app.py

import urllib.request
import fitz
import re
import numpy as np
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, openAI_key):
        self.openAI_key = openAI_key
        self.fitted = False

    def fit(self, data, n_neighbors=5):
        self.data = data
        self.embeddings = self.get_text_embedding(data)
        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.get_text_embedding([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):
        prompt = "Embed the following texts:"
        for text in texts:
            prompt += f"\n\n{text}"

        openai.api_key = self.openAI_key
        completions = openai.Completion.create(
            engine="text-davinci-003",
            prompt=prompt,
            max_tokens=len(texts) * 128,
            n=1,
            stop=None,
            temperature=0.5,
        )

        message = completions.choices[0].text
        embeddings = []
        for emb_str in message.split("\n"):
            emb_str = emb_str.strip()
            if emb_str:
                emb = np.array([float(x) for x in emb_str.split()])
                embeddings.append(emb)
        embeddings = np.array(embeddings)
        return embeddings

def load_recommender(path, openAI_key, start_page=1):
    global recommender
    texts = pdf_to_text(path, start_page=start_page)
    chunks = text_to_chunks(texts, start_page=start_page)
    recommender = SemanticSearch(openAI_key)  # add the openAI_key parameter here
    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 at least one.'

    if url.strip() != '' and file != None:
        return '[ERROR]: Both URL and PDF is provided. Please provide only one (either URL or PDF).'

    if url.strip() != '':
        glob_url = url
        download_pdf(glob_url, 'corpus.pdf')
        load_recommender('corpus.pdf', openAI_key)

    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, openAI_key)

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

    return generate_answer(question, openAI_key)

recommender = None

# Add your Gradio UI code here
title = 'PDF GPT'
description = """With PDF GPT, you can chat with your PDF files/books and get precise answers."""

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>")
            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])

demo.launch()

# remove this line
recommender = SemanticSearch(openAI_key)

# call load_recommender instead to initialize recommender
load_recommender('corpus.pdf', openAI_key)