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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'[{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
recommender = SemanticSearch()
pdf_paths = [] # List to store multiple PDF paths
def load_recommender(paths, start_page=1):
global recommender, pdf_paths
pdf_paths = paths
texts = []
for path in paths:
texts.extend(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(prompt,engine):
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):
engine = os.environ('Engine')
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 have 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(prompt, engine)
return answer
def question_answer(files, question, secret):
api_key = os.environ.get('AzureKey')
url_base = os.environ.get('AzureUrlBase')
if api_key is None or url_base is None:
return '[ERROR]: Please provide the Azure API Key and URL Base as environment variables.'
openai.api_key = api_key
openai.api_type = "azure"
openai.api_base = url_base
openai.api_version = "2022-12-01"
if files == []:
return '[ERROR]: Please provide at least one PDF.'
if secret != os.environ.get('Secret'):
return '[Error]: Please provide the correct secret'
else:
loaded_files = []
for file in files:
old_file_name = file.name
file_name = file.name
file_name = file_name[:-12] + file_name[-4:]
os.rename(old_file_name, file_name)
loaded_files.append(file_name)
load_recommender(loaded_files)
if question.strip() == '':
return '[ERROR]: Question field is empty.'
return generate_answer(question)
title = 'AzurePDFGPT'
description = "A test platform for indexing PDFs to in order to 'chat' with them. It is hardcoded to the Jaytest and MLSLGPT engine"
with gr.Interface(
fn=question_answer,
inputs=[
gr.File(label='PDFs', file_types=['.pdf'], file_count="multiple"),
gr.Textbox(label='Question'),
gr.Textbox(label='Secret')
],
outputs=gr.Textbox(label='Answer'),
title=title,
description=description
) as iface:
iface.launch()
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