Update app.py

app.py

@@ -33,7 +33,7 @@ def preprocess_text(element):
         return ""
 
 
-def answer_question(text, question):
+def answer_question(text, question, max_length=512):
     """Answers a question using the provided text and a pre-trained model.
 
     Args:
@@ -43,11 +43,15 @@ def answer_question(text, question):
     Returns:
         The answer extracted from the text using the model.
     """
-    qa_model_name = "bert-base-uncased"  # Replace with your chosen TensorFlow QA model
+    qa_model_name = "bert-base-uncased"  # Replace with your chosen model
 
     qa_model = TFBertForQuestionAnswering.from_pretrained(qa_model_name)
     tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
 
+    # Truncate text if necessary
+    if len(text) > max_length:
+        text = text[:max_length]
+
     inputs = tokenizer(question, text, return_tensors="tf")  # Tokenize inputs for TensorFlow
 
     start_logits, end_logits = qa_model(inputs)