Update app.py

app.py

@@ -4,17 +4,6 @@ from transformers import pipeline
 from sentence_transformers import CrossEncoder
 from  transformers  import  AutoTokenizer, AutoModelWithLMHead, pipeline
 
-model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelWithLMHead.from_pretrained(model_name)
-
-#from transformers import pipeline
-
-#text2text_generator = pipeline("text2text-generation", model = "gpt2")
-
-sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
-passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-qa_model = pipeline("question-answering",'a-ware/bart-squadv2')
 
 def fetch_answers(question, document ):
     document_paragraphs = document.splitlines()
@@ -56,10 +45,22 @@ def fetch_answers(question, document ):
      
     return top_5_query_paragraph_answer_list
 
+
 st.title('Document Question Answering System')
 query = st.text_area("Query", "", height=25)
 document = st.text_area("Document Text", "", height=100)
 
+
+
 if st.button("Get Answers From Document"):
-  st.markdown(fetch_answers(query, document))
+
+    model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelWithLMHead.from_pretrained(model_name)
+    
+    sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
+    passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+    qa_model = pipeline("question-answering",'a-ware/bart-squadv2')
+  
+    st.markdown(fetch_answers(query, document))