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import urllib.request
import fitz
import re
import numpy as np
import tensorflow_hub as hub
import gradio as gr
import os
from sklearn.neighbors import NearestNeighbors
import requests
api_url="https://free.churchless.tech/v1/chat/completions"
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'[Страница {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.use = hub.load('/app/models/')
self.fitted = False
def fit(self, data, batch=1000, n_neighbors=2):
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(prompt):
data = {
"frequency_penalty": 0,
"model": "gpt-3.5-turbo",
"presence_penalty": 0,
"temperature":1,
"top_p": 1,
"messages":[{"role":"system","content":"You are ChatGPT, a large language model trained by OpenAI.\nCarefully heed the user's instructions. \nRespond using Markdown."},{"role":"user", "content": prompt}]
}
#print(data)
r = requests.post(api_url, json = data)
completions = r.json()
message = completions.get("choices")[0].get("message").get("content")
print(message)
return message
def generate_answer(question):
topn_chunks = recommender(question)
prompt = ""
prompt += 'Результаты поиска:\n\n'
for c in topn_chunks:
prompt += c + '\n\n'
prompt += (
"Инструкция: Составь исчерпывающий ответ на вопрос, используя приведенные результаты поиска. "
"Включай только информацию, найденную в результатах поиска, и "
"не добавляй никакой дополнительной информации. Убедись, что ответ правильный, и не выводи ложный контент. "
"Если текст не относится к воросу, просто укажи 'Информация не найдена в этом документе'. Игнорируй "
"результаты поиска, которые не имеют никакого отношения к вопросу. Отвечайте только на то, о чем тебя спрашивают. "
"Ответ должен быть коротким и лаконичным. Отвечай шаг за шагом.\n\n"
)
prompt += f"Запрос: {question}\nОтвет:"
print('prompt','->', prompt)
answer = generate_text(prompt)
return answer
class Empty():
pass
def question_answer(file, question):
if file == None:
load_recommender('/app/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)
recommender = SemanticSearch()
title = 'PDF GPT'
description = """ PDF GPT позволяет вам общаться в чате с вашим PDF-файлом, используя Universal Sentence Encoder и Open AI"""
with gr.Blocks() as demo:
gr.Markdown(f'<center><h1>{title}</h1></center>')
gr.Markdown(description)
with gr.Row():
with gr.Group():
gr.Markdown("<center><h4><h4></center>")
file = gr.File(label='Загрузите ваш PDF', file_types=['.pdf'])
gr.Markdown("<center><h4><h4></center>")
question = gr.Textbox(label='Введите ваш вопрос')
btn = gr.Button(value='Submit')
btn.style(full_width=True)
with gr.Group():
answer = gr.Textbox(label='Ответ на ваш вопрос :')
btn.click(question_answer, inputs=[file, question], outputs=[answer])
demo.launch() |