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import os
import re
import shutil
import urllib.request
from pathlib import Path
from tempfile import NamedTemporaryFile
import fitz
import numpy as np
import openai
import tensorflow_hub as hub
from fastapi import UploadFile
from lcserve import serving
from sklearn.neighbors import NearestNeighbors
recommender = None
def download_pdf(url, output_path):
urllib.request.urlretrieve(url, output_path)
def preprocess(text):
text = text.replace('\n', ' ')
text = re.sub('\s+', ' ', text)
return text
def pdf_to_text(path, start_page=1, end_page=None):
doc = fitz.open(path)
total_pages = doc.page_count
if end_page is None:
end_page = total_pages
text_list = []
for i in range(start_page - 1, end_page):
text = doc.load_page(i).get_text("text")
text = preprocess(text)
text_list.append(text)
doc.close()
return text_list
def text_to_chunks(texts, word_length=150, start_page=1):
text_toks = [t.split(' ') for t in texts]
chunks = []
for idx, words in enumerate(text_toks):
for i in range(0, len(words), word_length):
chunk = words[i : i + word_length]
if (
(i + word_length) > len(words)
and (len(chunk) < word_length)
and (len(text_toks) != (idx + 1))
):
text_toks[idx + 1] = chunk + text_toks[idx + 1]
continue
chunk = ' '.join(chunk).strip()
chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
chunks.append(chunk)
return chunks
class SemanticSearch:
def __init__(self):
self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
self.fitted = False
def fit(self, data, batch=1000, n_neighbors=5):
self.data = data
self.embeddings = self.get_text_embedding(data, batch=batch)
n_neighbors = min(n_neighbors, len(self.embeddings))
self.nn = NearestNeighbors(n_neighbors=n_neighbors)
self.nn.fit(self.embeddings)
self.fitted = True
def __call__(self, text, return_data=True):
inp_emb = self.use([text])
neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
if return_data:
return [self.data[i] for i in neighbors]
else:
return neighbors
def get_text_embedding(self, texts, batch=1000):
embeddings = []
for i in range(0, len(texts), batch):
text_batch = texts[i : (i + batch)]
emb_batch = self.use(text_batch)
embeddings.append(emb_batch)
embeddings = np.vstack(embeddings)
return embeddings
def load_recommender(path, start_page=1):
global recommender
if recommender is None:
recommender = SemanticSearch()
texts = pdf_to_text(path, start_page=start_page)
chunks = text_to_chunks(texts, start_page=start_page)
recommender.fit(chunks)
return 'Corpus Loaded.'
def generate_text(openAI_key, prompt, engine="text-davinci-003"):
openai.api_key = openAI_key
try:
completions = openai.Completion.create(
engine=engine,
prompt=prompt,
max_tokens=512,
n=1,
stop=None,
temperature=0.7,
)
message = completions.choices[0].text
except Exception as e:
message = f'API Error: {str(e)}'
return message
def generate_answer(question, openAI_key):
topn_chunks = recommender(question)
prompt = ""
prompt += 'search results:\n\n'
for c in topn_chunks:
prompt += c + '\n\n'
prompt += (
"Instructions: Compose a comprehensive reply to the query using the search results given. "
"Cite each reference using [ Page Number] notation (every result has this number at the beginning). "
"Citation should be done at the end of each sentence. If the search results mention multiple subjects "
"with the same name, create separate answers for each. Only include information found in the results and "
"don't add any additional information. Make sure the answer is correct and don't output false content. "
"If the text does not relate to the query, simply state 'Text Not Found in PDF'. Ignore outlier "
"search results which has nothing to do with the question. Only answer what is asked. The "
"answer should be short and concise. Answer step-by-step. \n\nQuery: {question}\nAnswer: "
)
prompt += f"Query: {question}\nAnswer:"
answer = generate_text(openAI_key, prompt, "text-davinci-003")
return answer
def load_openai_key() -> str:
key = os.environ.get("OPENAI_API_KEY")
if key is None:
raise ValueError(
"[ERROR]: Please pass your OPENAI_API_KEY. Get your key here : https://platform.openai.com/account/api-keys"
)
return key
@serving
def ask_url(url: str, question: str):
download_pdf(url, 'corpus.pdf')
load_recommender('corpus.pdf')
openAI_key = load_openai_key()
return generate_answer(question, openAI_key)
@serving
async def ask_file(file: UploadFile, question: str) -> str:
suffix = Path(file.filename).suffix
with NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
shutil.copyfileobj(file.file, tmp)
tmp_path = Path(tmp.name)
load_recommender(str(tmp_path))
openAI_key = load_openai_key()
return generate_answer(question, openAI_key)
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