_tensors='tf', truncation=True)
+        outputs = bert_qa_model_v1(inputs)
+        answer_start = tf.argmax(outputs.start_logits, axis=1).numpy()[0]
+        answer_end = tf.argmax(outputs.end_logits, axis=1).numpy()[0] + 1
+        answer = bert_qa_tokenizer_v1.convert_tokens_to_string(
+            bert_qa_tokenizer_v1.convert_ids_to_tokens(inputs['input_ids'].numpy()[0][answer_start:answer_end])
+        )
+        return f"<span style='font-weight: bold;'>{answer}</span>"
+    except Exception as e:
+        return handle_error_message(e)
 # Main comparison function with language selection
 def compare_question_answering(language, context, question):
     if language == "English":