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import urllib.request
import fitz
import re
import numpy as np
import tensorflow_hub as hub
import openai
import gradio as gr
import os
from sklearn.neighbors import NearestNeighbors
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]
page_nums = []
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
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
completions = openai.Completion.create(
engine=engine,
prompt=prompt,
max_tokens=512,
n=1,
stop=None,
temperature=0.7,
)
message = completions.choices[0].text
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 question_answer(url, file, question,openAI_key):
if openAI_key.strip()=='':
return '[ERROR]: Please enter you Open AI Key. Get your key here : https://platform.openai.com/account/api-keys'
if url.strip() == '' and file == None:
return '[ERROR]: Both URL and PDF is empty. Provide atleast one.'
if url.strip() != '' and file != None:
return '[ERROR]: Both URL and PDF is provided. Please provide only one (eiter URL or PDF).'
if url.strip() != '':
glob_url = url
download_pdf(glob_url, 'corpus.pdf')
load_recommender('corpus.pdf')
else:
old_file_name = file.name
file_name = file.name
file_name = file_name[:-12] + file_name[-4:]
os.rename(old_file_name, file_name)
load_recommender(file_name)
if question.strip() == '':
return '[ERROR]: Question field is empty'
return generate_answer(question,openAI_key)
recommender = SemanticSearch()
title = 'PDF GPT'
description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. Thequote("def load_recommender(path, start_page=1):", "return generate_answer(question,openAI_key)")
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