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'[{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.'
# The modified function generates embeddings based on PDF file name and page number and checks if the embeddings file exists before loading or generating it.
def load_recommender(path, start_page=1):
global recommender
pdf_file = os.path.basename(path)
embeddings_file = f"{pdf_file}_{start_page}.npy"
if os.path.isfile(embeddings_file):
embeddings = np.load(embeddings_file)
recommender.embeddings = embeddings
recommender.fitted = True
return "Embeddings loaded from file"
texts = pdf_to_text(path, start_page=start_page)
chunks = text_to_chunks(texts, start_page=start_page)
recommender.fit(chunks)
np.save(embeddings_file, recommender.embeddings)
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 [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 'Found Nothing'. Ignore outlier "\
"search results which has nothing to do with the question. Only answer what is asked. The "\
"answer should be short and concise.\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 = """ What is PDF GPT ?
1. The problem is that Open AI has a 4K token limit and cannot take an entire PDF file as input. Additionally, it sometimes returns irrelevant responses due to poor embeddings. ChatGPT cannot directly talk to external data. The solution is PDF GPT, which allows you to chat with an uploaded PDF file using GPT functionalities. The application breaks the document into smaller chunks and generates embeddings using a powerful Deep Averaging Network Encoder. A semantic search is performed on your query, and the top relevant chunks are used to generate a response.
2. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly. The Responses are much better than the naive responses by Open AI."""
with gr.Blocks() as demo:
gr.Markdown(f'{title}
')
gr.Markdown(description)
with gr.Row():
with gr.Group():
gr.Markdown(f'Get your Open AI API key here
')
openAI_key=gr.Textbox(label='Enter your OpenAI API key here')
url = gr.Textbox(label='Enter PDF URL here')
gr.Markdown("OR
")
file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
question = gr.Textbox(label='Enter your question here')
btn = gr.Button(value='Submit')
btn.style(full_width=True)
with gr.Group():
answer = gr.Textbox(label='The answer to your question is :')
btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
#openai.api_key = os.getenv('Your_Key_Here')
demo.launch()
# import streamlit as st
# #Define the app layout
# st.markdown(f'{title}
', unsafe_allow_html=True)
# st.markdown(description)
# col1, col2 = st.columns(2)
# # Define the inputs in the first column
# with col1:
# url = st.text_input('URL')
# st.markdown("or
", unsafe_allow_html=True)
# file = st.file_uploader('PDF', type='pdf')
# question = st.text_input('question')
# btn = st.button('Submit')
# # Define the output in the second column
# with col2:
# answer = st.text_input('answer')
# # Define the button action
# if btn:
# answer_value = question_answer(url, file, question)
# answer.value = answer_value