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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 | |
| 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'[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(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(openAI_key, prompt, model="gpt-3.5-turbo"): | |
| openai.api_key = openAI_key | |
| temperature=0.7 | |
| max_tokens=256 | |
| top_p=1 | |
| frequency_penalty=0 | |
| presence_penalty=0 | |
| if model == "text-davinci-003": | |
| completions = openai.Completion.create( | |
| engine=model, | |
| prompt=prompt, | |
| max_tokens=max_tokens, | |
| n=1, | |
| stop=None, | |
| temperature=temperature, | |
| ) | |
| message = completions.choices[0].text | |
| else: | |
| message = openai.ChatCompletion.create( | |
| model=model, | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "assistant", "content": "Here is some initial assistant message."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| temperature=.3, | |
| max_tokens=max_tokens, | |
| top_p=top_p, | |
| frequency_penalty=frequency_penalty, | |
| presence_penalty=presence_penalty, | |
| ).choices[0].message['content'] | |
| return message | |
| def generate_answer(question, openAI_key, model): | |
| topn_chunks = recommender(question) | |
| 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. "\ | |
| "Only answer what is asked. The answer should be short and concise. \n\nQuery: " | |
| prompt += f"{question}\nAnswer:" | |
| answer = generate_text(openAI_key, prompt, model) | |
| return answer | |
| def question_answer(chat_history, url, file, question, openAI_key, model): | |
| try: | |
| 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 file is None: | |
| return '[ERROR]: Both URL and PDF is empty. Provide at least one.' | |
| if url.strip() != '' and file is not None: | |
| return '[ERROR]: Both URL and PDF is provided. Please provide only one (either URL or PDF).' | |
| if model is None or model =='': | |
| return '[ERROR]: You have not selected any model. Please choose an LLM model.' | |
| 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' | |
| if model == "text-davinci-003" or model == "gpt-4" or model == "gpt-4-32k": | |
| answer = generate_answer_text_davinci_003(question, openAI_key) | |
| else: | |
| answer = generate_answer(question, openAI_key, model) | |
| chat_history.append([question, answer]) | |
| return chat_history | |
| except openai.error.InvalidRequestError as e: | |
| return f'[ERROR]: Either you do not have access to GPT4 or you have exhausted your quota!' | |
| def generate_text_text_davinci_003(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_text_davinci_003(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 '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_text_davinci_003(openAI_key, prompt,"text-davinci-003") | |
| return answer | |
| # pre-defined questions | |
| questions = [ | |
| "What did the study investigate?", | |
| "Can you provide a summary of this paper?", | |
| "what are the methodologies used in this study?", | |
| "what are the data intervals used in this study? Give me the start dates and end dates?", | |
| "what are the main limitations of this study?", | |
| "what are the main shortcomings of this study?", | |
| "what are the main findings of the study?", | |
| "what are the main results of the study?", | |
| "what are the main contributions of this study?", | |
| "what is the conclusion of this paper?", | |
| "what are the input features used in this study?", | |
| "what is the dependent variable in this study?", | |
| ] | |
| def clear_all(chat_history, url, file, question, model): | |
| # Return default values for all fields | |
| return [], '', None, '', 'gpt-3.5-turbo' | |
| # Create a new button with the label "Clear" | |
| clear_btn = gr.Button(value='Clear') | |
| # Bind the click event of the "Clear" button to the clear_all function | |
| clear_btn.click(clear_all,inputs=[chatbot, url, file, question, model],outputs=[chatbot, url, file, question, model],) | |
| recommender = SemanticSearch() | |
| title = 'PDF GPT Turbo' | |
| description = """ PDF GPT Turbo 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 in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly.""" | |
| with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") 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', password=True) | |
| url = gr.Textbox(label='Enter PDF URL here (Example: https://arxiv.org/pdf/1706.06722.pdf )') | |
| 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') | |
| gr.Examples( | |
| [[q] for q in questions], | |
| inputs=[question], | |
| label="PRE-DEFINED QUESTIONS: Click on a question to auto-fill the input box, then press Enter!", | |
| ) | |
| model = gr.Radio([ | |
| 'gpt-3.5-turbo', | |
| 'gpt-3.5-turbo-16k', | |
| 'gpt-3.5-turbo-0613', | |
| 'gpt-3.5-turbo-16k-0613', | |
| 'text-davinci-003', | |
| 'gpt-4', | |
| 'gpt-4-32k' | |
| ], label='Select Model', default='gpt-3.5-turbo') | |
| btn = gr.Button(value='Submit') | |
| btn.style(full_width=True) | |
| with gr.Group(): | |
| #chatbot = gr.Chatbot(placeholder="Chat History", label="Chat History", lines=20, elem_id="chatbot") | |
| chatbot = gr.Chatbot(placeholder="Chat History", label="Chat History", lines=50, elem_id="chatbot") | |
| clear_btn | |
| # Bind the click event of the button to the question_answer function | |
| btn.click( | |
| question_answer, | |
| inputs=[chatbot, url, file, question, openAI_key, model], | |
| outputs=[chatbot], | |
| ) | |
| demo.launch() | |