add qa api

app.py

@@ -1,4 +1,68 @@
 import streamlit as st
+
+# TODO: improve layout (columns, sidebar, forms)
+# st.set_page_config(layout='wide')
+
+
+st.title('Question answering help desk application')
+
+
+##########################################################
+st.subheader('1. A simple question')
+##########################################################
+
+
+WIKI_URL = 'https://en.wikipedia.org/w/api.php'
+WIKI_QUERY = "?format=json&action=query&prop=extracts&explaintext=1"
+WIKI_BERT = "&titles=BERT_(language_model)"
+WIKI_METHOD = 'GET'
+
+response = req.request(WIKI_METHOD, f'{WIKI_URL}{WIKI_QUERY}{WIKI_BERT}')
+resp_json = json.loads(response.content.decode("utf-8"))
+wiki_bert = resp_json['query']['pages']['62026514']['extract']
+paragraph =  wiki_bert
+
+written_passage = st.text_area(
+    'Paragraph used for QA (you can also edit, or copy/paste new content)', 
+    paragraph, 
+    height=250
+)
+if written_passage:
+    paragraph = written_passage
+
+question = 'How many languages does bert understand?'
+written_question = st.text_input(
+    'Question used for QA (you can also edit, and experiment with the answers)', 
+    question
+)
+if written_question:
+    question = written_question
+
+QA_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"
+QA_METHOD = 'POST'
+
+
+if st.button('Run QA inference (get answer prediction)'):
+    if paragraph and question:
+        inputs = {'question': question, 'context': paragraph}
+        payload = json.dumps(inputs)
+        prediction = req.request(QA_METHOD, QA_URL, data=payload)
+        answer = json.loads(prediction.content.decode("utf-8"))
+        answer_span = answer["answer"]
+        answer_score = answer["score"]
+        st.write(f'Answer: **{answer_span}**')
+        start_par = max(0, answer["start"]-86)
+        stop_para = min(answer["end"]+90, len(paragraph))
+        answer_context = paragraph[start_par:stop_para].replace(answer_span, f'**{answer_span}**')
+        st.write(f'Answer context (and score): ... _{answer_context}_ ... (score: {format(answer_score, ".3f")})')
+        st.write(f'Answer JSON: ')
+        st.write(answer)
+    else:
+        st.write('Write some passage of text and a question')
+        st.stop()
+
+
+"""
 from transformers import pipeline
 
 x = st.slider('Select a value')
@@ -6,10 +70,10 @@ st.write(x, 'squared is', x * x)
 
 question_answerer = pipeline("question-answering")
 
-context = r""" Extractive Question Answering is the task of extracting an answer from a text given a question. 
+context = r" Extractive Question Answering is the task of extracting an answer from a text given a question. 
 An example of a question answering dataset is the SQuAD dataset, which is entirely based on that task. 
 If you would like to fine-tune a model on a SQuAD task, you may leverage the 
-examples/pytorch/question-answering/run_squad.py script."""
+examples/pytorch/question-answering/run_squad.py script."
 question = "What is extractive question answering?" #"What is a good example of a question answering dataset?"
 result = question_answerer(question=question, context=context)
 answer = result['answer']
@@ -19,3 +83,4 @@ span = f"start: {result['start']}, end: {result['end']}"
 st.write(answer)
 st.write(f"score: {score}")
 st.write(f"span: {span}") 
+"""