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| import streamlit as st | |
| from sentence_transformers import SentenceTransformer, util | |
| from transformers import (AutoModelForQuestionAnswering, | |
| AutoTokenizer, pipeline) | |
| import pandas as pd | |
| import regex as re | |
| # 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') | |
| book1_raw_0 = open("book_1.txt", mode="r", encoding="utf-8").read() | |
| # 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) |