Update app.py

app.py

@@ -28,7 +28,6 @@ def fetch_answers(question, document ):
      
                   
      model_input = f"question: {query} context: {evidence_sentence}"
-     #output_answer = text2text_generator(model_input)[0]['generated_text']
      encoded_input = tokenizer([model_input],
                                return_tensors='pt',
                                max_length=512,
@@ -49,26 +48,26 @@ def fetch_answers(question, document ):
 st.title('Document Question Answering System')
 query = st.text_area("Query", "", height=25)
 document = st.text_area("Document Text", "", height=100)
+st.text("Progress Bar")
 my_bar = st.progress(0)
+my_bar.progress(10)
 
-
-if st.button("Get Answers From Document"):
-    my_bar.progress(10)
-
-    model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelWithLMHead.from_pretrained(model_name)
+model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelWithLMHead.from_pretrained(model_name)
     
-    my_bar.progress(25)
+my_bar.progress(25)
     
-    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')
+sentence_segmenter = pysbd.Segmenter(language='en',clean=False)
+passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+my_bar.progress(50)
+qa_model = pipeline("question-answering",'a-ware/bart-squadv2')
   
-    my_bar.progress(75)
+my_bar.progress(75)
+
+if st.button("Get Answers From Document"):
     
     st.markdown(fetch_answers(query, document))
-    
     my_bar.progress(100)