import streamlit as st
from sentence_transformers import SentenceTransformer, util
from transformers import (AutoModelForQuestionAnswering,
                          AutoTokenizer, pipeline)

import pandas as pd
import regex as re
from urllib import request

# Select model for question answering
model_name = "deepset/roberta-base-squad2"

# Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Create pipeline
pipe = pipeline('question-answering', model=model_name, tokenizer=model_name)

# Load Harry Potter book corpus from link
url = ("https://raw.githubusercontent.com/formcept/whiteboard/master/nbviewer/notebooks/data/harrypotter/Book%201%20-%20The%20Philosopher's%20Stone.txt")
response = request.urlopen(url)
book1_raw_0 = response.read().decode('utf8')

# Text pre-processing
# Remove page statements
book1_raw_1 = re.sub(r'Page \| [0-9]+ Harry Potter [a-zA-Z \-]+J.K. Rowling',
                     '', book1_raw_0)

# Remove newlines
book1_raw_1 = re.sub(r'\n', '', book1_raw_1)

# Remove periods; this will relevant in the regrouping later
book1_raw_1 = re.sub(r'Mr. ', 'Mr ', book1_raw_1)
book1_raw_1 = re.sub(r'Ms. ', 'Ms ', book1_raw_1)
book1_raw_1 = re.sub(r'Mrs. ', 'Mrs ', book1_raw_1)

# Group into 3 sentences-long parts
paragraphs = re.findall("[^.?!]+[.?!][^.?!]+[.?!][^.?!]+[.?!]", book1_raw_1)

# Type in HP-related query here
query = st.text_area("Hello muggle! What is your question?")

# Perform sentence embedding on query and sentence groups
model_embed_name = 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1'

model_embed = SentenceTransformer(model_embed_name)
doc_emb = model_embed.encode(paragraphs)
query_emb = model_embed.encode(query)

#Compute dot score between query and all document embeddings
scores = util.cos_sim(query_emb, doc_emb)[0].cpu().tolist()

#Combine docs & scores
doc_score_pairs = list(zip(paragraphs, scores))

#Sort by decreasing score and get only 3 most similar groups
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1],
                         reverse=True)[:3]

# Join these similar groups to form the context 
context = "".join(x[0] for x in doc_score_pairs)

# Perform the querying
QA_input = {'question': query, 'context': context}
out = pipe(QA_input)
st.json(out)