Update app.py

app.py

@@ -40,6 +40,10 @@ def init_ner_pipeline():
     pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
     return pipe
 
+@st.cache(allow_output_mutation = True)
+def init_qa_pipeline():
+    question_answerer_pipe = pipeline("question-answering", model='distilbert-base-cased-distilled-squad')
+    return question_answerer_pipe
 
 def get_formatted_text_for_annotation(output):
     colour_map = {'Sex': '#5DD75D',
@@ -78,6 +82,7 @@ def get_formatted_text_for_annotation(output):
 pipeline_summarization = init_text_summarization_model()
 pipeline_zsl, template = init_zsl_topic_classification()
 pipeline_ner =init_ner_pipeline()
+pipeline_qa = init_qa_pipeline()
 
 st.header("Intelligent Document Automation")
 
@@ -98,13 +103,23 @@ if selected_menu == "Upload Document":
 elif selected_menu == "Extract Text":
     st.write(get_text_from_ocr_engine())
 elif selected_menu == "Summarize Document":
-    st.write(get_text_from_ocr_engine())
+    text = get_text_from_ocr_engine()
+    with st.spinner("Summarizing Document..."):
+        summary_text = pipeline_summarization(review, max_length=130, min_length=10, do_sample=False)
+        # Show output
+        st.write(summary_text[0]['summary_text'])
+        
 elif selected_menu == "Extract Entities":
     text = get_text_from_ocr_engine()
     output = pipeline_ner (text)
     entities_text =get_formatted_text_for_annotation(output)
     annotated_text(*entities_text)
 
-
 elif selected_menu == "Get Answers":
-    st.write(get_text_from_ocr_engine())
+    st.subheader('Question')
+    question_text = st.text_input("Type your question")
+    context = get_text_from_ocr_engine()
+    if question_text:
+        result = question_answerer(question=question_text,  context=context)
+        st.subheader('Answer')
+        st.text(result['answer'])