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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 | |
| # Function to download a PDF from a given URL and save it to a specified output path | |
| def download_pdf(url, output_path): | |
| urllib.request.urlretrieve(url, output_path) | |
| # Function to preprocess text by removing newline characters and multiple spaces | |
| def preprocess(text): | |
| text = text.replace('\n', ' ') | |
| text = re.sub('\s+', ' ', text) | |
| return text | |
| # Function to extract text from a PDF file | |
| 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 | |
| # Function to split the text into chunks with a specified word length | |
| 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 for performing semantic search using the Universal Sentence Encoder model | |
| 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() | |
| # Function to load the recommender with text from a PDF file | |
| 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.' | |
| # Function to generate text using GPT-3 | |
| def generate_text(prompt, engine="text-davinci-003"): | |
| 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 | |
| # Function to generate an answer for a given question | |
| def generate_answer(question): | |
| 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 has 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) | |
| return answer | |
| # Function to handle user inputs, process the PDF, and generate an answer | |
| def question_answer(url, file, question, api_key): | |
| openai.api_key = api_key | |
| 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') | |
| 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) | |
| title = 'PDF_GPT' | |
| description = "Based on Pritish's BookGPT, PDF_GPT allows users to input PDFs and ask questions about their contents. This app uses GPT-3 to generate answers based on the PDF's information. References and page numbers are added for improved credibility." | |
| with gr.Blocks() as demo: | |
| gr.Markdown(f'<center><h1>{title}</h1></center>') | |
| gr.Markdown(description) | |
| gr.Markdown("To use, enter OpenAI API Key ") | |
| with gr.Row(): | |
| with gr.Group(): | |
| url = gr.Textbox(label='URL') | |
| gr.Markdown("<center><h6>or<h6></center>") | |
| file = gr.File(label='PDF', file_types=['.pdf']) | |
| question = gr.Textbox(label='Question') | |
| api_key = gr.Textbox(label = 'OpenAI API Key',type= 'password') | |
| btn = gr.Button(value='Submit') | |
| btn.style(full_width=True) | |
| with gr.Group(): | |
| answer = gr.Textbox(label='answer') | |
| btn.click(question_answer, inputs=[url, file, question, api_key], outputs=[answer]) | |
| demo.launch() | |