Update app.py

app.py

@@ -50,27 +50,22 @@ def predict_answer(context, question):
 # Load short model
 short_answer_model = pipeline(model="Nighter/QA_wiki_data_short_answer", from_tf=True)
 
-# def answer_questions(context, question):
-#     long_answer = predict_answer(context, question)
-#     short_answer_result = short_answer_model(question=question, context=remove_parentheses(context))
-#     return short_answer_result['answer'], long_answer
-
+# Function to answer on all models
 def answer_questions(context, question):
     long_score, long_answer = predict_answer(context, question)
-
-    # Check if the original context is longer than 512 tokens
-    if len(tokenizer.texts_to_sequences([context])[0]) > 512:
-        # If yes, use the long answer as the context for the short answer model
-        short_context = long_answer
-    else:
-        # If no, use the original context
-        short_context = remove_parentheses(context)
-
-    short_answer_result = short_answer_model(question=question, context=short_context)
-
+    # # Check if the original context is longer than 512 tokens
+    # if len(tokenizer.texts_to_sequences([context])[0]) > 512:
+    #     # If yes, use the long answer as the context for the short answer model
+    #     short_context = long_answer
+    # else:
+    #     # If no, use the original context
+    #     short_context = remove_parentheses(context)
+
+    # short_answer_result = short_answer_model(question=question, context=short_context)
+    short_answer_result = short_answer_model(question=question, context=remove_parentheses(context))
     return short_answer_result['answer'], short_answer_result['score'], long_answer, long_score
 
-
+# App Interface
 with gr.Blocks() as app:
     gr.Markdown("<center> <h1>Question Answering with Short and Long Answer Models </h1> </center><hr>")
     with gr.Row():