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=4096, 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'

{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]) demo.launch() # remove this line recommender = SemanticSearch(openAI_key) # call load_recommender instead to initialize recommender load_recommender('corpus.pdf', openAI_key)