app.py

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, file_names, word_length=150, start_page=1):
    text_toks = [t.split(' ') for t in texts]
    page_nums = []
    chunks = []
    
    current_file_idx = 0

    for idx, words in enumerate(text_toks):
        if idx > 0 and idx % len(file_names) == 0:
            current_file_idx += 1

        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'[{file_names[current_file_idx]}, 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(paths, start_page=1):
    global recommender
    all_texts = []

    for path in paths:
        texts = pdf_to_text(path, start_page=start_page)
        all_texts.extend(texts)

    chunks = text_to_chunks(all_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 [PDF FILE NAME, 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 have 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, files, question, openAI_key):
    if openAI_key.strip() == '':
        return '[ERROR]: Please enter your Open AI Key. Get your key here : https://platform.openai.com/account/api-keys'
    if url.strip() == '' and not files:
        return '[ERROR]: Both URL and PDF are empty. Provide at least one.'

    if url.strip() != '' and files:
        return '[ERROR]: Both URL and PDF are provided. Please provide only one (either URL or PDF).'

    pdf_paths = []

    if url.strip() != '':
        glob_url = url
        output_path = 'corpus.pdf'
        download_pdf(glob_url, output_path)
        pdf_paths.append(output_path)

    else:
        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)
            pdf_paths.append(file_name)

    if question.strip() == '':
        return '[ERROR]: Question field is empty'

    load_recommender(pdf_paths)
    return generate_answer(question, openAI_key)

recommender = SemanticSearch()
title = 'PDF GPT (Sandbox)'

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. The returned response can even cite the page number and file name in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""

with gr.Blocks() as demo:

    gr.Markdown(f'<center><h1>{title}</h1></center>')
    gr.Markdown(description)

    with gr.Row():
        
        with gr.Group():
            gr.Markdown(f'<p style="text-align:center">Get your Open AI API key <a href="https://platform.openai.com/account/api-keys">here</a></p>')
            openAI_key = gr.Textbox(label='Enter your OpenAI API key here')
            url = gr.Textbox(label='Enter PDF URL here')
            gr.Markdown("<center><h4>OR<h4></center>")
            files = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'], file_count='multiple')
            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, files, question, openAI_key], outputs=[answer])

demo.launch()